基于GRU-DRSN的双通道人体活动识别

人体活动识别(human activity recognizition, HAR)在医疗、军工、智能家居等领域有很大的应用空间。传统机器学习方法特征提取难度较大且精度不高。针对上述问题并结合传感器时序特性,提出了一种融合CBAM(convolutional block attention module)注意力机制的GRU-DRSN双通道并行模型,有效避免了传统串行模型因网络深度加深引起梯度爆炸和消失问题。同时并行结构使得两条支路具有相同的优先级,使用深度残差收缩网络(deep residual shrinkage network, DRSN)提取数据的深层空间特征,同时使用门控循环结构(gated recurrent unit, GRU)学习活动样本在时间序列上的特征,同时进行提取样本不同维度的特征,并通过CBAM模块进行特征的权重分配,最后通过Softmax层进行识别,实现了端对端的人体活动识别。使用公开数据集(wireless sensor data mining, WISDM)进行验证,模型平均精度达到了97.6%,与传统机器学习模型和前人所提神经网络模型相比,有更好的识别效果。

Development and validation of a nomogram model for predicting the risk of gallstone recurrence after gallbladder-preserving surgery

Background:The high incidence of gallstone recurrence was a major concern for laparoscopic gallbladderpreserving surgery.This study aimed to investigate the risk factors for gallstone recurrence after gallbladder-preserving surgery and to establish an individualized nomogram model to predict the risk of gallstone recurrence.Methods:The clinicopathological and follow-up data of 183 patients who were initially diagnosed with gallstones and treated with gallbladder-preserving surgery at our hospital from January 2012 to January 2019 were retrospectively collected.The independent predictive factors for gallstone recurrence following gallbladder-preserving surgery were identified by multivariate logistic regression analysis.A nomogram model for the prediction of gallstone recurrence was constructed based on the selected variables.The C-index,receiver operating characteristic(ROC)curve and calibration curve were used to evaluate the predictive power of the nomogram model for gallstone recurrence.Results:During the follow-up period,a total of 65 patients experienced gallstone recurrence,and the recurrence rate was 35.5%.Multivariate logistic regression analysis revealed that the course of gallstones>2 years[odds ratio(OR)=2.567,95%confidence interval(CI):1.270-5.187,P=0.009],symptomatic gallstones(OR=2.589,95%CI:1.059-6.329,P=0.037),multiple gallstones(OR=2.436,95%CI:1.133-5.237,P=0.023),history of acute cholecystitis(OR=2.778,95%CI:1.178-6.549,P=0.020)and a greasy diet(OR=2.319,95%CI:1.186-4.535,P=0.014)were independent risk factors for gallstone recurrence after gallbladder-preserving surgery.A nomogram model for predicting the recurrence of gallstones was established based on the above five variables.The results showed that the C-index of the nomogram model was 0.692,suggesting it was valuable to predict gallstone recurrence.Moreover,the calibration curve showed good consistency between the predicted probability and actual probability.Conclusions:The nomogram model for the prediction of gallstone recurrence might help clinicians develop a proper treatment strategy for patients with gallstones.Gallbladder-preserving surgery should be cautiously considered for patients with high recurrence risks.

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.

Endoscopic treatment of scarred polyps with a non-thermal device(Endorotor):A review of the literature

Endoscopic resection(ER)of colorectal polyps has become a daily practice in most endoscopic units providing a colorectal cancer screening program and requires the availability of local experts and high-end endoscopic devices.ER procedures have evolved over the past few years from endoscopic mucosal resection(EMR)to more advanced techniques,such as endoscopic submucosal dissection and endo-scopic full-thickness resection.Complete resection and disease eradication are the ultimate goals of ER-based techniques,and novel devices have been developed to achieve these goals.The EndoRotor®Endoscopic Powered Resection System(Interscope Medical,Inc.,Northbridge,Massachusetts,United States)is one such device.The EndoRotor is a powered resection tool for the removal of alimentary tract mucosa,including post-EMR persistent lesions with scarring,and has both CE Mark and FDA clearance.This review covers available published evidence documenting the usefulness of EndoRotor for the management of recurrent colorectal polyps.

Dual-wavelength pumped latticed Fermi-Pasta-Ulam recurrences in nonlinear Schrödinger equation

We show that the nonlinear stage of the dual-wavelength pumped modulation instability(MI)in nonlinear Schrödinger equation(NLSE)can be effectively analyzed by mode truncation methods.The resulting complicated heteroclinic structure of instability unveils all possible dynamic trajectories of nonlinear waves.Significantly,the latticed-Fermi-Pasta-Ulam recurrences on the modulated-wave background in NLSE are also investigated and their dynamic trajectories run along the Hamiltonian contours of the heteroclinic structure.It is demonstrated that there has much richer dynamic behavior,in contrast to the nonlinear waves reported before.This novel nonlinear wave promises to inject new vitality into the study of MI.

Study on Chaotic Characteristics of the Friction Process between High Hardness Alloy Steel and Cemented Carbide under C60 Nanoparticle Fluid Lubrication

Friction and wear phenomenon is a complex nonlinear system,and it is also a significant problem in the process of metal cutting.In order to systematically analyze the friction and wear process of tool material-workpiece material friction pair in the cutting process of high hardness alloy steel under different lubrication conditions,the chaotic characteristics of friction process between high hardness alloy steel and cemented carbide under the lubrication C60 nano-particles fluid are studied based on the chaos theory.Firstly,the friction and wear experiments of the friction pair between high hardness alloy steel and cemented carbide tool are carried out based on the ring-block friction and wear tester,and the results of friction force signal in time domain and wear width are obtained.Then,the friction signals in time domain are processed and transformed based on phase space reconstruction and recurrence plot theory,and the recurrence plots of different experimental groups under different lubrication conditions are generated.The evolution law of recurrence plot is further observed and studied,and the recursive quantitative index is analyzed.Finally,the cutting experiments of tool wear are carried out.The results show that the proposed method can intuitively and accurately reveal the wear evolution process and the wear feature identification law of the tool material-high hardness alloy steel pair under different lubrication conditions.Meanwhile,it is found that when the concentration of C60 nanoparticles is 200∼300 ppm,the stability of the friction pair system is best.The proposed method can provide a strategy for wear prediction in cutting process,and provide a theoretical basis and technical support for antifriction lubrication methods in practical cutting applications.

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.

Low skeletal muscle mass and high visceral adiposity are associated with recurrence of acute cholecystitis after conservative management:A propensity score-matched cohort study

Background:Recurrent acute cholecystitis(RAC)can occur after non-surgical treatment for acute cholecystitis(AC),and can be more severe in comparison to the first episode of AC.Low skeletal muscle mass or adiposity have various effects in several diseases.We aimed to clarify the relationship between RAC and body parameters.Methods:Patients with AC who were treated at our hospital between January 2011 and March 2022 were enrolled.The psoas muscle mass and adipose tissue area at the third lumbar level were measured using computed tomography at the first episode of AC.The areas were divided by height to obtain the psoas muscle mass index(PMI)and subcutaneous/visceral adipose tissue index(SATI/VATI).According to median VATI,SATI and PMI values by sex,patients were divided into the high and low PMI groups.We performed propensity score matching to eliminate the baseline differences between the high PMI and low PMI groups and analyzed the cumulative incidence and predictors of RAC.Results:The entire cohort was divided into the high PMI(n=81)and low PMI(n=80)groups.In the propensity score-matched cohort there were 57 patients in each group.In Kaplan-Meier analysis,the low PMI group and the high VATI group had a significantly higher cumulative incidence of RAC than their counterparts(log-rank P=0.001 and 0.015,respectively).In a multivariate Cox regression analysis,the hazard ratios of low PMI and low VATI for RAC were 5.250(95%confidence interval 1.083-25.450,P=0.039)and 0.158(95%confidence interval:0.026-0.937,P=0.042),respectively.Conclusions:Low skeletal muscle mass and high visceral adiposity were independent risk factors for RAC.

Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma:A step towards precision medicine

The high rate of early recurrence in hepatocellular carcinoma(HCC)post curative surgical intervention poses a substantial clinical hurdle,impacting patient outcomes and complicating postoperative management.The advent of machine learning provides a unique opportunity to harness vast datasets,identifying subtle patterns and factors that elude conventional prognostic methods.Machine learning models,equipped with the ability to analyse intricate relationships within datasets,have shown promise in predicting outcomes in various medical disciplines.In the context of HCC,the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies.This editorial comments on the study carried out exploring the merits and efficacy of random survival forests(RSF)in identifying significant risk factors for recurrence,stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models(CPH).In doing so,the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.

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.

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.

Adjuvant therapy for hepatocellular carcinoma:Dilemmas at the start of a new era

Approximately 50%-70%of patients with hepatocellular carcinoma experience recurrence within five years after curative hepatic resection or ablation.As a result,many patients receive adjuvant therapy after curative resection or ablation in order to prolong recurrence-free survival.The therapy recommended by national guidelines can differ,and guidelines do not specify when to initiate adjuvant therapy or how long to continue it.These and other unanswered questions around adjuvant therapies make it difficult to optimize them and determine which may be more appropriate for a given type of patient.These questions need to be addressed by clinicians and researchers.

Outpatient management of obscure gastrointestinal bleeding:A new perspective in high-risk patients

Mid-gastrointestinal bleeding accounts for approximately 5%-10%of all gastrointestinal bleeding cases,and vascular lesions represent the most frequent cause.The rebleeding rate for these lesions is quite high(about 42%).We hereby recommend that scheduled outpatient management of these patients could reduce the risk of rebleeding episodes.

Hepatolithiasis:Epidemiology,presentation,classification and management of a complex disease

The term hepatolithiasis describes the presence of biliary stones within the intrahepatic bile ducts,above the hilar confluence of the hepatic ducts.The disease is more prevalent in Asia,mainly owing to socioeconomic and dietary factors,as well as the prevalence of biliary parasites.In the last century,owing to migration,its global incidence has increased.The main pathophysiological mechanisms involve cholangitis,bile infection and biliary strictures,creating a self-sustaining cycle that perpetuates the disease,frequently characterised by recurrent episodes of bacterial infection referred to as syndrome of“recurrent pyogenic cholangitis”.Furthermore,long-standing hepatolithiasis is a known risk factor for development of intrahepatic cholangiocarcinoma.Various classifications have aimed at providing useful insight of clinically relevant aspects and guidance for treatment.The management of symptomatic patients and those with complications can be complex,and relies upon a multidisciplinary team of hepatologists,endoscopists,interventional radiologists and hepatobiliary surgeons,with the main goal being to offer relief from the clinical presentations and prevent the development of more serious complications.This comprehensive review provides insight on various aspects of hepatolithiasis,with a focus on epidemiology,new evidence on pathophysiology,most important clinical aspects,different classification systems and contemporary management.

Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

BACKGROUND The prognosis for hepatocellular carcinoma(HCC)in the presence of cirrhosis is unfavourable,primarily attributable to the high incidence of recurrence.AIM To develop a machine learning model for predicting early recurrence(ER)of posthepatectomy HCC in patients with cirrhosis and to stratify patients’overall survival(OS)based on the predicted risk of recurrence.METHODS In this retrospective study,214 HCC patients with cirrhosis who underwent curative hepatectomy were examined.Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods.Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses.Five machine learning methods were used for model comparison,aiming to identify the optimal model.The model’s performance was evaluated using the receiver operating characteristic curve[area under the curve(AUC)],calibration,and decision curve analysis.Additionally,the Kaplan-Meier(K-M)curve was used to evaluate the stratification effect of the model on patient OS.RESULTS Within this study,the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features.In the training cohort,this model attained an AUC of 0.844,while in the validation cohort,it achieved a value of 0.790.The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients’OS.CONCLUSION The combined model,integrating both radiomics and clinical-radiologic characteristics,exhibited excellent performance in HCC with cirrhosis.The K-M curves assessing OS revealed statistically significant differences.

Evaluation of dry eye disease symptomatology and mental health status among patients with different COVID-19 statuses

AIM:To evaluate dry eye disease(DED)symptomatology and mental health status in different COVID-19 patients.METHODS:A cross-sectional observational design was used.Totally 123 eligible adults(46.34%of men,age range,18-59y)with COVID-19 included in the study from August to November,2022.Ocular Surface Disease Index(OSDI),Five-item Dry Eye Questionnaire(DEQ-5),Hospital Anxiety and Depression Scale(HADS),and Pittsburgh Sleep Quality Index(PSQI)were used in this study.RESULTS:OSDI scores were 6.82(1.25,15.91)in asymptomatic carriers,7.35(2.50,18.38)in mild cases,and 16.67(4.43,28.04)in recurrent cases,with 30.00%,35.56%,and 57.89%,respectively evaluated as having DED symptoms(χ2=7.049,P=0.029).DEQ-5 score varied from 2.00(0,6.00)in asymptomatic carriers,3.00(0,8.00)in mild cases,and 8.00(5.00,10.00)in recurrent cases,with 27.50%,33.33%,and 55.26%,respectively assessed as having DED symptoms(χ2=8.532,P=0.014).The prevalence of clinical anxiety(50.00%)and depression(47.37%)symptoms were also significantly higher in patients with recurrent infection(χ2=24.541,P<0.001;χ2=30.871,P<0.001).Recurrent infection was a risk factor for high OSDI scores[odds ratio,2.562;95%confidence interval(CI),1.631-7.979;P=0.033]and DEQ-5 scores(odds ratio,3.353;95%CI,1.038-8.834;P=0.043),whereas having a fixed occupation was a protective factor for OSDI scores(odds ratio,0.088;95%CI,0.022-0.360;P=0.001)and DEQ-5 scores(odds ratio,0.126;95%CI,0.039-0.405;P=0.001).CONCLUSION:Patients with recurrent COVID-19 have more severe symptoms of DED,anxiety,and depression.

Assessing recent recurrence after hepatectomy for hepatitis Brelated hepatocellular carcinoma by a predictive model based on sarcopenia

BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.

From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment.

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.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.