Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals i...Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.展开更多
Along with the massive applications of the non-linear loads and the impact loads, many non-stationary stochastic signals such as harmonics, inter-harmonics, impulse signals and so on are introduced into the electric n...Along with the massive applications of the non-linear loads and the impact loads, many non-stationary stochastic signals such as harmonics, inter-harmonics, impulse signals and so on are introduced into the electric network, and these non-stationary stochastic signals have had effects on the accuracy of the measurement of electric energy. The traditional method like Fourier Analysis can he applied efficiently on the stationary stochastic signals, hut it has little effect on non-stationary stochastic signals. In light of this, the form of the signals of the electric network in wavelet domain will he discussed in this paper. A measurement method of active power based on multi-resolution analysis in the stochastic process is presented. This method has a wider application scope compared with the traditional method Fourier analysis, and it is of good referential value and practical value in terms of raising the level of the existing electric energy measurement.展开更多
This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the ...This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.展开更多
In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains th...In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains the information required for the characterisation of the sensed phenomena.In the area of physiological signal processing for prosthesis control,scenarios where a signal decomposition analysis are required:the wavelet decomposition(WD)has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals.From a research perspective,the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification.Despite this,there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity.Here,for neuro-muscular(electromyography)and brainwave(electroencephalography)signals acquired from a transhumeral amputee,a computationally efficient time domain signal decom-position method based on a series of heuristics was applied to process the acquired signals before feature extraction.The results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.展开更多
A fidelity-ensured multi-resolution analysis deconvolution algorithm significantly enhances fluorescence microscopy's resolution and noise control,enabling more accurate and detailed imaging for advanced biologica...A fidelity-ensured multi-resolution analysis deconvolution algorithm significantly enhances fluorescence microscopy's resolution and noise control,enabling more accurate and detailed imaging for advanced biological research applications.展开更多
The density field around a vortex generator （VG） in supersonic flow is studied with a nanoparticle-based planar laser scattering （NPLS） method. Based on the calibration, i.e., the density distribution of the super...The density field around a vortex generator （VG） in supersonic flow is studied with a nanoparticle-based planar laser scattering （NPLS） method. Based on the calibration, i.e., the density distribution of the supersonic flow around a wedge, the density field of a supersonic VG is measured. According to movement characteristics of coherent structure in VG’s flow fields and the basic concepts of wavelet, the density fluctuating signals and multi-resolution characteristics of density field images are studied. The multi-resolution characteristics of density fluctuation can be analyzed with wavelet transformation of NPLS images. The wavelet approximate coefficients of density fluctuating signals exhibit their characteristics at different scales, and the corresponding detail coefficients show the difference of diverse layer smooth approximation in some way. Based on 2D wavelet decomposition and reconstruction of density field images, the approximate and detail signals at different scales are studied, and the coherent structures at different scales are extracted and analyzed.展开更多
A multi-resolution rectangular shell element with membrane-bending based on the Kirchhoff-Love theory is proposed. The multi-resolution analysis （MRA） framework is formulated out of a mutually nesting displacement s...A multi-resolution rectangular shell element with membrane-bending based on the Kirchhoff-Love theory is proposed. The multi-resolution analysis （MRA） framework is formulated out of a mutually nesting displacement subspace sequence, whose basis functions are constructed of scaling and shifting on the element domain of basic node shape functions. The basic node shape functions are constructed from shifting to other three quadrants around a specific node of a basic element in one quadrant and joining the corresponding node shape functions of four elements at the specific node. The MRA endows the proposed element with the resolution level （RL） to adjust the element node number, thus modulating structural analysis accuracy accordingly. The node shape functions of Kronecker delta property make the treatment of element boundary condition quite convenient and enable the stiffness matrix and the loading column vectors of the proposed element to be automatically acquired through quadraturing around nodes in RL adjusting. As a result, the traditional 4-node rectangular shell element is a mono-resolution one and also a special case of the proposed element. The accuracy of a structural analysis is actually determined by the RL, not by the mesh. The simplicity and clarity of node shape function formulation with the Kronecker delta property, and the rational MRA enable the proposed element method to be implemented more rationally, easily and efficiently than the conventional mono-resolution rectangular shell element method or other corresponding MRA methods.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
This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attrib...This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attributes, antecedents, and consequences of the concept were extracted from the target literature. The analysis revealed two attributes (“connecting people to people” and “connecting people to objects”);two antecedents (“recognition of artifacts” and “selection of artifacts”);and two consequences (“designing a fulfilling learning environment” and “improving the quality of education”). The concept was defined as “promoting the utilization of artifacts by recognizing and selecting them, connecting people to people and people to objects, designing a fulfilling learning environment, and improving the quality of education”.展开更多
基金This project was supported by the National Natural Science Foundation of China (60672034)the Research Fund for the Doctoral Program of Higher Education(20060217021)the Natural Science Foundation of Heilongjiang Province of China (ZJG0606-01)
文摘Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.
文摘Along with the massive applications of the non-linear loads and the impact loads, many non-stationary stochastic signals such as harmonics, inter-harmonics, impulse signals and so on are introduced into the electric network, and these non-stationary stochastic signals have had effects on the accuracy of the measurement of electric energy. The traditional method like Fourier Analysis can he applied efficiently on the stationary stochastic signals, hut it has little effect on non-stationary stochastic signals. In light of this, the form of the signals of the electric network in wavelet domain will he discussed in this paper. A measurement method of active power based on multi-resolution analysis in the stochastic process is presented. This method has a wider application scope compared with the traditional method Fourier analysis, and it is of good referential value and practical value in terms of raising the level of the existing electric energy measurement.
文摘This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.
基金National Natural Science Foundation of China,Grant/Award Numbers:#U1613222,#81850410557,#8201101443The Shenzhen Science and Technology Program,Grant/Award Number:#SGLH20180625142402055CAS President's International Fellowship Initiative Grant,Grant/Award Number:#2019PB0036。
文摘In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains the information required for the characterisation of the sensed phenomena.In the area of physiological signal processing for prosthesis control,scenarios where a signal decomposition analysis are required:the wavelet decomposition(WD)has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals.From a research perspective,the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification.Despite this,there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity.Here,for neuro-muscular(electromyography)and brainwave(electroencephalography)signals acquired from a transhumeral amputee,a computationally efficient time domain signal decom-position method based on a series of heuristics was applied to process the acquired signals before feature extraction.The results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.
基金supported by the Beijing Natural Science Foundation(1232027)the National Natural Science Foundation of China(62275010,U23A20481)the China Postdoctoral Science Foundation(2022M720347,2024T171115).
文摘A fidelity-ensured multi-resolution analysis deconvolution algorithm significantly enhances fluorescence microscopy's resolution and noise control,enabling more accurate and detailed imaging for advanced biological research applications.
基金National Natural Science Foundation of China (11072264)
文摘The density field around a vortex generator （VG） in supersonic flow is studied with a nanoparticle-based planar laser scattering （NPLS） method. Based on the calibration, i.e., the density distribution of the supersonic flow around a wedge, the density field of a supersonic VG is measured. According to movement characteristics of coherent structure in VG’s flow fields and the basic concepts of wavelet, the density fluctuating signals and multi-resolution characteristics of density field images are studied. The multi-resolution characteristics of density fluctuation can be analyzed with wavelet transformation of NPLS images. The wavelet approximate coefficients of density fluctuating signals exhibit their characteristics at different scales, and the corresponding detail coefficients show the difference of diverse layer smooth approximation in some way. Based on 2D wavelet decomposition and reconstruction of density field images, the approximate and detail signals at different scales are studied, and the coherent structures at different scales are extracted and analyzed.
基金financial support by the Open Foundation of Chongqing Key Laboratory of Geomechanics and Geoenvironment Protection(Logistical Engineering University)(No.GKLGGP 2013-02)
文摘A multi-resolution rectangular shell element with membrane-bending based on the Kirchhoff-Love theory is proposed. The multi-resolution analysis （MRA） framework is formulated out of a mutually nesting displacement subspace sequence, whose basis functions are constructed of scaling and shifting on the element domain of basic node shape functions. The basic node shape functions are constructed from shifting to other three quadrants around a specific node of a basic element in one quadrant and joining the corresponding node shape functions of four elements at the specific node. The MRA endows the proposed element with the resolution level （RL） to adjust the element node number, thus modulating structural analysis accuracy accordingly. The node shape functions of Kronecker delta property make the treatment of element boundary condition quite convenient and enable the stiffness matrix and the loading column vectors of the proposed element to be automatically acquired through quadraturing around nodes in RL adjusting. As a result, the traditional 4-node rectangular shell element is a mono-resolution one and also a special case of the proposed element. The accuracy of a structural analysis is actually determined by the RL, not by the mesh. The simplicity and clarity of node shape function formulation with the Kronecker delta property, and the rational MRA enable the proposed element method to be implemented more rationally, easily and efficiently than the conventional mono-resolution rectangular shell element method or other corresponding MRA methods.
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.
文摘This study aims to clarify the conceptual characteristics of artifact utilization in nursing practice instruction. Five selected articles were analyzed using the concept analysis method by Walker and Avant. The attributes, antecedents, and consequences of the concept were extracted from the target literature. The analysis revealed two attributes (“connecting people to people” and “connecting people to objects”);two antecedents (“recognition of artifacts” and “selection of artifacts”);and two consequences (“designing a fulfilling learning environment” and “improving the quality of education”). The concept was defined as “promoting the utilization of artifacts by recognizing and selecting them, connecting people to people and people to objects, designing a fulfilling learning environment, and improving the quality of education”.
