Longing

Friend,you are like a butterfly flying into my window.How long will you stay?We have been separated for a long time.I have missed you very much.

LONGINGES浪琴表170周年庆典全球巡回展

源自1832年的浪琴表,一直以款式优雅华丽及工艺精湛的钟表享誉表坛。今年为庆祝诞生170周年,浪琴表特别挑选世界各地著名城市举行全球巡回展览,将浪琴表历代相传的古董钟表及崭新的腕表系列共治一炉。

利用长短期记忆网络LSTM对赤道太平洋海表面温度短期预报

海表面温度作为海洋中一个最重要的变量,对全球气候、海洋生态等有很大的影响,因此十分有必要对海表面温度(SST)进行预报。深度学习具备高效的数据处理能力,但目前利用深度学习对整个赤道太平洋的SST短期预报及预报技巧的研究仍较少。本文基于最优插值海表面温度(OISST)的日平均SST数据,利用长短期记忆(LSTM)网络构建了未来10天赤道太平洋(10°S~10°N,120°E~80°W)SST的逐日预报模型。LSTM预报模型利用1982~2010年的观测数据进行训练,2011~2020年的观测数据作为初值进行预报和检验评估。结果表明:赤道太平洋东部地区预报均方根误差(RMSE)大于中、西部,东部预报第1天RMSE为0.6℃左右,而中、西部均小于0.3℃。在不同的年际变化位相,预报RMSE在拉尼娜出现时期最大,正常年份次之,厄尔尼诺时期最小,RMSE在拉尼娜时期比在厄尔尼诺时期可达20%。预报偏差整体表现为东正、西负。相关预报技巧上,中部最好,可预报天数基本为10天以上,赤道冷舌附近可预报天数为4~7天,赤道西边部分地区可预报天数为3天。预报模型在赤道太平洋东部地区各月份预报技巧普遍低于西部地区,相比较而言各区域10、11月份预报技巧最低。总的来说,基于LSTM构建的SST预报模型能很好地捕捉到SST在时序上的演变特征,在不同案例中预报表现良好。同时该预报模型依靠数据驱动,能迅速且较好地预报未来10天以内的日平均SST的短期变化。

Construction and validation of somatic mutation-derived long noncoding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND Long non-coding RNAs(LncRNAs)have been found to be a potential prognostic factor for cancers,including hepatocellular carcinoma(HCC).Some LncRNAs have been confirmed as potential indicators to quantify genomic instability(GI).Nevertheless,GI-LncRNAs remain largely unexplored.This study established a GI-derived LncRNA signature(GILncSig)that can predict the prognosis of HCC patients.AIM To establish a GILncSig that can predict the prognosis of HCC patients.METHODS Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles.The GI-LncRNAs were then analyzed for functional enrichment.The GILncSig was established in the training set by Cox regression analysis,and its predictive ability was verified in the testing set and TCGA set.In addition,we explored the effects of the GILncSig and TP53 on prognosis.RESULTS A total of 88 GI-LncRNAs were found,and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI.The GILncSig was constructed by 5 LncRNAs(miR210HG,AC016735.1,AC116351.1,AC010643.1,LUCAT1).In the training set,the prognosis of high-risk patients was significantly worse than that of low-risk patients,and similar results were verified in the testing set and TCGA set.Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor.Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve(0.773)was higher than the two LncRNA signatures published recently.Furthermore,the GILncSig may have a better predictive performance than TP53 mutation status alone.CONCLUSION We established a GILncSig that can predict the prognosis of HCC patients,which will help to guide prognostic evaluation and treatment decisions.

Numerical Simulation of Rainfall-induced Xianchi Reservoir Landslide in Yunyang,Chongqing,China

A calamitous landslide happened at 22:00 on September 1,2014 in the Yunyang area of Chongqing City,southwest China,enforcing the evacuation of 508 people and damaging 23 buildings.The landslide volume comprised 1.44 million m^(3) of material in the source area and 0.4 million m^(3) of shoveled material.The debris flow runout extended 400 m vertically and 1600 m horizontally.The Xianchi reservoir landslide event has been investigated as follows:(1)samples collected from the main body of landslide were carried out using GCTS ring shear apparatus;(2)the parameters of shear and pore water pressure have been measured;and(3)the post-failure characteristics of landslide have been analyzed using the numerical simulation method.The excess pore-water pressure and erosion in the motion path are considered to be the key reasons for the long-runout motion and the scale-up of landslides,such as that at Xianchi,were caused by the heavy rainfall.The aim of this paper is to acquired numerical parameters and the basic resistance model,which is beneficial to improve simulation accuracy for hazard assessment for similar to potentially dangerous hillslopes in China and elsewhere.

A 9.5-kb deletion in the 1st intron of OsMADS51 enhances temperature sensitivity in rice

Temperature is an important environmental factor affecting heading date of rice.Despite its importance,genes responsible for temperature-sensitive heading in rice have remained elusive.Our previous study identified a quantitative trait locus qHd1 which advances heading date under high temperatures.A 9.5-kb insertion was found in the first intron of OsMADS51 in indica variety Zhenshan 97(ZS97).However,the function of this natural variant in controlling temperature sensitivity has not been verified.In this study,we used CRISPR/Cas9 to knock out the 9.5-kb insertion in ZS97.Experiments conducted under cotrolled conditions in phytotrons confirmed that deletion increased temperature sensitivity and advanced heading by downregulating the expression level of OsMADS51.One-hybrid assays in yeast,ChIP-quantitative polymerase chain reaction,electrophoretic mobility shift,and luciferase-based transient transactivation assays collectively confirmed that OsMADS51 affects heading date by regulation of heading date gene Ehd1.We further determined that the long non-coding RNA HEATINR is generated from the first intron of OsMADS51,offering an explanation for how the 9.5-kb insertion affects temperature sensitivity.We also found that OsMADS51 was strongly selected in early/late-season rice varieties in South China,possibly accounting for their strong temperature sensitivity.These insights not only advance our understanding of the molecular mechanisms underlying the temperature-responsive regulation of heading date in rice but also provide a valuable genetic target for molecular breeding.

A spatiotemporal deep learning method for excavation-induced wall deflections

Data-driven approaches such as neural networks are increasingly used for deep excavations due to the growing amount of available monitoring data in practical projects.However,most neural network models only use the data from a single monitoring point and neglect the spatial relationships between multiple monitoring points.Besides,most models lack flexibility in providing predictions for multiple days after monitoring activity.This study proposes a sequence-to-sequence(seq2seq)two-dimensional(2D)convolutional long short-term memory neural network(S2SCL2D)for predicting the spatiotemporal wall deflections induced by deep excavations.The model utilizes the data from all monitoring points on the entire wall and extracts spatiotemporal features from data by combining the 2D convolutional layers and long short-term memory(LSTM)layers.The S2SCL2D model achieves a long-term prediction of wall deflections through a recursive seq2seq structure.The excavation depth,which has a significant impact on wall deflections,is also considered using a feature fusion method.An excavation project in Hangzhou,China,is used to illustrate the proposed model.The results demonstrate that the S2SCL2D model has superior prediction accuracy and robustness than that of the LSTM and S2SCL1D(one-dimensional)models.The prediction model demonstrates a strong generalizability when applied to an adjacent excavation.Based on the long-term prediction results,practitioners can plan and allocate resources in advance to address the potential engineering issues.

Numerical analysis of downward progressive landslides in long natural slopes with sensitive clay

Landslides occurring in sensitive clay often result in widespread destruction,posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation.Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms.While studies have extensively explored upward progressive landslides in sensitive clays,understanding downward progressive cases remains limited.In this study,we utilised the nodal integration-based particle finite element method(NPFEM)with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes,induced by surcharge loads near the crest.We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strainsoftening characteristics.The simulation results revealed the typical pattern for downward landslides,which typically start with a localised failure in proximity to the surcharge loads,followed by a combination of different types of failure mechanisms,including single flow slides,translational progressive landslides,progressive flow slides,and spread failures.Additionally,inclined shear bands occur within spread failures,often adopting distinctive ploughing patterns characterised by triangular shapes.The sensitive clay thickness at the base,the clay strength gradient,the sensitivity,and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement.Remarkably,some of these effects mirror those observed in upward progressive landslides,underscoring the interconnectedness of these phenomena.This study contributes valuable insights into the complex dynamics of sensitive clay landslides,shedding light on the intricate interplay of factors governing their behaviour and progression.

Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

Failure mechanism and simulation for long run-out of the catastrophic rock landslide in the Shanyang Vanadium Mine,China

On 12th August 2015,a massive rapid long run-out rock landslide occurred in the Shanyang Vanadium Mine in Shaanxi Province,China,which claimed the lives of 65 miners.No heavy rainfalls,earthquakes,and mining blasts were recorded before the incident.Therefore,the failure mechanism and the cause of the long run-out movement are always in arguments.In this paper,we conducted a detailed field investigation,laboratory tests,block theory analysis,and numerical simulation to investigate the failure and long run-out mechanisms of the landslide.The field investigation results show that the source material of the rock landslide is a huge dolomite wedge block bedding on siliceous shale layers.Uniaxial compression tests indicate that the uniaxial compression strength of the intact dolomite is 130-140MPa and the dolomite shows a brittle failure mode.Due to the progressive downward erosion of the gully,the dolomite rock bridge at the slope toe became thinner.As the compression stress in the dolomite bridge increased to surpass its strength,the brittle failure of the bridge occurred.Then huge potential energy was released following the disintegration of the landslide,which led to the high acceleration of this rock landslide.The 3D discrete element simulation results suggest that the low intergranular friction contributes to the long run-out movement of this rock landslide.

Investigation of granite failure precursor under axial load using modified LSTM framework

Granite is usually composed of quartz,biotite,feldspar,and cracks,and the variation characteristics of these components could reflect the deformation and failure process of rock well.Taking granite as an example,the video camera was used to record the deformation and failure process of rock.The distribution of meso-components in video images was then identified.The meso-components of rock failure precursors were also discussed.Moreover,a modified LSTM(long short-term memory method)based on SSA(sparrow search algorithm)was proposed to estimate the change of meso-components of rock failure precursor.It shows that the initiation and expansion of cracks are mainly caused by feldspar and quartz fracture,and when the quartz and feldspar exit the stress framework,rock failure occurs;the second large increase of crack area and the second large decrease of quartz or feldspar area may be used as a precursor of rock failure;the precursor time of rock failure based on meso-scopic components is about 4 s earlier than that observed by the naked eye;the modified LSTM network has the strongest estimation ability for quartz area change,followed by feldspar and biotite,and has the worst estimation ability for cracks;when using the modified LSTM network to predict the precursors of rock instability and failure,quartz and feldspar could be given priority.The results presented herein may provide reference in the investigation of rock failure mechanism.

High-modulus solid electrolyte interphase layer with gradient composition enables long-cycle all-solid-state lithium-sulfur batteries

All-solid-state lithium-sulfur batteries(ASSLSBs) have become one of the most potential candidates for the next-generation high-energy systems due to their intrinsic safety and high theoretical energy density.However, PEO-based ASSLSBs face the dilemma of insufficient Coulombic efficiency and long-term stability caused by the coupling problems of dendrite growth of anode and polysulfide shuttle of cathode. In this work, 1,3,5-trioxane(TOX) is used as a functional additive to design a PEO-based composite solidstate electrolyte(denoted as TOX-CSE), which realizes the stable long-term cycle of an ASSLSB. The results show that TOX can in-situ decompose on the anode to form a composite solid electrolyte interphase(SEI) layer with rich-organic component. It yields a high average modulus of 5.0 GPa, greatly improving the mechanical stability of the SEI layer and thus inhibiting the growth of dendrites. Also,the robust SEI layer can act as a barrier to block the side reaction between polysulfides and lithium metal.As a result, a Li-Li symmetric cell assembled with a TOX-CSE exhibits prolonged cycling stability over 2000 h at 0.2 m A cm^(-2). The ASSLSB also shows a stable cycling performance of 500 cycles at 0.5 C.This work reveals the structure–activity relationship between the mechanical property of interface layer and the battery's cycling stability.

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

Non-flammable long chain phosphate ester based electrolyte via competitive solventized structures for high-performance lithium metal batteries

Safety remains a persistent challenge for high-energy-density lithium metal batteries(LMBs).The development of safe and non-flammable electrolytes is especially important in harsh conditions such as high temperatures.Herein,a flame-retardant,low-cost and thermally stable long chain phosphate ester based(tributyl phosphate,TBP)electrolyte is reported,which can effectively enhance the cycling stability of highly loaded high-nickel LMBs with high safety through co-solvation strategy.The interfacial compatibility between TBP and electrode is effectively improved using a short-chain ether(glycol dimethyl ether,DME),and a specially competitive solvation structure is further constructed using lithium borate difluorooxalate(LiDFOB)to form the stable and inorganic-rich electrode interphases.Benefiting from the presence of the cathode electrolyte interphase(CEI)and solid electrolyte interphase(SEI)enriched with LiF and Li_(x)PO_(y)F_(z),the electrolyte demonstrates excellent cycling stability assembled using a 50μm lithium foil anode in combination with a high loading NMC811(15.4 mg cm^(-2))cathode,with 88%capacity retention after 120 cycles.Furthermore,the electrolyte exhibits excellent high-temperature characteristics when used in a 1-Ah pouch cell(N/P=0.26),and higher thermal runaway temperature(238℃)in the ARC(accelerating rate calorimeter)demonstrating high safety.This novel electrolyte adopts long-chain phosphate as the main solvent for the first time,and would provide a new idea for the development of extremely high safety and high-temperature electrolytes.

Lipid metabolism-related long noncoding RNAs:A potential prognostic biomarker for hepatocellular carcinoma

The incidence rates of hepatocellular carcinoma(HCC)have increased in recent decades.Despite advancements in therapy and early diagnosis improving shortterm prognosis,long-term outcomes remain poor.Long noncoding RNAs(lncRNAs)and lipid metabolism play crucial roles in the development and progression of HCC.Enhanced lipid synthesis promotes HCC progression,and lncRNAs can reprogram the expression of lipogenic enzymes.Consequently,lipid metabolism-related(LMR)-lncRNAs regulate lipid anabolism,accelerating the onset and progression of HCC.This suggests that LMR-lncRNAs could serve as novel prognostic biomarkers and therapeutic targets.

Gene Expression Profile Identifies LncRNA AL355974.3 As a Potential Glioma Biomarker

Objective Glioma is a central nervous system tumor arising from glial cells.Despite significant advances in diagnosis and treatment,most patients with high-grade gliomas have a poor prognosis.Many studies have shown that long noncoding RNAs(lncRNAs)may play important roles in the development,progression and treatment of many tumors,including gliomas.Molecularly targeted therapy may be a new direction for the adjuvant treatment of glioma.Therefore,we hope that by studying differentially expressed lncRNAs(DElncRNAs)in glioma,we can discover lncRNAs that can serve as biomarkers for glioma and provide better therapeutic modalities for glioma patients.Methods First,the expression of lncRNAs in 5 normal brain(NB)tissues and 10 glioma tissues was examined by RNA sequencing(RNA-seq).Next,we performed Kaplan-Meier analysis of data from The Cancer Genome Atlas(TCGA)database to assess the prognostic value of these variables.Finally,functional analysis of the DElncRNAs was performed by means of Gene Ontology(GO)enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis.Results RNA sequencing analysis revealed 85 upregulated miRNAs and 71 downregulated lncRNAs in low-grade glioma(LGG)and 50 upregulated lncRNAs and 70 downregulated lncRNAs in glioblastoma(GBM).Among them,AL355974.3 was the most upregulated lncRNA.LINC00632 was the most downregulated lncRNA.Second,LGG patients with higher AL355974.3 expression had worse overall survival according to Kaplan-Meier analysis of the TCGA database.Finally,bioinformatics analysis revealed that the target genes of these DElncRNAs were enriched in various biological processes and signaling pathways,such as cell metabolic and developmental processes.Conclusion Our findings provide evidence that AL355974.3 may be a new biomarker for glioma.

Estimation of free core nutation parameters and availability of computing options

The Earth’s Free Core Nutation(FCN) causes Earth tides and forced nutation with frequencies close to the FCN that exhibit resonance effects.High-precision superconducting gravimeter(SG) and very long baseline interferometry(VLBI) provide good observation techniques for detecting the FCN parameters.However,some choices in data processing and solution procedures increase the uncertainty of the FCN parameters.In this study,we analyzed the differences and the effectiveness of weight function and ocean tide corrections in the FCN parameter detection using synthetic data,SG data from thirty-one stations,and the 10 celestial pole offset(CPO) series.The results show that significant discrepancies are caused by different computing options for a single SG station.The stacking method,which results in a variation of0.24-5 sidereal days(SDs) in the FCN period(T) and 10^(3)-10^(4) in the quality factor(Q) due to the selection of the weighting function and the ocean tide model(OTM),can effectively suppress this influence.The statistical analysis results of synthetic data shows that although different weight choices,while adjusting the proportion of diurnal tidal waves involved,do not significantly improve the accuracy of fitted FCN parameters from gravity observations.The study evaluated a series of OTMs using the loading correction efficiency.The fitting of FCN parameters can be improved by selecting the mean of appropriate OTMs based on the evaluation results.Through the estimation of the FCN parameters based on the forced nutation,it was found that the weight function P_(1) is more suitable than others,and different CPO series(after 2009) resulted in a difference of 0.4 SDs in the T and of 103 in the Q.We estimated the FCN parameters for SG(T=430.4±1.5 SDs and Q=1.52×10^(4)±2.5×10^(3)) and for VLBI(T=429.8±0.7 SDs,Q=1.88×10^(4)±2.1×10^(3)).

Enhanced thermal emission from metal-free,fully epitaxial structures with epsilon-near-zero InAs layers

We introduce a novel method to create mid-infrared(MIR)thermal emitters using fully epitaxial,metal-free structures.Through the strategic use of epsilon-near-zero(ENZ)thin films in InAs layers,we achieve a narrow-band,wide-angle,and p-polarized thermal emission spectra.This approach,employing molecular beam epitaxy,circumvents the complexities associated with current layered structures and yields temperature-resistant emission wavelengths.Our findings contribute a promising route towards simpler,more efficient MIR optoelectronic devices.

A three-dimensional co-continuous network structure polymer electrolyte with efficient ion transport channels enabling ultralong-life all solid-state lithium metal batteries

Solid polymer electrolytes(SPEs)have emerged as one of the most promising candidates for the construction of solid-state lithium batteries due to their excellent flexibility,scalability,and interface compatibility with electrodes.Herein,a novel all-solid polymer electrolyte(PPLCE)was fabricated by the copolymer network of liquid crystalline monomers and poly(ethylene glycol)dimethacrylate(PEGDMA)acts as a structural frame,combined with poly(ethylene glycol)diglycidyl ether short chain interspersed serving as mobile ion transport entities.The preparaed PPLCEs exhibit excellent mechanical property and out-standing electrochemical performances,which is attributed to their unique three-dimensional cocontinuous structure,characterized by a cross-linked semi-interpenetrating network and an ionic liquid phase,resulting in a distinctive nanostructure with short-range order and long-range disorder.Remarkably,the addition of PEGDMA is proved to be critical to the comprehensive performance of the PPLCEs,which effectively modulates the microscopic morphology of polymer networks and improves the mechanical properties as well as cycling stability of the solid electrolyte.When used in a lithiumion symmetrical battery configuration,the 6 wt%-PPLCE exhibites super stability,sustaining operation for over 2000 h at 30 C,with minimal and consistent overpotential of 50 mV.The resulting Li|PPLCE|LFP solid-state battery demonstrates high discharge specific capacities of 160.9 and 120.1 mA h g^(-1)at current densities of 0.2 and 1 C,respectively.Even after more than 300 cycles at a current density of 0.2 C,it retaines an impressive 73.5%capacity.Moreover,it displayes stable cycling for over 180 cycles at a high current density of 0.5C.The super cycle stability may promote the application for ultralong-life all solid-state lithium metal batteries.

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.