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
Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characte...Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.展开更多
Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive di...Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.展开更多
Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC...Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.展开更多
Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high com...Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.展开更多
Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Wa...Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.展开更多
The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This...The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.展开更多
Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity effica...Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.展开更多
This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric...This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.展开更多
We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of t...We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.展开更多
Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamformin...Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.展开更多
Breast cancer,which is the most commonly diagnosed cancers among women,is a notable health issues globally.Breast cancer is a result of abnormal cells in the breast tissue growing out of control.Histopathology,which r...Breast cancer,which is the most commonly diagnosed cancers among women,is a notable health issues globally.Breast cancer is a result of abnormal cells in the breast tissue growing out of control.Histopathology,which refers to the detection and learning of tissue diseases,has appeared as a solution for breast cancer treatment as it plays a vital role in its diagnosis and classification.Thus,considerable research on histopathology in medical and computer science has been conducted to develop an effective method for breast cancer treatment.In this study,a vision Transformer(ViT)was employed to classify tumors into two classes,benign and malignant,in the Breast Cancer Histopathological Database(BreakHis).To enhance the model performance,we introduced the novel multi-head locality large kernel self-attention during fine-tuning,achieving an accuracy of 95.94%at 100×magnification,thereby improving the accuracy by 3.34%compared to a standard ViT(which uses multi-head self-attention).In addition,the application of principal component analysis for dimensionality reduction led to an accuracy improvement of 3.34%,highlighting its role in mitigating overfitting and reducing the computational complexity.In the final phase,SHapley Additive exPlanations,Local Interpretable Model-agnostic Explanations,and Gradient-weighted Class Activation Mapping were used for the interpretability and explainability of machine-learning models,aiding in understanding the feature importance and local explanations,and visualizing the model attention.In another experiment,ensemble learning with VGGIN further boosted the performance to 97.13%accuracy.Our approach exhibited a 0.98%to 17.13%improvement in accuracy compared with state-of-the-art methods,establishing a new benchmark for breast cancer histopathological image classification.展开更多
Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical...Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical components and sources of fine particulate matter(PM2.5)under different pollution levels were comparatively analyzed using PMF(Positive Matrix Factorization)and backward trajectory analysis.SNA(NO_(3)^(-),NH_(4)^(+),SO_(4)^(2-))was found to be the primary chemical component of PM2.5,making up 63.6%(clean days)to 69.7%(heavy pollution)of it.The NO_(3)^(-)concentration was 3.14(clean days)to 6.01(heavy pollution)times higher than that of SO_(4)^(2-).NO_(3)^(-),POC,Fe,Mn,Al concentrations increased,while SOC,EC,crustal elements(Ca,Si)and other water-soluble ions(WSIs)concentrations decreased as the pollution level increased.The contribution of secondary inorganics and biomass-burning emissions and industrial and ship emissions increased significantly as the pollution level increased,which accounted for 40.3%and 36.7%,respectively,in the heavy pollution stage.The contribution of traffic sources decreases gradually with increasing pollution levels,accounting for only 59.1%of the light pollution stage in the heavy pollution stage.PM_(2.5) and its main chemical components showed similar potential source distribution,located in the northwest(Fuyang,Huainan,Nanjing),south(Taizhou,Lishui,Jiande)and north(Taizhou,Yancheng).However,distinct transport routes were observed under the different air quality levels.During the heavy pollution period,the polluted air masses primarily came from the harbor regions,whereas during the light pollution period they were transported from the southeast(Taizhou)and the North China Plain.展开更多
[Objectives]To analyze the main chemical components in Cocculus laurifolius DC.by ultra-high performance liquid chromatography-quaternary rod/electrostatic field orbital hydrazine high resolution mass spectrometry.[Me...[Objectives]To analyze the main chemical components in Cocculus laurifolius DC.by ultra-high performance liquid chromatography-quaternary rod/electrostatic field orbital hydrazine high resolution mass spectrometry.[Methods]Using Welch AQ-C 18 chromatographic column(150 mm×2.1 mm,1.8μm),gradient elution was performed with 0.1%formic acid aqueous solution(A)-methanol(B)as the mobile phase,and electrospray ESI ionization source and simultaneous mass spectrometry scanning mode of positive and negative ions were used.[Results]26 kinds of chemical component were identified or inferred,including 3 organic acids,5 flavonoids,4 alkaloids,1 coumarin and 13 others.[Conclusions]The UPLC-Q-Exactive HRMS technique is simple,which lays a foundation for the drug-efficacy material basis and medicinal quality evaluation of C.laurifolius DC.展开更多
[Objectives]To analyze the differences in medicinal component contents of Magnolia officinalis across different altitude gradients and explore their causes.[Methods]In this experiment,M.officinalis trees aged 15-20 ye...[Objectives]To analyze the differences in medicinal component contents of Magnolia officinalis across different altitude gradients and explore their causes.[Methods]In this experiment,M.officinalis trees aged 15-20 years growing at four altitudes(1301,1444,1573,and 1643 m)were selected as experimental materials.Leaf traits,soil physicochemical properties,and medicinal component contents were investigated,and the relationships among leaf traits,soil physicochemical properties,and medicinal components were analyzed.[Results]With increasing altitude,the specific leaf area(SLA)of M.officinalis significantly increased,while stomatal density,vein density,leaf thickness,and mesophyll tissue thickness decreased.Soil total nitrogen(TN),total phosphorus(TP),total potassium(TK),available nitrogen(AN),and organic matter contents(OM)decreased significantly with altitude,whereas available potassium(AK)showed the opposite trend.The contents of medicinal components magnolol and honokiol in M.officinalis also significantly decreased with altitude.Correlation analysis revealed that,in addition to altitude,soil physicochemical properties(pH,TP,OM)and leaf traits(leaf thickness,palisade tissue thickness,SLA)were significantly correlated with magnolol and honokiol contents.[Conclusions]M.officinalis at lower altitudes exhibited better growth and higher magnolol and honokiol contents,which may be attributed to higher soil nutrient availability in low-altitude regions.This study provides guidance for selecting cultivation sites and optimizing planting patterns for M.officinalis.展开更多
The specific and cumulative effects of fine particulate matter(PM_(2.5))components on hypertension remain less defined,notably in susceptible older adults.This national study utilized a representative sample of 220,42...The specific and cumulative effects of fine particulate matter(PM_(2.5))components on hypertension remain less defined,notably in susceptible older adults.This national study utilized a representative sample of 220,425 older adults in China,to scrutinize their relationship.Residential PM_(2.5)and five chemical components(black carbon(BC),organic matter(OM),sulphate(SO_(4)^(2−)),nitrate(NO_(3)^(−)),and ammonium(NH_(4)^(+)))were estimated by the bilinear interpolation.Associations between PM_(2.5)and five chemical components with hypertension were examined through two-stage logistic regression models,with population attributable fractions(PAFs)determined via counterfactual analysis.Elevated exposure to PM_(2.5)and its components was generally linked to higher hypertension prevalence.With each interquartile range increase,the odds ratio(OR)of hypertension rose by 1.09(95%CI:1.08–1.11)for NO_(3)^(−),1.06(95%CI:1.05–1.08)for NH_(4)^(+),1.06(95%CI:1.05–1.07)for OM,1.05(95%CI:1.04–1.06)for BC,and 1.06(95%CI:1.04–1.07)for SO42-.Notably,the cumulative impact of five PM_(2.5)chemical components(OR:1.13,95%CI:1.12–1.13)was significantly greater than the effect of total PM_(2.5)mass alone(OR:1.04,95%CI:1.03–1.05).Regarding PAFs,NO_(3)^(−)represented the strongest contribution to hypertension,followed by OM,NH_(4)^(+),SO_(4)^(2−),and BC.Furthermore,the effects were accentuated in low socio-economic population.These findings underline that using total PM_(2.5)as a surrogate marker may underestimate the comprehensive impact of its chemical components,underscoring the necessity for targeted interventions to reduce emissions of specific PM_(2.5)chemical constituents.展开更多
As core components of precision-guided projectiles,projectile-borne components are highly susceptible to failure or even damage in complex high-overload environments,thereby significantly compromising launch reliabili...As core components of precision-guided projectiles,projectile-borne components are highly susceptible to failure or even damage in complex high-overload environments,thereby significantly compromising launch reliability and safety.However,accurately characterizing the mechanical behavior of propellants remains challenging due to the limitations in the current internal ballistic theory and the constraints of large-scale artillery firing experiments.This complicates the high-precision numerical modeling of projectile launch,and obstructs investigations into the failure mechanisms of projectile-borne components.Therefore,this paper identifies propellant parameters using the computational inverse method under uncertainty,further establishes high-precision numerical models of projectile launch,and explores the failure mechanisms of projectile-borne components in complex high-overload environments.First,a projectile launching experiment is meticulously designed and executed to obtain the breech pressure and muzzle velocity.Then,a general simulation model is built,and the powder burn model is used to simulate the ignition and combustion.Subsequently,the propellant parameters are effectively identified with the computational inverse method by the combination of the experiments and simulations.A high-precision numerical model of projectile launch is modified with the parameters validated by another experiment,and the high-overload characteristics during projectile launch are thoroughly analyzed based on this model.Finally,the high-overload characteristics of projectile-borne components are analyzed to elucidate the stress variation laws and to reveal the failure mechanisms influenced by time and spatial locations.This research provides an effective method for perfectly identifying propellant parameters and building high-precision numerical models of projectile launch.Additionally,it provides significant guidance for the anti-high overload design and analysis of projectile-borne components.展开更多
Bitter tea is a special kind of tea germplasm in China.The major biochemical components of 24 bitter teas and other 8 Camellia sinensis var.sinensis and 8 C.sinensis var.assamica tea germplasms,which were stored in th...Bitter tea is a special kind of tea germplasm in China.The major biochemical components of 24 bitter teas and other 8 Camellia sinensis var.sinensis and 8 C.sinensis var.assamica tea germplasms,which were stored in the China National Germplasm Hangzhou Tea Repository(CNGHTR),were analyzed and evaluated.The results showed that no significant differences of major biochemical components affecting the tea quality were found between bitter tea and common tea.According to the processing suitability index,bitter tea was suitable for the manufacturing of black tea;while according to evolutionary indices such as the composition and content of catechin,bitter tea was similar to C.sinensis var.assamica belonging to the relatively primitive type in evolution.The results of cluster analysis indicated that bitter tea was clustered with C.sinensis var.assamica,so it could be considered to belong to C.sinensis var.assamica.展开更多
[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experim...[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.展开更多
[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal ...[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.展开更多
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.
基金Supported by the Central Public-interest Scientific Institution Basal Research Fund,YSFRI,CAFS(No.20603022024016)the Central Public-interest Scientific Institution Basal Research Fund,CAFS(Nos.2023TD52,2023TD76)the earmarked fund for CARS(No.CARS-49)。
文摘Crassostrea gigas has good taste and high nutritional value;however,there are few assessments of comprehensive and panoramic analyses of the nutritional quality of the northern oyster.To study the nutritional characteristics of C.gigas from different sources(ploidy,region,size,and culture mode),C.gigas from various ploidy(diploid and triploid),regions(Rushan,Off-site fattening,and Rongcheng),sizes(small,medium,and large)and culture modes(nearshore and offshore)were selected for comparative analyses.The nutritional components(moisture,protein,fat,and mineral),flavor substances(taste amino acids,nucleotides,and succinic acid),and functional indices(eicosapentaenoic acid(EPA),docosahexaenoic acid(DHA),and taurine)of C.gigas were determined.Principal component analysis(PCA)was used to comprehensively evaluate the oysters and investigate the variations in nutritional quality.The PCA results indicate that protein,essential fatty acids,selenium,zinc,taste amino acids,taurine,EPA,and DHA were core components contributing to 82.25%of the cumulative variance,providing a more comprehensive reflection of the nutrient composition of C.gigas.The extensive quality rankings for the C.gigas were as follows:diploid>triploid,Rushan>fattening>Rongcheng,medium>large>small,and offshore>nearshore.The score rank revealed that diploid oysters of medium-size from Rushan demonstrated superior nutritional quality compared to other tested samples.This is the first comprehensive and systematic investigation of C.gigas in northern China to reveal the feature of nutrients,flavor,and functional components.The study provided data support for the culture,consumption,processing,research,and nutritional quality improvement of oyster industry.
基金Supported by National Natural Science Foundation of China(U24B6018,22178243)。
文摘Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.
基金funding from the National Natural Science Foundation of China (Grant No.42277175)the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056)the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).
文摘Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.
文摘Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.
文摘Groundwater is a crucial water source for urban areas in Africa, particularly where surface water is insufficient to meet demand. This study analyses the water quality of five shallow wells (WW1-WW5) in Half-London Ward, Tunduma Town, Tanzania, using Principal Component Analysis (PCA) to identify the primary factors influencing groundwater contamination. Monthly samples were collected over 12 months and analysed for physical, chemical, and biological parameters. The PCA revealed between four and six principal components (PCs) for each well, explaining between 84.61% and 92.55% of the total variance in water quality data. In WW1, five PCs captured 87.53% of the variability, with PC1 (33.05%) dominated by pH, EC, TDS, and microbial contamination, suggesting significant influences from surface runoff and pit latrines. In WW2, six PCs explained 92.55% of the variance, with PC1 (36.17%) highlighting the effects of salinity, TDS, and agricultural runoff. WW3 had four PCs explaining 84.61% of the variance, with PC1 (39.63%) showing high contributions from pH, hardness, and salinity, indicating geological influences and contamination from human activities. Similarly, in WW4, six PCs explained 90.83% of the variance, where PC1 (43.53%) revealed contamination from pit latrines and fertilizers. WW5 also had six PCs, accounting for 92.51% of the variance, with PC1 (42.73%) indicating significant contamination from agricultural runoff and pit latrines. The study concludes that groundwater quality in Half-London Ward is primarily affected by a combination of surface runoff, pit latrine contamination, agricultural inputs, and geological factors. The presence of microbial contaminants and elevated nitrate and phosphate levels underscores the need for improved sanitation and sustainable agricultural practices. Recommendations include strengthening sanitation infrastructure, promoting responsible farming techniques, and implementing regular groundwater monitoring to safeguard water resources and public health in the region.
基金supported by the Natural Science Foundation of China(Grant Nos.42374032,42174103,42004073)Provincial Natural Science Foundation(2024JJ8348)the Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region,Ministry of Natural Resources(NRMSSHR2023Y01)。
文摘The Global Navigation Satellite System(GNSS)is vital for monitoring terrestrial water storage(TWS).However,effectively extracting hydrological load deformation from GNSS observations poses a significant challenge.This study proposes a novel strategy;the seasonal hydrological load signals are removed from the raw data,and the remaining signals use principal component analysis(PCA).Simulation results from Yunnan Province demonstrate that the spatial distribution of the root mean square error(RMSE)is improved by approximately 15% compared with traditional PCA extraction from raw data.From January 2013 to December 2022,TWS was inverted from 24 GNSS stations in Yunnan Province.The spatial distribution and time series of TWS inverted from GNSS align well with those TWS inferred from the Gravity Recovery and Climate Experiment(GRACE),GRACE Follow-On(GFO),and the Global Land Data Assimilation System(GLDAS)land surface model.However,the amplitude of the GNSS-inverted TWS is slightly higher.Since GNSS ground stations are more sensitive to hydrological load signals,they show correlations with precipitation data that are 8.6%and 6.0%higher than those of GRACE and GLDAS,respectively.In the power spectral density analysis of GRACE/GFO,GLDAS,and GNSS,the signal strength of GNSS is much higher than that of GRACE/GFO and GLDAS in the June and February cycles.These findings suggest that the new data extraction strategy can capture higher frequency hydrological signals in TWS,and GNSS observations can help address limitations in GRACE/GFO observations.This study demonstrates the potential of GNSS TWS in capturing higher-frequency hydrological signals and climate extremes application.
基金The financial support received from the Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173207022,WDZC20200821141349001)Shenzhen Bay Laboratory Startup Fund(21310041,S234602003)。
文摘Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.
基金supported by the National Natural Science Foundation of China(Grant Numbers 42374195 and 42188101)the fellowship of China National Postdoctoral Program for Innovative Talents(Grant Number BX20230273)+1 种基金the Hubei Provincial Natural Science Foundation of China(Grant Number 2024AFB-097)the Postdoctor Project of Hubei Province(Grant Number 2024HBBHCXA054).
文摘This study employs Principal Component Analysis(PCA)and 13 years of SD-WACCM-X model data(2007-2019)to investigate the characteristics and mechanisms of Inter-hemispheric Coupling(IHC)triggered by sudden stratospheric warming(SSW)events.IHC in both hemispheres leads to a cold anomaly in the equatorial stratosphere,a warm anomaly in the equatorial mesosphere,and increased temperatures in the mesosphere and lower thermosphere(MLT)region of the summer hemisphere.However,the IHC features during boreal winter period are significantly weaker than during the austral winter period,primarily due to weaker stationary planetary wave activity in the Southern Hemisphere(SH).During the austral winter period,IHC results in a warm anomaly in the polar mesosphere of the SH,which does not occur in the NH during boreal winter period.This study also examines the possible influence of quasi-two-day waves(QTDWs)on IHC.We found that the largest temperature anomaly in the summer polar MLT region is associated with a large wind instability area,and a well-developed critical layer structure of QTDW in January.In contrast,during July,despite favorable conditions for QTDW propagation in the Northern Hemisphere,weaker IHC response is observed,suggesting that IHC features and the relationship with QTDWs during July would be more complex than during January.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104075 and 12347101).
文摘We study the influence of disorder on the Moore–Read state by principal component analysis(PCA),which is one of the ground state candidates for the 5/2 fractional Hall state.By using PCA,the topological features of the ground state wave functions with different disorder strengths can be distilled.As the disorder strength increases,the Moore–Read state will be destroyed.We explore the phase transition by analyzing the overlaps between the random sample wave functions and the topologically distilled state.The cross-point between the amplitudes of the principal component and its counterpart is the phase transition point.Additionally,the origin of the second component comes from the excited states,which is different from the Laughlin state.
基金supported by the National Key Research and Development Plan of China(No.2023YFB3406500)the National Natural Science Foundation of China(No.52475132)+2 种基金the Aeronautical Science Foundation of China(No.20200015053001)the Shaanxi Key Research Program Project,China(No.2024GX-ZDCYL-01–16)the Xi’an Key Industrial Chain Technology Research Project,China(No.2023JH-RGZNGG-0033)。
文摘Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.
基金funded by the Master,PhD Scholarship Programme of Vingroup Innovation Foundation(VINIF),No.VINIF.2024.TS.067.
文摘Breast cancer,which is the most commonly diagnosed cancers among women,is a notable health issues globally.Breast cancer is a result of abnormal cells in the breast tissue growing out of control.Histopathology,which refers to the detection and learning of tissue diseases,has appeared as a solution for breast cancer treatment as it plays a vital role in its diagnosis and classification.Thus,considerable research on histopathology in medical and computer science has been conducted to develop an effective method for breast cancer treatment.In this study,a vision Transformer(ViT)was employed to classify tumors into two classes,benign and malignant,in the Breast Cancer Histopathological Database(BreakHis).To enhance the model performance,we introduced the novel multi-head locality large kernel self-attention during fine-tuning,achieving an accuracy of 95.94%at 100×magnification,thereby improving the accuracy by 3.34%compared to a standard ViT(which uses multi-head self-attention).In addition,the application of principal component analysis for dimensionality reduction led to an accuracy improvement of 3.34%,highlighting its role in mitigating overfitting and reducing the computational complexity.In the final phase,SHapley Additive exPlanations,Local Interpretable Model-agnostic Explanations,and Gradient-weighted Class Activation Mapping were used for the interpretability and explainability of machine-learning models,aiding in understanding the feature importance and local explanations,and visualizing the model attention.In another experiment,ensemble learning with VGGIN further boosted the performance to 97.13%accuracy.Our approach exhibited a 0.98%to 17.13%improvement in accuracy compared with state-of-the-art methods,establishing a new benchmark for breast cancer histopathological image classification.
基金supported by the National Key Research and Development Program of China(No.2022YFC3701204)the Natural Science Foundation of Jiangsu Province(No.BK20231300).
文摘Based on the chemical composition data of a regional long-lasting haze event that occurred in the Yangtze River Delta(YRD)region from 17 December 2023 to 8 January 2024,the evolutionary characteristics of the chemical components and sources of fine particulate matter(PM2.5)under different pollution levels were comparatively analyzed using PMF(Positive Matrix Factorization)and backward trajectory analysis.SNA(NO_(3)^(-),NH_(4)^(+),SO_(4)^(2-))was found to be the primary chemical component of PM2.5,making up 63.6%(clean days)to 69.7%(heavy pollution)of it.The NO_(3)^(-)concentration was 3.14(clean days)to 6.01(heavy pollution)times higher than that of SO_(4)^(2-).NO_(3)^(-),POC,Fe,Mn,Al concentrations increased,while SOC,EC,crustal elements(Ca,Si)and other water-soluble ions(WSIs)concentrations decreased as the pollution level increased.The contribution of secondary inorganics and biomass-burning emissions and industrial and ship emissions increased significantly as the pollution level increased,which accounted for 40.3%and 36.7%,respectively,in the heavy pollution stage.The contribution of traffic sources decreases gradually with increasing pollution levels,accounting for only 59.1%of the light pollution stage in the heavy pollution stage.PM_(2.5) and its main chemical components showed similar potential source distribution,located in the northwest(Fuyang,Huainan,Nanjing),south(Taizhou,Lishui,Jiande)and north(Taizhou,Yancheng).However,distinct transport routes were observed under the different air quality levels.During the heavy pollution period,the polluted air masses primarily came from the harbor regions,whereas during the light pollution period they were transported from the southeast(Taizhou)and the North China Plain.
基金Supported by Scientific Research Project of China Medical Association of Minorities(2022M2038-310401)Guangxi First-class Discipline Project for Traditional Chinese Medicine(GuiJiaoKeYan 202201)+3 种基金Scientific Research and Training Project for College Students of Guangxi University of Chinese Medicine(2023DXS14)Funding Project for High-level Innovation Team and Outstanding Scholars in Guangxi Universities(GuiJiaoRen 201407)NATCM s Project of High-level Construction of Key TCM Disciplines/Medicine for Ethnic Minorities(Zhuang Medicine)(ZYYZDXK-2023164)Guangxi Higher Education Key Laboratory for the Research of Toxic Diseases in Zhuang Medicine(GuiJiaoKeYan 202210).
文摘[Objectives]To analyze the main chemical components in Cocculus laurifolius DC.by ultra-high performance liquid chromatography-quaternary rod/electrostatic field orbital hydrazine high resolution mass spectrometry.[Methods]Using Welch AQ-C 18 chromatographic column(150 mm×2.1 mm,1.8μm),gradient elution was performed with 0.1%formic acid aqueous solution(A)-methanol(B)as the mobile phase,and electrospray ESI ionization source and simultaneous mass spectrometry scanning mode of positive and negative ions were used.[Results]26 kinds of chemical component were identified or inferred,including 3 organic acids,5 flavonoids,4 alkaloids,1 coumarin and 13 others.[Conclusions]The UPLC-Q-Exactive HRMS technique is simple,which lays a foundation for the drug-efficacy material basis and medicinal quality evaluation of C.laurifolius DC.
基金Supported by National Key Research and Development Program of China in 2018(2018YFC1708005)the Southwest Minzu University Research Startup Funds(RQD2023020).
文摘[Objectives]To analyze the differences in medicinal component contents of Magnolia officinalis across different altitude gradients and explore their causes.[Methods]In this experiment,M.officinalis trees aged 15-20 years growing at four altitudes(1301,1444,1573,and 1643 m)were selected as experimental materials.Leaf traits,soil physicochemical properties,and medicinal component contents were investigated,and the relationships among leaf traits,soil physicochemical properties,and medicinal components were analyzed.[Results]With increasing altitude,the specific leaf area(SLA)of M.officinalis significantly increased,while stomatal density,vein density,leaf thickness,and mesophyll tissue thickness decreased.Soil total nitrogen(TN),total phosphorus(TP),total potassium(TK),available nitrogen(AN),and organic matter contents(OM)decreased significantly with altitude,whereas available potassium(AK)showed the opposite trend.The contents of medicinal components magnolol and honokiol in M.officinalis also significantly decreased with altitude.Correlation analysis revealed that,in addition to altitude,soil physicochemical properties(pH,TP,OM)and leaf traits(leaf thickness,palisade tissue thickness,SLA)were significantly correlated with magnolol and honokiol contents.[Conclusions]M.officinalis at lower altitudes exhibited better growth and higher magnolol and honokiol contents,which may be attributed to higher soil nutrient availability in low-altitude regions.This study provides guidance for selecting cultivation sites and optimizing planting patterns for M.officinalis.
基金supported by the National Key Research and Development Program of China(No.2022YFC3600800)the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-RC330–01)+1 种基金the Special Research Fund for Central Universities,Peking Union Medical College(No.3332023087)the Population and Aging Health Science Program(No.WH10022023035).
文摘The specific and cumulative effects of fine particulate matter(PM_(2.5))components on hypertension remain less defined,notably in susceptible older adults.This national study utilized a representative sample of 220,425 older adults in China,to scrutinize their relationship.Residential PM_(2.5)and five chemical components(black carbon(BC),organic matter(OM),sulphate(SO_(4)^(2−)),nitrate(NO_(3)^(−)),and ammonium(NH_(4)^(+)))were estimated by the bilinear interpolation.Associations between PM_(2.5)and five chemical components with hypertension were examined through two-stage logistic regression models,with population attributable fractions(PAFs)determined via counterfactual analysis.Elevated exposure to PM_(2.5)and its components was generally linked to higher hypertension prevalence.With each interquartile range increase,the odds ratio(OR)of hypertension rose by 1.09(95%CI:1.08–1.11)for NO_(3)^(−),1.06(95%CI:1.05–1.08)for NH_(4)^(+),1.06(95%CI:1.05–1.07)for OM,1.05(95%CI:1.04–1.06)for BC,and 1.06(95%CI:1.04–1.07)for SO42-.Notably,the cumulative impact of five PM_(2.5)chemical components(OR:1.13,95%CI:1.12–1.13)was significantly greater than the effect of total PM_(2.5)mass alone(OR:1.04,95%CI:1.03–1.05).Regarding PAFs,NO_(3)^(−)represented the strongest contribution to hypertension,followed by OM,NH_(4)^(+),SO_(4)^(2−),and BC.Furthermore,the effects were accentuated in low socio-economic population.These findings underline that using total PM_(2.5)as a surrogate marker may underestimate the comprehensive impact of its chemical components,underscoring the necessity for targeted interventions to reduce emissions of specific PM_(2.5)chemical constituents.
基金Project supported by the National Natural Science Foundation of China (No. 12302435)。
文摘As core components of precision-guided projectiles,projectile-borne components are highly susceptible to failure or even damage in complex high-overload environments,thereby significantly compromising launch reliability and safety.However,accurately characterizing the mechanical behavior of propellants remains challenging due to the limitations in the current internal ballistic theory and the constraints of large-scale artillery firing experiments.This complicates the high-precision numerical modeling of projectile launch,and obstructs investigations into the failure mechanisms of projectile-borne components.Therefore,this paper identifies propellant parameters using the computational inverse method under uncertainty,further establishes high-precision numerical models of projectile launch,and explores the failure mechanisms of projectile-borne components in complex high-overload environments.First,a projectile launching experiment is meticulously designed and executed to obtain the breech pressure and muzzle velocity.Then,a general simulation model is built,and the powder burn model is used to simulate the ignition and combustion.Subsequently,the propellant parameters are effectively identified with the computational inverse method by the combination of the experiments and simulations.A high-precision numerical model of projectile launch is modified with the parameters validated by another experiment,and the high-overload characteristics during projectile launch are thoroughly analyzed based on this model.Finally,the high-overload characteristics of projectile-borne components are analyzed to elucidate the stress variation laws and to reveal the failure mechanisms influenced by time and spatial locations.This research provides an effective method for perfectly identifying propellant parameters and building high-precision numerical models of projectile launch.Additionally,it provides significant guidance for the anti-high overload design and analysis of projectile-borne components.
基金Supported by the"Study on High Efficiency Machining and Multiple Utilization Technology of Tea Germplasm Resource"of National Science&Technology Supporting Project(2006BAD06B01)"Data Standard of Perennial and Vegetative Propagation Crop Germplasm Resources as a Share Experimental Unit"of National Fundamental Resources Platform of Science&Technology Project(2005DKA21002-08)~~
文摘Bitter tea is a special kind of tea germplasm in China.The major biochemical components of 24 bitter teas and other 8 Camellia sinensis var.sinensis and 8 C.sinensis var.assamica tea germplasms,which were stored in the China National Germplasm Hangzhou Tea Repository(CNGHTR),were analyzed and evaluated.The results showed that no significant differences of major biochemical components affecting the tea quality were found between bitter tea and common tea.According to the processing suitability index,bitter tea was suitable for the manufacturing of black tea;while according to evolutionary indices such as the composition and content of catechin,bitter tea was similar to C.sinensis var.assamica belonging to the relatively primitive type in evolution.The results of cluster analysis indicated that bitter tea was clustered with C.sinensis var.assamica,so it could be considered to belong to C.sinensis var.assamica.
文摘[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.
基金Supported by the Fund of Anhui Provincial Tobacco Monopoly Bureau(AHKJ2008-03)Anhui Provincial University Key Project of Natural Science(KJ2010A114)Undergraduate Student Science and Technology Innovation Fund of Anhui Agricultural University(2010233)~~
文摘[Objective] This study aimed to explore the related mechanisms of the breaking of flue-cured tobacco leaves. [Method] Anti-breaking models of the main veins of flue-cured tobacco leaves were constructed for principal component analysis on the anti-breaking index, leaf traits and cellulose contents. [Result] The results showed that the growth traits had certain relevance with the cellulose contents while the leaf weight assumed a significant negative correlation with the anti-breaking index, indicating that the heavier the leaf weight was, the weaker the anti-breaking capacity of flue-cured tobacco would be; the cross-sectional area of main veins and the cellulose contents had shown a positive correlation with the anti-breaking index, indicating that the thicker the main vein of flue-cured tobacco was, the higher the cellulose contents would be, and the stronger the anti-breaking capacity of flue-cured tobacco leaves would be. [Conclusion] This study provided theoretical basis and reference to improve tobacco production and enhance the quality of flue-cured tobacco.
