Analysis of the Standardization of Administrative Management in Colleges and Universities Under the Background of Information Technology

In the development of the information age,universities should recognize the significant impact of information technology on their own development.In practice,it is possible to effectively improve the efficiency and quality of work by dealing with and constructing information-based methods,innovating administrative management models,and adopting more efficient and scientific methods to address various problems and shortcomings encountered in work.As an important part of university management,administrative management is a complex task.Establishing a standardized management model through information technology can further improve the comprehensive efficiency of administrative management.In this regard,the administrative standardization in universities under the background of information technology is analyzed and studied in this paper.

Research on Online-Offline Hybrid Teaching Mode of College English in the Context of New Liberal Arts

English is an important course in colleges,and students’proficiency in the language can significantly affect their learning in multiple disciplines.At present,offline classroom teaching is still the primary mode of college English teaching,and online teaching has not been widely used.Under the background of new liberal arts,the teaching tasks and objectives of college English have changed,and it is imperative to carry out online-offline hybrid teaching mode.This paper analyzes the feasibility of the online-offline hybrid teaching mode of college English,summarizes its problems,and explores its implementation strategies in the context of the new liberal arts,to provide references for teachers.

The Quantitative Evaluation of Music Teaching to Introduce Computer Technology in Colleges and Universities

Through the establishment of music teaching effect evaluation model and the multimedia courseware evaluation model, which can effectively avoid the influence of subjective evaluation, the evaluation process will be simple and objective. Through the issuance of assessment questionnaire, each index is divided into four grades of excellent, good, poor, according to percentile scoring, the score of every index multiply corresponding weight, and then addition, and finally we can obtain the evaluation results.

Compositional and Hollow Engineering of Silicon Carbide/Carbon Microspheres as High-Performance Microwave Absorbing Materials with Good Environmental Tolerance

Microwave absorbing materials(MAMs)characterized by high absorption efficiency and good environmental tolerance are highly desirable in practical applications.Both silicon carbide and carbon are considered as stable MAMs under some rigorous conditions,while their composites still fail to produce satisfactory microwave absorption performance regardless of the improvements as compared with the individuals.Herein,we have successfully implemented compositional and structural engineering to fabricate hollow Si C/C microspheres with controllable composition.The simultaneous modulation on dielectric properties and impedance matching can be easily achieved as the change in the composition of these composites.The formation of hollow structure not only favors lightweight feature,but also generates considerable contribution to microwave attenuation capacity.With the synergistic effect of composition and structure,the optimized SiC/C composite exhibits excellent performance,whose the strongest reflection loss intensity and broadest effective absorption reach-60.8 dB and 5.1 GHz,respectively,and its microwave absorption properties are actually superior to those of most SiC/C composites in previous studies.In addition,the stability tests of microwave absorption capacity after exposure to harsh conditions and Radar Cross Section simulation data demonstrate that hollow SiC/C microspheres from compositional and structural optimization have a bright prospect in practical applications.

No basalt accumulation and segregation atop the 660-km discontinuity beneath the Sea of Okhotsk

Recent seismic evidence shows that basalt accumulation is widespread in the mantle transition zone(MTZ),yet its ubiquity or sporadic nature remains uncertain.To investigate this phenomenon further,we characterized the velocity structure across the 660-km discontinuity that separates the upper mantle from the lower mantle beneath the Sea of Okhotsk by modeling the waveform of the S660P phase,a downgoing S wave converting into a P wave at the 660-km interface.These waves were excited by two regional>410-km-deep events and were recorded by stations in central Asia.Our findings showed no need to introduce velocity anomalies at the base of the MTZ to explain the S660P waveforms because the IASP91 model adequately reproduced the waveforms.This finding indicates that the basalt accumulation has not affected the bottom of the MTZ in the study area.Instead,this discontinuity is primarily controlled by temperature or water content variations,or both.Thus,we argue that the basalt accumulation at the base of the MTZ is sporadic,not ubiquitous,reflecting its heterogeneous distribution.

The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Characteristics of long-distance mobile mRNAs from shoot to root in grafted plant species

Thousands of long-distance mobile mRNAs were identified from different grafting systems,based on high-throughput sequencing technology.Moreover,the long-distance delivery of RNAs was proved to involve multiple mechanisms.Here,we analyzed the homology,motif,and tRNA-like structure(TLS)of long-distance mobile mRNAs identified by RNA-seq as well as the RNA-binding protein(RBP)in nine grafting combinations including Arabidopsis thaliana,Vitis vinifera,Cucumis sativus,Citrullus lanatus,Nicotiana benthamiana,Malus domestica,Pyrus spp.,Glycine max and Phaseolus vulgaris.Although several mRNAs were found to be shared in herbaceous,woody,and related species,the vast majority of long-distance mobile mRNAs were species-specific.Four non-specific movement-related motifs were identified,while the TLS was not necessary for mRNA long distance mobility.In addition,we found that RBPs were conserved among herbaceous and woody plants as well as related species.This paper reports a further in-depth analysis of the endogenous mechanisms by which the species-specific transportable m RNAs were selected by bioinformatics,in order to provide insights for future research on long-distance mobile mRNAs.

A Review on the Recent Trends of Image Steganography for VANET Applications

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside look.Whereas vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation systems.VANETs steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy protection.This paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality loss.According to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network conditions.In this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and filtering.This study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

Fine Sand and Clay Sediment Acoustic Properties of the Novel Sediment Sample from the Arabian Sea:Experimental Investigations and Biot−Stoll Model Validation

The present study explores the physical and acoustic characteristics of fine sand and clay in novel seabed marine sediments from of Pakistan coastline of the Arabian Sea.The measured physical parameters included mean grain size,mass density,bulk density,salinity,porosity,permeability,pore size and mineralogical composition.Acoustic properties,including sound speed and attenuation,in the high frequency range of 90-170 kHz were analyzed.A controlled laboratory setup with the acoustic transmission method and Fourier transform techniques was utilized to examine the sound propagation and absorption of novel seabed sediments.The standard deviation of mean sound speed in fresh water was 0.75 m/s,and attenuation was observed in the range of 0.43 to 0.61 dB/m.The mean sound velocity in sand and clay varied from 1706 to 1709 m/s and 1602 to 1608 m/s,respectively.Corresponding average attenuation was observed at 80 to 93 dB/m in sandy sediments and from 31.8 to 38.6 dB/m in clayey sediments.Sound velocity variation within sandy sediment is low,consistent with expected results,and smaller than the predicted uncertainty.However,clay sediment exhibited a positive linear correlation and low sound speed variation.Attenuation increased linearly with frequency for both sediments.Finally,the laboratory results were validated by using the Biot−Stoll model.The dispersion of sound speed in sandy and clayey sediments was consistent with the predictions of the Biot−Stoll model.Measured attenuation aligned more with Biot−Stoll model predictions due to improved permeability,tortuosity and pore size parameter fitting.

A High-Accuracy Curve Boundary Recognition Method Based on the Lattice Boltzmann Method and Immersed Moving Boundary Method

Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Lattice Boltzmann(LBM)and the immersion boundary method based on solid ratio(IMB)have limitations in identifying custom curved boundaries.Meanwhile,IBM based on velocity correction(IBM-VC)suffers from inaccuracies and numerical instability.Therefore,this study introduces a high-accuracy curve boundary recognition method(IMB-CB),which identifies boundary nodes by moving the search box,and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,achieving accurate recognition of custom curve boundaries.In addition,curve boundary image and dot methods are utilized to verify IMB-CB.The findings revealed that IMB-CB can accurately identify the boundary,showing an error of less than 1.8%with 500 lattices.Also,the flow in the custom curve boundary and aerodynamic characteristics of the NACA0012 airfoil are calculated and compared to IBM-VC.Results showed that IMB-CB yields lower lift and drag coefficient errors than IBM-VC,with a 1.45%drag coefficient error.In addition,the characteristic curve of IMB-CB is very stable,whereas that of IBM-VC is not.For the moving boundary problem,LBM-IMB-CB with discrete element method(DEM)is capable of accurately simulating the physical phenomena of multi-moving particle flow in complex curved pipelines.This research proposes a new curve boundary recognition method,which can significantly promote the stability and accuracy of fluid-solid interaction simulations and thus has huge applications in engineering.

Graphene effectively activating "dead" water molecules between manganese dioxide layers in potassium-ion battery

Aqueous potassium-ion batteries(APIBs),recognized as safe and reliable new energy devices,are considered as one of the alternatives to traditional batteries.Layered MnO_(2),serving as the main cathode,exhibits a lower specific capacity in aqueous electrolytes compared to organic systems and operates through a different reaction mechanism.The application of highly conductive graphene may effectively enhance the capacity of APIBs but could complicate the potassium storage environment.In this study,a MnO_(2) cathode pre-intercalated with K~+ions and grown on graphene(KMO@rGO) was developed using the microwave hydrothermal method for APIBs.KMO@rGO achieved a specific capacity of 90 mA h g^(-1) at a current density of 0.1 A g^(-1),maintaining a capacity retention rate of>90% after 5000 cycles at 5 A g^(-1).In-situ and exsitu characterization techniques revealed the energy-storage mechanism of KMO@rGO:layered MnO_(2)traps a large amount of "dead" water molecules during K~+ions removal.However,the introduction of graphene enables these water molecules to escape during K~+ ions insertion at the cathode.The galvanostatic intermittent titration technique and density functional theory confirmed that KMO@rGO has a higher K~+ions migration rate than MnO_(2).Therefore,the capacity of this cathode depends on the interaction between dead water and K~+ions during the energy-storage reaction.The optimal structural alignment between layered MnO_(2) and graphene allows electrons to easily flow into the external circuit.Rapid charge compensation forces numerous low-solvent K~+ions to displace interlayer dead water,enhancing the capacity.This unique reaction mechanism is unprecedented in other aqueous battery studies.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy Decision-Making Approach

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in cybersecurity.KSA has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage.The widespread usage of Information Technology(IT)and IT Enable Services(ITES)reinforces securitymeasures.The constantly evolving cyber threats are a topic that is generating a lot of discussion.In this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region.The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online.Different competency models have been devised to address the necessary cybercrime competencies in this context.The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security.To address this research query,the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(F.AHP),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy.The similarities and differences between the three methods are also discussed.This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority.The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for SaudiArabia.Moreover,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

Semi-quantitative analysis of the structural evolution of mesophase pitch-based carbon foams by Raman and FTIR spectroscopy

Graphitized carbon foams(GFms)were prepared using mesophase pitch(MP)as a raw material by foaming(450℃),pre-oxidation(320℃),carbonization(1000℃)and graphitization(2800℃).The differences in structure and properties of GFms prepared from different MP precursors pretreated by ball milling or liquid phase extraction were investigated and compared,and semi-quantitative calculations were conducted on the Raman and FTIR spectra of samples at each preparation stage.Semi-quantitat-ive spectroscopic analysis provided detailed information on the structure and chemical composition changes of the MP and GFm de-rived from it.Combined with microscopic observations,the change from precursor to GFm was analyzed.The results showed that ball milling concentrated the distribution of aromatic molecules in the pitch,which contributed to uniform foaming to give a GFm with a uniform pore distribution and good properties.Liquid phase extraction helped remove light components while retaining large aromatics to form graphitic planes with the largest average size during post-treatment to produce a GFm with the highest degree of graphitization and the fewest open pores,giving the best compression resistance(2.47 MPa),the highest thermal conductivity(64.47 W/(m·K))and the lowest electrical resistance(13.02μΩ·m).Characterization combining semi-quantitative spectroscopic ana-lysis with microscopic observations allowed us to control the preparation of the MP-derived GFms.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

Enhancing Photocatalytic Hydrogen Evolution through Electronic Structure and Wettability Adjustment of ZnIn2S4/Bi2O3 S-Scheme Heterojunction

The production of renewable fuels through water splitting via photocatalytic hydrogen production holds significant promise.Nonetheless,the sluggish kinetics of hydrogen evolution and the inadequate water adsorption on photocatalysts present notable challenges.In this study,we have devised a straightforward hydrothermal method to synthesize Bi_(2)O_(3)(BO)derived from metal‐organic frameworks(MOFs),loaded with flower-like ZnIn_(2)S_(4)(ZIS).This approach substantially enhances water adsorption and surface catalytic reactions,resulting in a remarkable enhancement of photocatalytic activity.By employing triethanolamine(TEOA)as a sacrificial agent,the hydrogen evolution rate achieved with 15%(mass fraction)ZIS loading on BO reached an impressive value of 1610μmol∙h^(−1)∙g^(−1),marking a 6.34-fold increase compared to that observed for bare BO.Furthermore,through density functional theory(DFT)and ab initio molecular dynamics(AIMD)calculations,we have identified the reactions occurring at the ZIS/BO S-scheme heterojunction interface,including the identification of active sites for water adsorption and catalytic reactions.This study provides valuable insights into the development of high-performance composite photocatalytic materials with tailored electronic properties and wettability.

Solvation Engineering via Fluorosurfactant Additive Toward Boosted Lithium-Ion Thermoelectrochemical Cells

Lithium-ion thermoelectrochemical cell(LTEC), featuring simultaneous energy conversion and storage, has emerged as promising candidate for low-grade heat harvesting. However, relatively poor thermosensitivity and heat-to-current behavior limit the application of LTECs using LiPF_6 electrolyte. Introducing additives into bulk electrolyte is a reasonable strategy to solve such problem by modifying the solvation structure of electrolyte ions. In this work, we develop a dual-salt electrolyte with fluorosurfactant(FS) additive to achieve high thermopower and durability of LTECs during the conversion of low-grade heat into electricity. The addition of FS induces a unique Li~+ solvation with the aggregated double anions through a crowded electrolyte environment,resulting in an enhanced mobility kinetics of Li~+ as well as boosted thermoelectrochemical performances. By coupling optimized electrolyte with graphite electrode, a high thermopower of 13.8 mV K^(-1) and a normalized output power density of 3.99 mW m^(–2) K^(–2) as well as an outstanding output energy density of 607.96 J m^(-2) can be obtained.These results demonstrate that the optimization of electrolyte by regulating solvation structure will inject new vitality into the construction of thermoelectrochemical devices with attractive properties.

High-sensitive state perception method for inverter-fed machine turn insulation based on FrFT-Mel

Amidst the swift advancement of new power systems and electric vehicles,inverter-fed machines have progressively materialized as a pivotal apparatus for efficient energy conversion.Stator winding turn insulation failure is the root cause of inverter-fed machine breakdown.The online monitoring of turn insulation health can detect potential safety risks promptly,but faces the challenge of weak characteristics of turn insulation degradation.This study proposes an innovative method to evaluate the turn insulation state of inverter-fed machines by utilizing the fractional Fourier transform with a Mel filter(FrFT-Mel).First,the sensitivity of the high-frequency(HF)switching oscillation current to variations in turn insulation was analyzed within the fractional domain.Subsequently,an improved Mel filter is introduced,and its structure and parameters are specifically designed based on the features intrinsic to the common-mode impedance resonance point of the electrical machine.Finally,an evaluation index was proposed for the turn insulation state of inverter-fed machines.Experimental results on a 3kW permanent magnet synchronous machine(PMSM)demonstrate that the proposed FrFT-Mel method significantly enhances the sensitivity of turn insulation state perception by approximately five times,compared to the traditional Fourier transform method.

Meta-analysis and sequential analysis of acupuncture compared to carbamazepine in the treatment of trigeminal neuralgia

BACKGROUND In this randomized controlled trial(RCT)comparing current acupuncture with carbamazepine for trigeminal neuralgia,meta-and sequential analyses were utilized.AIM To guide clinical decision making regarding the treatment of trigeminal neuralgia with carbamazepine.METHODS The RCT literature on needle comparison was searched in various Chinese biomedical databases including Chinese Biomedical Literature Database,Wanfang Data,VIP Database,as well as international databases such as Excerpt Medica Database,Cochrane Library,PubMed,and Web of Science,along with related clinical registration platforms such as World Health Organization International Clinical Trial Registry Platform,ChiCTR,and Clinical Trials up to 1 April 2020.Risk of bias was evaluated using the Cochrane Collaborative Risk Bias tool,primary outcome measures(pain reduction)were analyzed using STATA metaanalysis,outcome measures were analyzed using trial sequential analysis 0.9.5.10 Beta sequential analysis,GRADE was used to assess the evidence,and adverse reactions were documented.RESULTS This study analyzed 16 RCTs with a total of 1231 participants.The meta-analysis revealed a statistically significant difference in pain reduction between acupuncture and carbamazepine[standardized mean difference(SMD)=1.47;95%confidence interval(CI):0.99-1.95],although the quality of evidence was deemed to be of extremely low quality.Cumulative meta-analysis based on the year of publication indicated that carbamazepine treatment first demonstrated a statistically significant difference in pain reduction in 2014 and remained relatively stable over time[SMD=1.84;95%CI:0.22-3.47].Additionally,the number of adverse events associated with acupuncture was significantly lower compared to carbamazepine.CONCLUSION Acupuncture for trigeminal neuralgia is better than analgesia and safer than carbamazepine;however,firm conclusions still require a high-quality,multicenter,large-sample RCT to confirm these findings.