Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement e...Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.展开更多
The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China hav...The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.展开更多
Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxi...Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.展开更多
This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends t...This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].展开更多
Affected by the Super Typhoon“Mangkhut,”a total of five base towers of a transmission line in the mountainous area of China collapsed.In this paper,a mathematical model is established based on the Shuttle Radar Topo...Affected by the Super Typhoon“Mangkhut,”a total of five base towers of a transmission line in the mountainous area of China collapsed.In this paper,a mathematical model is established based on the Shuttle Radar Topography Mission(SRTM)data near the accident tower.The measured wind speed in the plain area under the mountain is used as the calculation boundary condition.The wind speed at the top of the mountain is calculated by using a numerical simulation method.The design wind speed and calculated wind speed at the tower site are compared,and the influence of wind speed on tower position in this wind disaster accident is analyzed.展开更多
[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish emb...[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.展开更多
The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).Ne...The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.展开更多
Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The re...Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emp...Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.展开更多
As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal...As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.展开更多
App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While t...App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.展开更多
Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and ...Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.展开更多
BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,an...BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,and short stature.Approximately 60%-70%of patients with CCD have mutations in the RUNX family transcription factor 2 gene.However,prenatal diagnosis of CCD is difficult when the family history is unknown.CASE SUMMARY We report a rare case of fetal CCD with an unknown family history,confirmed by prenatal ultrasonography and genetic testing at a gestational age of 16 weeks.The genetic reports indicated that the fetus carried pathogenic mutations in the RUNX family transcription factor 2 gene(c.674G>A).After careful consideration,the pregnant woman and her family decided to continue the pregnancy.CONCLUSION Definitive prenatal diagnosis of CCD should include family history,ultrasound diagnosis,and genetic analysis,especially if family history is unknown.展开更多
BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features ...BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.展开更多
BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of...BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of global research on BD drug treatment over the last decade.METHODS The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment.A total of 2624 articles were extracted.Data visualization and analysis were conducted using CiteSpace,VOSviewer,Pajek,Scimago Graphica,and R-studio bibliometrix to identify RESULTS The United States,China,and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks.The University of Pittsburgh,Massachusetts General Hospital,and the University of Michigan have been identified as the major research institutions in this field.The Journal of Affective Disorders is the most influential journal.A keyword analysis revealed research hotspots related to clinical symptoms,drug efficacy,and genetic mechanisms.A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.CONCLUSION This study provides a detailed overview of the field of BD drug treatment,highlighting key contributors,research hotspots,and future directions.The study findings can be employed as a reference for future research and policymaking,which may enable further development and optimization of BD pharmacotherapy.展开更多
DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expres...DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alte...BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alterations,mechanisms,and signaling pathways underlying gallbladder NEC remain unclear.CASE SUMMARY This case study presents a rare instance of primary gallbladder NEC in a 73-year-old female patient,who underwent a radical cholecystectomy with hepatic hilar lymphadenectomy and resection of liver segments IV-B and V.Targeted gene sequencing and bioinformatics analysis tools,including STRING,GeneMANIA,Metascape,TRRUST,Sangerbox,cBioPortal and GSCA,were used to analyze the biological functions and features of mutated genes in gallbladder NEC.Twelve mutations(APC,ARID2,IFNA6,KEAP1,RB1,SMAD4,TP53,BTK,GATA1,GNAS,and PRDM3)were identified,and the tumor mutation burden was determined to be 9.52 muts/Mb via targeted gene sequencing.A protein-protein interaction network showed significant interactions among the twelve mutated genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to assess mutation functions and pathways.The results revealed 40 tumor-related pathways.A key regulatory factor for gallbladder NEC-related genes was identified,and its biological functions and features were compared with those of gallbladder carcinoma.CONCLUSION Gallbladder NEC requires standardized treatment.Comparisons with other gallbladder carcinomas revealed clinical phenotypes,molecular alterations,functional characteristics,and enriched pathways.展开更多
The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental result...The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.展开更多
基金the National Natural Science Foundation of China(52304236)the Natural Science Foundation of Shandong Province(ZR2021QE076)for the financial support to this research extracted from the project.
文摘Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.
文摘The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.
基金supported by the National Natural Science Foundation of China(Nos.52121003,51827901 and 52204110)China Postdoctoral Science Foundation(No.2022M722346)+1 种基金the 111 Project(No.B14006)the Yueqi Outstanding Scholar Program of CUMTB(No.2017A03).
文摘Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.
文摘This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].
基金CRSRI Open Research Program(Project No.CKWV2014202/KY).
文摘Affected by the Super Typhoon“Mangkhut,”a total of five base towers of a transmission line in the mountainous area of China collapsed.In this paper,a mathematical model is established based on the Shuttle Radar Topography Mission(SRTM)data near the accident tower.The measured wind speed in the plain area under the mountain is used as the calculation boundary condition.The wind speed at the top of the mountain is calculated by using a numerical simulation method.The design wind speed and calculated wind speed at the tower site are compared,and the influence of wind speed on tower position in this wind disaster accident is analyzed.
基金Supported by National Natural Science Foundation of Shandong Province (No. SY2008C179)~~
文摘[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.
基金supported by the National Natural Science Foundation of China(No.21107066)National Instrumentation Program(No.2011YQ170067)Young Teachers Program of Universities in Shanghai(2012).
文摘The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.
文摘Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
文摘Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.
基金financially supported by the National Key R&D Program of China(No.2022YFE0121300)the National Natural Science Foundation of China(No.52374376)the Introduction Plan for High-end Foreign Experts(No.G2023105001L)。
文摘As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.
基金supported by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:13-612-2024).
文摘App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.
文摘Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.
基金Supported by Science and Technology Development Plan Project of Weifang,No.2023YX005。
文摘BACKGROUND Cleidocranial dysplasia(CCD)is an infrequent clinical condition with an autosomal dominant inheritance pattern.It is characterized by abnormal clavicles,patent sutures and fontanelles,supernumerary teeth,and short stature.Approximately 60%-70%of patients with CCD have mutations in the RUNX family transcription factor 2 gene.However,prenatal diagnosis of CCD is difficult when the family history is unknown.CASE SUMMARY We report a rare case of fetal CCD with an unknown family history,confirmed by prenatal ultrasonography and genetic testing at a gestational age of 16 weeks.The genetic reports indicated that the fetus carried pathogenic mutations in the RUNX family transcription factor 2 gene(c.674G>A).After careful consideration,the pregnant woman and her family decided to continue the pregnancy.CONCLUSION Definitive prenatal diagnosis of CCD should include family history,ultrasound diagnosis,and genetic analysis,especially if family history is unknown.
文摘BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.
基金Supported by the National College Students’Innovative Entrepreneurial Training Plan Program,No.202410403067the Innovation and Entrepreneurship Training Program for College Students in Jiangxi Province,No.S202410403035.
文摘BACKGROUND Bipolar disorder(BD)is a severe mental illness characterized by significant mood swings.Effective drug treatment modalities are crucial for managing BD.AIM To analyze the current status and future trends of global research on BD drug treatment over the last decade.METHODS The Web of Science Core Collection database spanning from 2015 to 2024 was utilized to retrieve literature related to BD drug treatment.A total of 2624 articles were extracted.Data visualization and analysis were conducted using CiteSpace,VOSviewer,Pajek,Scimago Graphica,and R-studio bibliometrix to identify RESULTS The United States,China,and the United Kingdom have made the most significant contributions to research on BD drug treatment and formed notable research collaboration networks.The University of Pittsburgh,Massachusetts General Hospital,and the University of Michigan have been identified as the major research institutions in this field.The Journal of Affective Disorders is the most influential journal.A keyword analysis revealed research hotspots related to clinical symptoms,drug efficacy,and genetic mechanisms.A citation analysis identified the management guidelines published by Yatham et al in 2018 as the most cited paper.CONCLUSION This study provides a detailed overview of the field of BD drug treatment,highlighting key contributors,research hotspots,and future directions.The study findings can be employed as a reference for future research and policymaking,which may enable further development and optimization of BD pharmacotherapy.
文摘DNA microarray technology is an extremely effective technique for studying gene expression patterns in cells, and the main challenge currently faced by this technology is how to analyze the large amount of gene expression data generated. To address this, this paper employs a mixed-effects model to analyze gene expression data. In terms of data selection, 1176 genes from the white mouse gene expression dataset under two experimental conditions were chosen, setting up two conditions: pneumococcal infection and no infection, and constructing a mixed-effects model. After preprocessing the gene chip information, the data were imported into the model, preliminary results were calculated, and permutation tests were performed to biologically validate the preliminary results using GSEA. The final dataset consists of 20 groups of gene expression data from pneumococcal infection, which categorizes functionally related genes based on the similarity of their expression profiles, facilitating the study of genes with unknown functions.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金Supported by School-Level Key Projects at Bengbu Medical College,No.2021byzd109.
文摘BACKGROUND Gallbladder neuroendocrine carcinoma(NEC)represents a subtype of gallbladder malignancies characterized by a low incidence,aggressive nature,and poor prognosis.Despite its clinical severity,the genetic alterations,mechanisms,and signaling pathways underlying gallbladder NEC remain unclear.CASE SUMMARY This case study presents a rare instance of primary gallbladder NEC in a 73-year-old female patient,who underwent a radical cholecystectomy with hepatic hilar lymphadenectomy and resection of liver segments IV-B and V.Targeted gene sequencing and bioinformatics analysis tools,including STRING,GeneMANIA,Metascape,TRRUST,Sangerbox,cBioPortal and GSCA,were used to analyze the biological functions and features of mutated genes in gallbladder NEC.Twelve mutations(APC,ARID2,IFNA6,KEAP1,RB1,SMAD4,TP53,BTK,GATA1,GNAS,and PRDM3)were identified,and the tumor mutation burden was determined to be 9.52 muts/Mb via targeted gene sequencing.A protein-protein interaction network showed significant interactions among the twelve mutated genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were used to assess mutation functions and pathways.The results revealed 40 tumor-related pathways.A key regulatory factor for gallbladder NEC-related genes was identified,and its biological functions and features were compared with those of gallbladder carcinoma.CONCLUSION Gallbladder NEC requires standardized treatment.Comparisons with other gallbladder carcinomas revealed clinical phenotypes,molecular alterations,functional characteristics,and enriched pathways.
基金Partially funded by the National Natural Science Foundation of China(No.51065012)。
文摘The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.
