Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ...Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this wor...A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this work investigates the thermal reaction of a dynamic fin system to a hybrid nanofluid with shape-based properties,flowing uniformly at a velocity U.The analysis focuses on four distinct types of nanoparticles,i.e.,Al2O3,Ag,carbon nanotube(CNT),and graphene.Specifically,two of these particles exhibit a spherical shape,one possesses a cylindrical form,and the final type adopts a platelet morphology.The investigation delves into the pairing of these nanoparticles.The examination employs a combined approach to assess the constructional and thermal exchange characteristics of the hybrid nanofluid.The fin design,under the specified circumstances,gives rise to the derivation of a differential equation.The given equation is then transformed into a dimensionless form.Notably,the Hermite wavelet method is introduced for the first time to address the challenge posed by a moving fin submerged in a hybrid nanofluid with shape-dependent features.To validate the credibility of this research,the results obtained in this study are systematically compared with the numerical simulations.The examination discloses that the highest heat flux is achieved when combining nanoparticles with spherical and platelet shapes.展开更多
Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis.However, the application is problematic since different wavelets always return the same sequence analysis results. To ad...Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis.However, the application is problematic since different wavelets always return the same sequence analysis results. To address this issue, we applied five commonly used wavelets to theoretical sequence models to document some application criteria. Five gradual scale-change sequence models were simplified from the glutenite succession deposition by gravity flows to form the fining-upwards cycle sequences(FUCS) and coarsening-upwards cycle sequences(CUCS). After conducting theoretical sequence model tests, the optimal wavelet(sym4) was selected and successfully used with actual data to identify the sequence boundaries. We also proposed a new method to optimize the scale of continuous wavelet transformation(CWT) for sequence boundary determination. We found that the balloon-like marks in scalograms of db4, sym4, and coif4 wavelet determine, respectively, the fourth-order sequence boundary, the thick succession sequence boundaries in FUCS, and the thick succession sequence in FUCS and CUCS. Comparing the sequence identification results shows that the asymmetric wavelets had an advantage in high-frequency sequence boundary determination and sedimentary cycle discrimination through the amplitude trend of the coefficient, in which the sym4 wavelet is the most effective. In conclusion, the asymmetry of wavelets is the first selection principle, of which asymmetric wavelets are more sensitive to sediment deposition by flood flows. The match of the wavelet between the sequence is the second selection principle, in which the correlation of time-frequency impacts the accuracy of sequence surface localization. However, the waveform of the wavelet is a visual and abstract parameter for sequence boundary detection. The appropriate wavelet for lacustrine sequence analysis is the asymmetric wavelet with a weak number of side lobes. The depositional flows, depositional process,and autogenic are three sedimentary factors that influence the sequence analysis results.展开更多
Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition...Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.展开更多
This research explores the dynamic behaviour of horn-shaped single-walled carbon nanotubes(HS-SWCNTs)conveying viscous nanofluid with pulsating the influence of a longitudinal magnetic field.The analysis utilizes Eule...This research explores the dynamic behaviour of horn-shaped single-walled carbon nanotubes(HS-SWCNTs)conveying viscous nanofluid with pulsating the influence of a longitudinal magnetic field.The analysis utilizes Euler-Bernoulli beam model,considering the variable cross section,and incorporating Eringen’s nonlocal theory to formulate the governing partial differential equation of motion.The instability domain of HS-SWCNTs is estimated using Galerkin’s approach.Numerical analysis is performed using the Haar wavelet method.The critical buckling load obtained in this study is compared with previous research to validate the proposed model.The results highlight the effectiveness of the proposed model in assessing the vibrational characteristics of a complex multi-physics system involving HS-SWCNTs.Dispersion graphs and tables are presented to visualize the numerical findings pertaining to various system parameters,including the nonlocal parameter,magnetic flux,Knudsen number,and viscous factor.展开更多
This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features ...This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).展开更多
To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achiev...To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.展开更多
The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the ...The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.展开更多
Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes...Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.展开更多
As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal vari...As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.展开更多
Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer ...Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.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
The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone ver...The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone vertical growth trajectories by detecting the stable isotope signatures(2H and 18O compositions)of different shells in 27 hailstones from 9hailstorms,which allowed us to capture the ambient temperature during hailstone growth.The vertical growth trajectories were obtained by comparing the isotopic compositions of water condensate in clouds,derived from the Adiabatic Model,with those measured in hailstones.Although hailstone growth was primarily observed in the–10°C to–30°C temperature layer,the embryo formation height and subsequent growth trajectories significantly varied among hailstones.Embryos formed over a wide range of temperatures(–8.7°C to–33.4°C);four originated at temperatures above–15°C and 16originated at temperatures below–20°C,suggesting ice nuclei composed of bioproteins and mineral dust,respectively.Among the 27 measured hailstones,3 exhibited minimal vertical movement,16 exhibited a monotonic rise or fall,and the remaining 8 exhibited alternating up-down trajectories;only one experienced“recycling”during up-down drifting.Trajectory analysis revealed that similar-sized hailstones from a single storm tended to form at similar heights,whereas those larger than 25 mm in diameter exhibited at least one period of upward growth.Vertical trajectories derived from isotopic analysis were corroborated by radar hydrometeor observations.展开更多
Background China is seeing a growing demand for rehabilitation treatments for post-stroke upper limb spastic paresis(PSSP-UL).Although acupuncture is known to be effective for PSSP-UL,there is room to enhance its effi...Background China is seeing a growing demand for rehabilitation treatments for post-stroke upper limb spastic paresis(PSSP-UL).Although acupuncture is known to be effective for PSSP-UL,there is room to enhance its efficacy.Objective This study explored a semi-personalized acupuncture approach for PSSP-UL that used three-dimensional kinematic analysis(3DKA)results to select additional acupoints,and investigated the feasibility,efficacy and safety of this approach.Design,setting,participants and interventions This single-blind,single-center,randomized,controlled trial involved 74 participants who experienced a first-ever ischemic or hemorrhagic stroke with spastic upper limb paresis.The participants were then randomly assigned to the intervention group or the control group in a 1:1 ratio.Both groups received conventional treatments and acupuncture treatment 5 days a week for 4 weeks.The main acupoints in both groups were the same,while participants in the intervention group received additional acupoints selected on the basis of 3DKA results.Follow-up assessments were conducted for 8 weeks after the treatment.Main outcome measures The primary outcome was the Fugl-Meyer Assessment for Upper Extremity(FMA-UE)response rate(≥6-point change)at week 4.Secondary outcomes included changes in motor function(FMA-UE),Brunnstrom recovery stage(BRS),manual muscle test(MMT),spasticity(Modified Ashworth Scale,MAS),and activities of daily life(Modified Barthel Index,MBI)at week 4 and week 12.Results Sixty-four participants completed the trial and underwent analyses.Compared with control group,the intervention group exhibited a significantly higher FMA-UE response rate at week 4(χ^(2)=5.479,P=0.019)and greater improvements in FMA-UE at both week 4 and week 12(both P<0.001).The intervention group also showed bigger improvements from baseline in the MMT grades for shoulder adduction and elbow flexion at weeks 4 and 12 as well as thumb adduction at week 4(P=0.007,P=0.049,P=0.019,P=0.008,P=0.029,respectively).The intervention group showed a better change in the MBI at both week 4 and week 12(P=0.004 and P=0.010,respectively).Although the intervention group had a higher BRS for the hand at week 12(P=0.041),no intergroup differences were observed at week 4(all P>0.05).The two groups showed no differences in MAS grades as well as in BRS for the arm at weeks 4 and 12(all P>0.05).Conclusion Semi-personalized acupuncture prescription based on 3DKA results significantly improved motor function,muscle strength,and activities of daily living in patients with PSSP-UL.展开更多
The high-temperature dissolution behavior of primary carbides in samples taken from GCr15 continuous-casting bloom was observed in-situ by confocal laser scanning microscopy.Equations were fitted to the dissolution ki...The high-temperature dissolution behavior of primary carbides in samples taken from GCr15 continuous-casting bloom was observed in-situ by confocal laser scanning microscopy.Equations were fitted to the dissolution kinetics of primary carbides during either heating or soaking.Dissolution of carbides proceeded in three stages(fast→slow→faster)as either temperature or holding time was increased.During the heating process and during the first and third stages of the soaking process,the original size of the carbides determined the steepness of the slope,but during the middle(“slow”)stage of the soaking process,the slope remained zero.The initial size of the carbides varied greatly,but their final dissolution temperature fell within the narrow range of 1210-1235℃,and the holding time remained within 50 min.Fractal analysis was used to study the morphological characteristics of small and medium-sized carbides during the dissolution process.According to changes in the fractal dimension before and after soaking,the carbides tended to evolve towards a more regular morphology.展开更多
The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Pale...The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.展开更多
The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns,biological systems,and secur...The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns,biological systems,and secure communications.So,this work aims to present the numerical performances of the nonlinear fractional Rössler attractor system under Caputo derivatives by designing the numerical framework based on Ultraspherical wavelets.The Caputo fractional Rössler attractor model is simulated into two categories,(i)Asymmetric and(ii)Symmetric.The Ultraspherical wavelets basis with suitable collocation grids is implemented for comprehensive error analysis in the solutions of the Caputo fractional Rössler attractor model,depicting each computation in graphs and tables to analyze how fractional order affects the model’s dynamics.Approximate solutions obtained through the proposed scheme for integer order are well comparable with the fourth-order Runge-Kutta method.Also,the stability analyses of the considered model are discussed for different equilibrium points.Various fractional orders are considered while performing numerical simulations for the Caputo fractional Rössler attractor model by using Mathematica.The suggested approach can solve another non-linear fractional model due to its straightforward implementation.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
Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in relat...Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.展开更多
基金supported by the Technology Innovation Program(20023566,‘Development and Demonstration of Industrial IoT and AI-Based Process Facility Intelligence Support System in Small and Medium Manufacturing Sites’)funded by the Ministry of Trade,Industry,&Energy(MOTIE,Republic of Korea).
文摘Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
文摘A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this work investigates the thermal reaction of a dynamic fin system to a hybrid nanofluid with shape-based properties,flowing uniformly at a velocity U.The analysis focuses on four distinct types of nanoparticles,i.e.,Al2O3,Ag,carbon nanotube(CNT),and graphene.Specifically,two of these particles exhibit a spherical shape,one possesses a cylindrical form,and the final type adopts a platelet morphology.The investigation delves into the pairing of these nanoparticles.The examination employs a combined approach to assess the constructional and thermal exchange characteristics of the hybrid nanofluid.The fin design,under the specified circumstances,gives rise to the derivation of a differential equation.The given equation is then transformed into a dimensionless form.Notably,the Hermite wavelet method is introduced for the first time to address the challenge posed by a moving fin submerged in a hybrid nanofluid with shape-dependent features.To validate the credibility of this research,the results obtained in this study are systematically compared with the numerical simulations.The examination discloses that the highest heat flux is achieved when combining nanoparticles with spherical and platelet shapes.
文摘Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis.However, the application is problematic since different wavelets always return the same sequence analysis results. To address this issue, we applied five commonly used wavelets to theoretical sequence models to document some application criteria. Five gradual scale-change sequence models were simplified from the glutenite succession deposition by gravity flows to form the fining-upwards cycle sequences(FUCS) and coarsening-upwards cycle sequences(CUCS). After conducting theoretical sequence model tests, the optimal wavelet(sym4) was selected and successfully used with actual data to identify the sequence boundaries. We also proposed a new method to optimize the scale of continuous wavelet transformation(CWT) for sequence boundary determination. We found that the balloon-like marks in scalograms of db4, sym4, and coif4 wavelet determine, respectively, the fourth-order sequence boundary, the thick succession sequence boundaries in FUCS, and the thick succession sequence in FUCS and CUCS. Comparing the sequence identification results shows that the asymmetric wavelets had an advantage in high-frequency sequence boundary determination and sedimentary cycle discrimination through the amplitude trend of the coefficient, in which the sym4 wavelet is the most effective. In conclusion, the asymmetry of wavelets is the first selection principle, of which asymmetric wavelets are more sensitive to sediment deposition by flood flows. The match of the wavelet between the sequence is the second selection principle, in which the correlation of time-frequency impacts the accuracy of sequence surface localization. However, the waveform of the wavelet is a visual and abstract parameter for sequence boundary detection. The appropriate wavelet for lacustrine sequence analysis is the asymmetric wavelet with a weak number of side lobes. The depositional flows, depositional process,and autogenic are three sedimentary factors that influence the sequence analysis results.
文摘Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.
文摘This research explores the dynamic behaviour of horn-shaped single-walled carbon nanotubes(HS-SWCNTs)conveying viscous nanofluid with pulsating the influence of a longitudinal magnetic field.The analysis utilizes Euler-Bernoulli beam model,considering the variable cross section,and incorporating Eringen’s nonlocal theory to formulate the governing partial differential equation of motion.The instability domain of HS-SWCNTs is estimated using Galerkin’s approach.Numerical analysis is performed using the Haar wavelet method.The critical buckling load obtained in this study is compared with previous research to validate the proposed model.The results highlight the effectiveness of the proposed model in assessing the vibrational characteristics of a complex multi-physics system involving HS-SWCNTs.Dispersion graphs and tables are presented to visualize the numerical findings pertaining to various system parameters,including the nonlocal parameter,magnetic flux,Knudsen number,and viscous factor.
文摘This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).
基金supported by the National Natural Science Foundation of China under Grant No.21933006.
文摘To eliminate distortion caused by vertical drift and illusory slopes in atomic force microscopy(AFM)imaging,a lifting-wavelet-based iterative thresholding correction method is proposed in this paper.This method achieves high-quality AFM imaging via line-by-line corrections for each distorted profile along the fast axis.The key to this line-by-line correction is to accurately simulate the profile distortion of each scanning row.Therefore,a data preprocessing approach is first developed to roughly filter out most of the height data that impairs the accuracy of distortion modeling.This process is implemented through an internal double-screening mechanism.A line-fitting method is adopted to preliminarily screen out the obvious specimens.Lifting wavelet analysis is then carried out to identify the base parts that are mistakenly filtered out as specimens so as to preserve most of the base profiles and provide a good basis for further distortion modeling.Next,an iterative thresholding algorithm is developed to precisely simulate the profile distortion.By utilizing the roughly screened base profile,the optimal threshold,which is used to screen out the pure bases suitable for distortion modeling,is determined through iteration with a specified error rule.On this basis,the profile distortion is accurately modeled through line fitting on the finely screened base data,and the correction is implemented by subtracting the modeling result from the distorted profile.Finally,the effectiveness of the proposed method is verified through experiments and applications.
基金supported by grants from the National Natural Science Foundation of China(Grant No.31960585)Science and Technology Major Project of Guangxi(Grant No.Guike AA22068092)+1 种基金Guangxi Science and Technology Vanguard Special Action Project(Grant No.202204)State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources(Grant Nos.SKLCUSA-a201906,SKLCU-SA-c201901)。
文摘The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.
基金supported by grants from the Natural Science Foundation of Tianjin(General Program),Nos.23JCYBJC01390(to RL),22JCYBJC00220(to XC),and 22JCYBJC00210(to QL).
文摘Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the Ministry of Science and Technology(MOST)Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.
基金Opening Foundation of Key Laboratory of Explosive Energy Utilization and Control,Anhui Province(BP20240104)Graduate Innovation Program of China University of Mining and Technology(2024WLJCRCZL049)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2701)。
文摘Because of the challenge of compounding lightweight,high-strength Ti/Al alloys due to their considerable disparity in properties,Al 6063 as intermediate layer was proposed to fabricate TC4/Al 6063/Al 7075 three-layer composite plate by explosive welding.The microscopic properties of each bonding interface were elucidated through field emission scanning electron microscope and electron backscattered diffraction(EBSD).A methodology combining finite element method-smoothed particle hydrodynamics(FEM-SPH)and molecular dynamics(MD)was proposed for the analysis of the forming and evolution characteristics of explosive welding interfaces at multi-scale.The results demonstrate that the bonding interface morphologies of TC4/Al 6063 and Al 6063/Al 7075 exhibit a flat and wavy configuration,without discernible defects or cracks.The phenomenon of grain refinement is observed in the vicinity of the two bonding interfaces.Furthermore,the degree of plastic deformation of TC4 and Al 7075 is more pronounced than that of Al 6063 in the intermediate layer.The interface morphology characteristics obtained by FEM-SPH simulation exhibit a high degree of similarity to the experimental results.MD simulations reveal that the diffusion of interfacial elements predominantly occurs during the unloading phase,and the simulated thickness of interfacial diffusion aligns well with experimental outcomes.The introduction of intermediate layer in the explosive welding process can effectively produce high-quality titanium/aluminum alloy composite plates.Furthermore,this approach offers a multi-scale simulation strategy for the study of explosive welding bonding interfaces.
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.
基金supported by the National Natural Science Foundation of China(Grant No.42030607)the Beijing Municipal Science and Technology Commission(Grant No.Z251100004525005)the National Science Foundation/National Center for Atmospheric Research,and NASA(Grant No.80NSSC22M0129)。
文摘The growth trajectory of hailstones within clouds has remained elusive due to the inability to trace them directly,impeding the comprehension of their underlying growth mechanisms.This study investigated hailstone vertical growth trajectories by detecting the stable isotope signatures(2H and 18O compositions)of different shells in 27 hailstones from 9hailstorms,which allowed us to capture the ambient temperature during hailstone growth.The vertical growth trajectories were obtained by comparing the isotopic compositions of water condensate in clouds,derived from the Adiabatic Model,with those measured in hailstones.Although hailstone growth was primarily observed in the–10°C to–30°C temperature layer,the embryo formation height and subsequent growth trajectories significantly varied among hailstones.Embryos formed over a wide range of temperatures(–8.7°C to–33.4°C);four originated at temperatures above–15°C and 16originated at temperatures below–20°C,suggesting ice nuclei composed of bioproteins and mineral dust,respectively.Among the 27 measured hailstones,3 exhibited minimal vertical movement,16 exhibited a monotonic rise or fall,and the remaining 8 exhibited alternating up-down trajectories;only one experienced“recycling”during up-down drifting.Trajectory analysis revealed that similar-sized hailstones from a single storm tended to form at similar heights,whereas those larger than 25 mm in diameter exhibited at least one period of upward growth.Vertical trajectories derived from isotopic analysis were corroborated by radar hydrometeor observations.
基金funded by Science Foundation for Youth supported by Shanghai Municipal Health Commission(No.20204Y0313)Sailing Program with the support of Science and Technology Commission of Shanghai Municipality(No.21YF1443800).
文摘Background China is seeing a growing demand for rehabilitation treatments for post-stroke upper limb spastic paresis(PSSP-UL).Although acupuncture is known to be effective for PSSP-UL,there is room to enhance its efficacy.Objective This study explored a semi-personalized acupuncture approach for PSSP-UL that used three-dimensional kinematic analysis(3DKA)results to select additional acupoints,and investigated the feasibility,efficacy and safety of this approach.Design,setting,participants and interventions This single-blind,single-center,randomized,controlled trial involved 74 participants who experienced a first-ever ischemic or hemorrhagic stroke with spastic upper limb paresis.The participants were then randomly assigned to the intervention group or the control group in a 1:1 ratio.Both groups received conventional treatments and acupuncture treatment 5 days a week for 4 weeks.The main acupoints in both groups were the same,while participants in the intervention group received additional acupoints selected on the basis of 3DKA results.Follow-up assessments were conducted for 8 weeks after the treatment.Main outcome measures The primary outcome was the Fugl-Meyer Assessment for Upper Extremity(FMA-UE)response rate(≥6-point change)at week 4.Secondary outcomes included changes in motor function(FMA-UE),Brunnstrom recovery stage(BRS),manual muscle test(MMT),spasticity(Modified Ashworth Scale,MAS),and activities of daily life(Modified Barthel Index,MBI)at week 4 and week 12.Results Sixty-four participants completed the trial and underwent analyses.Compared with control group,the intervention group exhibited a significantly higher FMA-UE response rate at week 4(χ^(2)=5.479,P=0.019)and greater improvements in FMA-UE at both week 4 and week 12(both P<0.001).The intervention group also showed bigger improvements from baseline in the MMT grades for shoulder adduction and elbow flexion at weeks 4 and 12 as well as thumb adduction at week 4(P=0.007,P=0.049,P=0.019,P=0.008,P=0.029,respectively).The intervention group showed a better change in the MBI at both week 4 and week 12(P=0.004 and P=0.010,respectively).Although the intervention group had a higher BRS for the hand at week 12(P=0.041),no intergroup differences were observed at week 4(all P>0.05).The two groups showed no differences in MAS grades as well as in BRS for the arm at weeks 4 and 12(all P>0.05).Conclusion Semi-personalized acupuncture prescription based on 3DKA results significantly improved motor function,muscle strength,and activities of daily living in patients with PSSP-UL.
基金supported by Independent Research Project of State Key Laboratory of Advanced Special Steel,Shanghai Key Laboratory of Advanced Ferrometallurgy,Shanghai University(SKLASS-2023-Z13)the Science and Technology Commission of Shanghai Municipality(No.19DZ2270200)+1 种基金A portion of the work was performed at US National High Magnetic Field Laboratory,which is supported by the National Science Foundation(Cooperative Agreement No.DMR-1157490 and DMR-1644779)the State of Florida.Thanks also to Mary Tyler for editing.
文摘The high-temperature dissolution behavior of primary carbides in samples taken from GCr15 continuous-casting bloom was observed in-situ by confocal laser scanning microscopy.Equations were fitted to the dissolution kinetics of primary carbides during either heating or soaking.Dissolution of carbides proceeded in three stages(fast→slow→faster)as either temperature or holding time was increased.During the heating process and during the first and third stages of the soaking process,the original size of the carbides determined the steepness of the slope,but during the middle(“slow”)stage of the soaking process,the slope remained zero.The initial size of the carbides varied greatly,but their final dissolution temperature fell within the narrow range of 1210-1235℃,and the holding time remained within 50 min.Fractal analysis was used to study the morphological characteristics of small and medium-sized carbides during the dissolution process.According to changes in the fractal dimension before and after soaking,the carbides tended to evolve towards a more regular morphology.
基金“High precision prestack reverse time depth migration imaging of long array seismic data in the East China Sea Shelf Basin”of the National Natural Science Foundation of China(No.42106207)“Seismic acquisition technology for deep strata under strong shielding layers in the sea and rugged seabed”of Laoshan Laboratory Science and Technology Innovation Project(No.LSKJ202203404)“Research on the compensation methods of the middledeep weak seismic reflections in the South Yellow Sea based on multi-resolution HHT time-frequency analysis”of the National Natural Science Foundation of China(No.42106208).
文摘The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.
基金"La derivada fraccional generalizada,nuevos resultados y aplicaciones a desigualdades integrales"Cod UIO-077-2024supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns,biological systems,and secure communications.So,this work aims to present the numerical performances of the nonlinear fractional Rössler attractor system under Caputo derivatives by designing the numerical framework based on Ultraspherical wavelets.The Caputo fractional Rössler attractor model is simulated into two categories,(i)Asymmetric and(ii)Symmetric.The Ultraspherical wavelets basis with suitable collocation grids is implemented for comprehensive error analysis in the solutions of the Caputo fractional Rössler attractor model,depicting each computation in graphs and tables to analyze how fractional order affects the model’s dynamics.Approximate solutions obtained through the proposed scheme for integer order are well comparable with the fourth-order Runge-Kutta method.Also,the stability analyses of the considered model are discussed for different equilibrium points.Various fractional orders are considered while performing numerical simulations for the Caputo fractional Rössler attractor model by using Mathematica.The suggested approach can solve another non-linear fractional model due to its straightforward implementation.
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supported by the National Natural Science Foundation of China(61902095).
文摘Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
