Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration para...Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red ti...As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red tide outbreak can provide guidance to the coastal management,and is of great value to the aquaculture industry and marine environment protection.An approach for the risk assessment of red tide occurrence using spectral indices was made.The optimal spectral indices were explored from three candidates,namely two-band ratio(TBR)method,three-band spectral index(TBSI)method,and fluorescence baseline(FLB)method.The correlations between the spectral indices and the red tide occurrence were quantitatively evaluated through analysis of variance(ANOVA).The risk maps for the Beibu Gulf and the Bohai Bay in China were produced with the normalized spectral indices based on the multi-spectral observation from Sentinel-3 satellite.Results show that both TBR and TBSI values have significant correlations with the occurrences of red tide as the ANOVA results.TBSI illustrated correctly the risk of red tide occurrence in the risk maps and was the optimal spectral index offshore risk assessment of red tide.FLB method failed to recognize the high-risk regions and may not be the appropriate spectral index.The risk assessment method proposed in this study can provide early alarms on red tide occurrence and help timely the countermeasure against potential harms.展开更多
The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the pr...The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.展开更多
To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed,...To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.展开更多
BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomogra...BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomography imaging(GSI)to improve diagnostic accuracy in lymph node metastasis(LNM)assessment.AIM To extensively investigate the clinical utility of GSI in the preoperative assessment of CRC.METHODS The subject population included 200 patients with CRC who were admitted to Zibo Central Hospital from January 2022 to December 2023.All patients underwent dual-phase contrast-enhanced scans in the arterial and venous phases using GSI before surgical intervention.During the research,meticulous quantification was conducted regarding the number of patients with CRC with LNM as well as the exact count of metastatic lymph nodes.Moreover,for both metastatic and non-metastatic lymph nodes,the short diameter at the maximum crosssectional area(covering the axial,sagittal,and coronal planes),morphological features(including manifestations such as margin blurring,aggregation,and enhancement),and spectral parameters in the arterial and venous phases[specifically iodine concentration(IC),normalized IC(NIC),and the slope of the spectral curve(λHU)]were measured and recorded,and a comparative analysis was conducted.The diagnostic efficacy of each index with differences was systematically assessed using the receiver operating characteristic(ROC)curve.Concurrently,receiver operating characteristic curves were constructed for LNM screening based on the short diameter at the maximum cross-sectional area of lymph nodes and each spectral parameter in the arterial and venous phases.RESULTS The area under the curve of GSI for diagnosing LNM in patients with CRC can reach 0.897,with sensitivity,specificity,and accuracy of 92.59%,85.87%,and 89.50%,respectively.A total of 265 lymph nodes were analyzed from the 200 participants with CRC,with metastatic lymph nodes accounting for 56.60%.Compared with nonmetastatic lymph nodes,the short diameters of metastatic lymph nodes in the axial,sagittal,and coronal planes were significantly increased,whereas the IC values in the arterial and venous phases,the NIC value in the arterial phase,and theλHU values in the arterial and venous phases were significantly decreased.The short axial,sagittal,and coronal diameters,arterial-phase IC,venous-phase IC,arterial-phase NIC,arterial-phaseλHU,and venousphaseλHU for diagnosing metastatic lymph nodes demonstrated area under the curve values of 0.631,0.681,0.659,0.862,0.808,0.831,0.801,and 0.706,respectively.CONCLUSION GSI exhibits substantial clinical significance in the preoperative assessment of CRC.Among the parameters assessed,the arterial-phase IC demonstrates the most outstanding diagnostic performance,effectively improving the diagnostic efficacy for preoperative LNM in CRC.展开更多
In this article,we develop the Laplace transform(LT)based Chebyshev spectral collocation method(CSCM)to approximate the time fractional advection-diffusion equation,incorporating the Atangana-Baleanu Caputo(ABC)deriva...In this article,we develop the Laplace transform(LT)based Chebyshev spectral collocation method(CSCM)to approximate the time fractional advection-diffusion equation,incorporating the Atangana-Baleanu Caputo(ABC)derivative.The advection-diffusion equation,which governs the transport of mass,heat,or energy through combined advection and diffusion processes,is central to modeling physical systems with nonlocal behavior.Our numerical scheme employs the LT to transform the time-dependent time-fractional PDEs into a time-independent PDE in LT domain,eliminating the need for classical time-stepping methods that often suffer from stability constraints.For spatial discretization,we employ the CSCM,where the solution is approximated using Lagrange interpolation polynomial based on the Chebyshev collocation nodes,achieving exponential convergence that outperforms the algebraic convergence rates of finite difference and finite element methods.Finally,the solution is reverted to the time domain using contour integration technique.We also establish the existence and uniqueness of the solution for the proposed problem.The performance,efficiency,and accuracy of the proposed method are validated through various fractional advection-diffusion problems.The computed results demonstrate that the proposed method has less computational cost and is highly accurate.展开更多
This study presents a new boundary element method(BEM)framework for the numerical solution of general time-dependent or transient problems.By reformulating the time derivative as a domain integral,the framework effect...This study presents a new boundary element method(BEM)framework for the numerical solution of general time-dependent or transient problems.By reformulating the time derivative as a domain integral,the framework effectively decouples the treatment of spatial and temporal variables,allowing for the independent application of specialized discretization methods.For the temporal domain,we introduce an innovative time-spectral integration technique,which is based on Gaussian-quadrature-based orthogonal polynomial expansions.This method not only achieves arbitrary orders of accuracy but also significantly enhances computational efficiency and stability,particularly for simulations involving rapid transients or long-time dynamic simulations.The domain integrals in the spatial domain are calculated using the scaled coordinate transformation BEM(SCT-BEM),a mathematically rigorous technique that converts domain integrals into equivalent boundary integrals,preserving the boundary-only discretization advantage inherent in BEM.Numerical experiments on transient heat conduction and dynamic wave propagation further demonstrate the framework’s performance and capabilities.These experiments show that the proposed framework outperforms traditional time-stepping BEM methods,particularly in terms of stability,convergence rates,and computational cost,making it a highly promising tool for practical engineering applications.展开更多
Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macro...Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.展开更多
Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 us...Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 using MODIS observations at 470,500,and 550 nm,combined with meteorological data and ground-based air quality records.The Mann–Kendall test identified a statistically significant decreasing trend at 500 nm(slope=–2.07,p<0.05),while 470 and 550 nm showed weak,nonsignificant declines.AOD peaked in April–June,declined during the monsoon,and rose again in October–November due to burning and festivals.Correlation analysis demonstrated strong positive associations with PM_(2.5),PM_(10),and temperature,with minimum temperature emerging as the most influential predictor,whereas relative humidity showed weak or negative relationships.Anomaly detection confirmed episodic high-AOD events during dust storms,winter inversions,and agricultural burning.Predictive modelling using Multiple Linear Regression(MLR)and Random Forest highlighted the complementary roles of linear drivers.Nonlinear dynamics,with Random Forest achieving high predictive accuracy(R^(2)=0.892 for training,0.588 for testing).These findings demonstrate that aerosol variability in Jaipur is governed by a dual influence of natural dust and anthropogenic emissions,with wavelength-specific responses.The results provide scientific evidence for integrating satellite monitoring,ground observations,and predictive models into urban air quality management and climate adaptation strategies in semi-arid regions.展开更多
Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'...Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.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
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary gro...The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.展开更多
Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adj...Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adjusted life years(DALYs),stratified by age,sex,and economic development level based on data retrieved from the Global Burden of Disease study 2021.Decomposition and frontier analyses were employed to identify key drivers of burden variation and visualize potential reductions based on development levels.Results:Between 1990 and 2021,the global burden of ARIs showed a significant decline in both achievable age-standardized DALYs rate and age-standardized mortality rates(EAPC=-3.87 and-3.81,respectively).Different age groups and sex witnessed different levels of ARI burden,males experienced heavier burden than females and the 0-4 years-old group experienced heavier burden than other study age groups.Most of the 204 countries and territories experienced a downward trend of ARI burden,with slight increases observed only in Lesotho and Dominica.A negative correlation was found between the Socio-demographic Index and ARI burden.Decomposition analysis indicated that the significant decreases in deaths and DALYs were primarily driven by epidemiological changes.Conclusions:The global burden of ARIs among children and adolescents has declined over the past three decades,but substantial regional disparities persist.Targeted public health strategies are needed to address the continued ARI burden in high-risk regions and vulnerable age groups.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hoch...In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.展开更多
Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study ...Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study revealed that removing bags from‘Gengcunyangtao’red-fleshed peach fruits triggers the rapid and uniform accumulation of anthocyanins in the flesh,resulting in anthocyanin levels that exceed those in unbagged fruits.The exposure to light after bag removal triggered significant increases in anthocyanin levels within 24 h.This was accompanied by the rapid upregulation of light-responsive and flavonoid biosynthetic gene expression levels within 6 h.A metabolomic analysis indicated that anthocyanin precursors,especially p-coumaric acid,accumulated before bag removal,thereby increasing substrate availability for rapid anthocyanin synthesis.On the basis of a weighted gene co-expression network analysis,MYB transcription factors,anthocyanin transporters,glutathione S-transferase,and multidrug and toxic compound extrusion(MATE)were identified as key regulators that coordinate precursor storage along with light-induced transcriptional activation.Notably,PpMYB4 binds to the promoter of PpGSTF14 and activates its expression,thereby promoting anthocyanin accumulation.The study findings elucidated the temporal coordination of metabolic priming and light-responsive transcriptional regulation driving rapid anthocyanin biosynthesis,with possible implications for improving peach fruit flesh coloration.展开更多
An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximatio...An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.展开更多
A new quadrilateral edge element method is proposed and analyzed for Maxwell equations.This proposed method is based on Duan-Liang quadrilateral element(Math.Comp.73(2004),pp.1–18).When applied to the eigenvalue prob...A new quadrilateral edge element method is proposed and analyzed for Maxwell equations.This proposed method is based on Duan-Liang quadrilateral element(Math.Comp.73(2004),pp.1–18).When applied to the eigenvalue problem,the method is spectral-correct and spurious-free.Stability and error estimates are obtained,including the interpolation error estimates and the error estimates between the finite element solution and the exact solution.The method is suitable for singular solution as well as smooth solution,and consequently,the method is valid for nonconvex domains which may have a number of reentrant corners.Of course,the method is suitable for arbitrary quadrilaterals(under the usual shape-regular condition).展开更多
基金supported by the National Natural Science Foundation of China(No.41804141)。
文摘Energy resolution calibration is crucial for gamma-ray spectral analysis,as measured using a scintillation detector.A locally constrained regularization method was proposed to determine the resolution calibration parameters.First,a Monte Carlo simulation model consistent with an actual measurement system was constructed to obtain the energy deposition distribution in the scintillation crystal.Subsequently,the regularization objective function is established based on weighted least squares and additional constraints.Additional constraints were designed using a special weighting scheme based on the incident gamma-ray energies.Subsequently,an intelligent algorithm was introduced to search for the optimal resolution calibration parameters by minimizing the objective function.The most appropriate regularization parameter was determined through mathematical experiments.When the regularization parameter was 30,the calibrated results exhibited the minimum RMSE.Simulations and test pit experiments were conducted to verify the performance of the proposed method.The simulation results demonstrate that the proposed algorithm can determine resolution calibration parameters more accurately than the traditional weighted least squares,and the test pit experimental results show that the R-squares between the calibrated and measured spectra are larger than 0.99.The accurate resolution calibration parameters determined by the proposed method lay the foundation for gamma-ray spectral processing and simulation benchmarking.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金Supported by the National Key R&D Program of China(No.2023YFC3108300)the Dalian High-level Talent Innovation Program(No.2022RG02)the Fundamental Research Funds for the Central Universities(No.3132023507)。
文摘As a frequently occurred marine pollution phenomenon,red tides of water body due to eutrophication cause massive mortality of marine organisms and serious ecological problems.The early warning and prediction of red tide outbreak can provide guidance to the coastal management,and is of great value to the aquaculture industry and marine environment protection.An approach for the risk assessment of red tide occurrence using spectral indices was made.The optimal spectral indices were explored from three candidates,namely two-band ratio(TBR)method,three-band spectral index(TBSI)method,and fluorescence baseline(FLB)method.The correlations between the spectral indices and the red tide occurrence were quantitatively evaluated through analysis of variance(ANOVA).The risk maps for the Beibu Gulf and the Bohai Bay in China were produced with the normalized spectral indices based on the multi-spectral observation from Sentinel-3 satellite.Results show that both TBR and TBSI values have significant correlations with the occurrences of red tide as the ANOVA results.TBSI illustrated correctly the risk of red tide occurrence in the risk maps and was the optimal spectral index offshore risk assessment of red tide.FLB method failed to recognize the high-risk regions and may not be the appropriate spectral index.The risk assessment method proposed in this study can provide early alarms on red tide occurrence and help timely the countermeasure against potential harms.
基金supported by the National Science Foundation of China(Grant Nos.42374205 and 41974179)the Specialized Research Fund of the National Space Science Center,Chinese Academy of Sciences(Grant No.E4PD3010)supported by the Specialized Research Fund for State Key Laboratories.
文摘The three-dimensional spectral analysis method was applied to airglow data from September 2023 to August 2024 derivedfrom an OH airglow imager located at the Hejing station (42.79°N, 83.73°E) to study the propagation characteristics of gravity waves(GWs) over Northwest China. We found that obvious seasonal variations occur in the propagation of GWs. In spring, GWs mainlypropagate in the northeast direction. In summer and autumn, GWs mainly propagate in the north direction. However, GWs mainlypropagate in the south direction in winter. The direction of GW propagation in the zonal direction is controlled by the wind-filteringeffect, whereas the north–south meridional direction is mainly determined by the location of the wave source. We found that the averageenergy spectrum exhibits a 10%–20% higher intensity in summer and winter compared with spring and autumn. For the first time, wereport the seasonal variation characteristics of GWs over the inland areas of Northwest China, which is of great significance forunderstanding the regional distribution characteristics of GWs.
基金supported by the Jilin Province Science and Technology Development Plan Item (No.20240402068GH)。
文摘To identify coatings and analyze the anti-detection capabilities of camouflage patterns, material samples can be prepared using the super-pixel segmentation method. A spectral polarization imaging system is developed, based on the principle of bidirectional reflectance distribution function(BRDF), to obtain spectral reflection intensities of coatings at full spatial angles, and use polarization images to calculate the refractive index by the Fresnel equation. The index is then coupled into TorranceSparrow model to simulate the spectral scattering intensity to mutually verify the experimental results. The spectral scattering characteristics of standard camouflage patterns are then revealed and pinpoint the signature band and the angle of reflecting sensitivity.
文摘BACKGROUND The diagnostic accuracy for detecting metastatic lymph nodes in colorectal cancer(CRC)remains suboptimal.To address this limitation,our study investigates the potential of gemstone spectral computed tomography imaging(GSI)to improve diagnostic accuracy in lymph node metastasis(LNM)assessment.AIM To extensively investigate the clinical utility of GSI in the preoperative assessment of CRC.METHODS The subject population included 200 patients with CRC who were admitted to Zibo Central Hospital from January 2022 to December 2023.All patients underwent dual-phase contrast-enhanced scans in the arterial and venous phases using GSI before surgical intervention.During the research,meticulous quantification was conducted regarding the number of patients with CRC with LNM as well as the exact count of metastatic lymph nodes.Moreover,for both metastatic and non-metastatic lymph nodes,the short diameter at the maximum crosssectional area(covering the axial,sagittal,and coronal planes),morphological features(including manifestations such as margin blurring,aggregation,and enhancement),and spectral parameters in the arterial and venous phases[specifically iodine concentration(IC),normalized IC(NIC),and the slope of the spectral curve(λHU)]were measured and recorded,and a comparative analysis was conducted.The diagnostic efficacy of each index with differences was systematically assessed using the receiver operating characteristic(ROC)curve.Concurrently,receiver operating characteristic curves were constructed for LNM screening based on the short diameter at the maximum cross-sectional area of lymph nodes and each spectral parameter in the arterial and venous phases.RESULTS The area under the curve of GSI for diagnosing LNM in patients with CRC can reach 0.897,with sensitivity,specificity,and accuracy of 92.59%,85.87%,and 89.50%,respectively.A total of 265 lymph nodes were analyzed from the 200 participants with CRC,with metastatic lymph nodes accounting for 56.60%.Compared with nonmetastatic lymph nodes,the short diameters of metastatic lymph nodes in the axial,sagittal,and coronal planes were significantly increased,whereas the IC values in the arterial and venous phases,the NIC value in the arterial phase,and theλHU values in the arterial and venous phases were significantly decreased.The short axial,sagittal,and coronal diameters,arterial-phase IC,venous-phase IC,arterial-phase NIC,arterial-phaseλHU,and venousphaseλHU for diagnosing metastatic lymph nodes demonstrated area under the curve values of 0.631,0.681,0.659,0.862,0.808,0.831,0.801,and 0.706,respectively.CONCLUSION GSI exhibits substantial clinical significance in the preoperative assessment of CRC.Among the parameters assessed,the arterial-phase IC demonstrates the most outstanding diagnostic performance,effectively improving the diagnostic efficacy for preoperative LNM in CRC.
基金extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/174/46.
文摘In this article,we develop the Laplace transform(LT)based Chebyshev spectral collocation method(CSCM)to approximate the time fractional advection-diffusion equation,incorporating the Atangana-Baleanu Caputo(ABC)derivative.The advection-diffusion equation,which governs the transport of mass,heat,or energy through combined advection and diffusion processes,is central to modeling physical systems with nonlocal behavior.Our numerical scheme employs the LT to transform the time-dependent time-fractional PDEs into a time-independent PDE in LT domain,eliminating the need for classical time-stepping methods that often suffer from stability constraints.For spatial discretization,we employ the CSCM,where the solution is approximated using Lagrange interpolation polynomial based on the Chebyshev collocation nodes,achieving exponential convergence that outperforms the algebraic convergence rates of finite difference and finite element methods.Finally,the solution is reverted to the time domain using contour integration technique.We also establish the existence and uniqueness of the solution for the proposed problem.The performance,efficiency,and accuracy of the proposed method are validated through various fractional advection-diffusion problems.The computed results demonstrate that the proposed method has less computational cost and is highly accurate.
基金supported by the National Natural Science Foundation of China(Grant Nos.12372199,12422207,and W2431010)the Natural Science Foundation of Shandong Province of China(Grant No.ZR2021JQ02)the Ningbo Municipal Excellence Research Program(Zhejiang Province,China).
文摘This study presents a new boundary element method(BEM)framework for the numerical solution of general time-dependent or transient problems.By reformulating the time derivative as a domain integral,the framework effectively decouples the treatment of spatial and temporal variables,allowing for the independent application of specialized discretization methods.For the temporal domain,we introduce an innovative time-spectral integration technique,which is based on Gaussian-quadrature-based orthogonal polynomial expansions.This method not only achieves arbitrary orders of accuracy but also significantly enhances computational efficiency and stability,particularly for simulations involving rapid transients or long-time dynamic simulations.The domain integrals in the spatial domain are calculated using the scaled coordinate transformation BEM(SCT-BEM),a mathematically rigorous technique that converts domain integrals into equivalent boundary integrals,preserving the boundary-only discretization advantage inherent in BEM.Numerical experiments on transient heat conduction and dynamic wave propagation further demonstrate the framework’s performance and capabilities.These experiments show that the proposed framework outperforms traditional time-stepping BEM methods,particularly in terms of stability,convergence rates,and computational cost,making it a highly promising tool for practical engineering applications.
基金supported by Qingdao Key Medical and Health Discipline ProjectThe Intramural Research Program of the Affiliated Hospital of Qingdao University,No. 4910Qingdao West Coast New Area Science and Technology Project,No. 2020-55 (all to SW)。
文摘Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.
文摘Aerosol dynamics in semi-arid cities are key to understanding air quality and climate interactions.This study examines the spatiotemporal variability of Aerosol Optical Depth(AOD)over Jaipur,India,from 2018 to 2024 using MODIS observations at 470,500,and 550 nm,combined with meteorological data and ground-based air quality records.The Mann–Kendall test identified a statistically significant decreasing trend at 500 nm(slope=–2.07,p<0.05),while 470 and 550 nm showed weak,nonsignificant declines.AOD peaked in April–June,declined during the monsoon,and rose again in October–November due to burning and festivals.Correlation analysis demonstrated strong positive associations with PM_(2.5),PM_(10),and temperature,with minimum temperature emerging as the most influential predictor,whereas relative humidity showed weak or negative relationships.Anomaly detection confirmed episodic high-AOD events during dust storms,winter inversions,and agricultural burning.Predictive modelling using Multiple Linear Regression(MLR)and Random Forest highlighted the complementary roles of linear drivers.Nonlinear dynamics,with Random Forest achieving high predictive accuracy(R^(2)=0.892 for training,0.588 for testing).These findings demonstrate that aerosol variability in Jaipur is governed by a dual influence of natural dust and anthropogenic emissions,with wavelength-specific responses.The results provide scientific evidence for integrating satellite monitoring,ground observations,and predictive models into urban air quality management and climate adaptation strategies in semi-arid regions.
文摘Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.
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The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
文摘The rapid growth in available network bandwidth has directly contributed to an exponential increase in mobile data traffic,creating significant challenges for network energy consumption.Also,with the extraordinary growth of mobile communications,the data traffic has dramatically expanded,which has led to massive grid power consumption and incurred high operating expenditure(OPEX).However,the majority of current network designs struggle to efficientlymanage a massive amount of data using little power,which degrades energy efficiency performance.Thereby,it is necessary to have an efficient mechanism to reduce power consumption when processing large amounts of data in network data centers.Utilizing renewable energy sources to power the Cloud Radio Access Network(C-RAN)greatly reduces the need to purchase energy from the utility grid.In this paper,we propose a bandwidth-aware hybrid energypowered C-RAN that focuses on throughput and energy efficiency(EE)by lowering grid usage,aiming to enhance the EE.This paper examines the energy efficiency,spectral efficiency(SE),and average on-grid energy consumption,dealing with the major challenges of the temporal and spatial nature of traffic and renewable energy generation across various network setups.To assess the effectiveness of the suggested network by changing the transmission bandwidth,a comprehensive simulation has been conducted.The numerical findings support the efficacy of the suggested approach.
文摘Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adjusted life years(DALYs),stratified by age,sex,and economic development level based on data retrieved from the Global Burden of Disease study 2021.Decomposition and frontier analyses were employed to identify key drivers of burden variation and visualize potential reductions based on development levels.Results:Between 1990 and 2021,the global burden of ARIs showed a significant decline in both achievable age-standardized DALYs rate and age-standardized mortality rates(EAPC=-3.87 and-3.81,respectively).Different age groups and sex witnessed different levels of ARI burden,males experienced heavier burden than females and the 0-4 years-old group experienced heavier burden than other study age groups.Most of the 204 countries and territories experienced a downward trend of ARI burden,with slight increases observed only in Lesotho and Dominica.A negative correlation was found between the Socio-demographic Index and ARI burden.Decomposition analysis indicated that the significant decreases in deaths and DALYs were primarily driven by epidemiological changes.Conclusions:The global burden of ARIs among children and adolescents has declined over the past three decades,but substantial regional disparities persist.Targeted public health strategies are needed to address the continued ARI burden in high-risk regions and vulnerable age groups.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
基金Supported by National Natural Science Foundation of China(Grant No.11971418).
文摘In this paper,we offer a new construction of Stefan’s homological spectral sequence for Hopf Galois extensions,by using the double complex argument.Under the faithfully flat condition,a method for computation of Hochschild homology is given.
基金supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties(Grant No.2021C12066-4)Huzhou Agricultural Science and Technology Innovation Team Project(Grant No.2022HN01).
文摘Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study revealed that removing bags from‘Gengcunyangtao’red-fleshed peach fruits triggers the rapid and uniform accumulation of anthocyanins in the flesh,resulting in anthocyanin levels that exceed those in unbagged fruits.The exposure to light after bag removal triggered significant increases in anthocyanin levels within 24 h.This was accompanied by the rapid upregulation of light-responsive and flavonoid biosynthetic gene expression levels within 6 h.A metabolomic analysis indicated that anthocyanin precursors,especially p-coumaric acid,accumulated before bag removal,thereby increasing substrate availability for rapid anthocyanin synthesis.On the basis of a weighted gene co-expression network analysis,MYB transcription factors,anthocyanin transporters,glutathione S-transferase,and multidrug and toxic compound extrusion(MATE)were identified as key regulators that coordinate precursor storage along with light-induced transcriptional activation.Notably,PpMYB4 binds to the promoter of PpGSTF14 and activates its expression,thereby promoting anthocyanin accumulation.The study findings elucidated the temporal coordination of metabolic priming and light-responsive transcriptional regulation driving rapid anthocyanin biosynthesis,with possible implications for improving peach fruit flesh coloration.
基金supported by the National Natural Science Foundation of China(Nos.12422207 and 12372199).
文摘An efficient data-driven numerical framework is developed for transient heat conduction analysis in thin-walled structures.The proposed approach integrates spectral time discretization with neural network approximation,forming a spectral-integrated neural network(SINN)scheme tailored for problems characterized by long-time evolution.Temporal derivatives are treated through a spectral integration strategy based on orthogonal polynomial expansions,which significantly alleviates stability constraints associated with conventional time-marching schemes.A fully connected neural network is employed to approximate the temperature-related variables,while governing equa-tions and boundary conditions are enforced through a physics-informed loss formulation.Numerical investigations demonstrate that the proposed method maintains high accuracy even when large time steps are adopted,where standard numerical solvers often suffer from instability or excessive computational cost.Moreover,the framework exhibits strong robustness for ultrathin configurations with extreme aspect ratios,achieving relative errors on the order of 10−5 or lower.These results indicate that the SINN framework provides a reliable and efficient alternative for transient thermal analysis of thin-walled structures under challenging computational conditions.
基金supported by the National Natural Science Foundation of China(12401482)the second author was supported by the National Natural Science Foundation of China(12371371,12261160361,11971366)supported by the Open Research Fund of Hubei Key Laboratory of Computational Science,Wuhan University.
文摘A new quadrilateral edge element method is proposed and analyzed for Maxwell equations.This proposed method is based on Duan-Liang quadrilateral element(Math.Comp.73(2004),pp.1–18).When applied to the eigenvalue problem,the method is spectral-correct and spurious-free.Stability and error estimates are obtained,including the interpolation error estimates and the error estimates between the finite element solution and the exact solution.The method is suitable for singular solution as well as smooth solution,and consequently,the method is valid for nonconvex domains which may have a number of reentrant corners.Of course,the method is suitable for arbitrary quadrilaterals(under the usual shape-regular condition).
