The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.Howev...The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.However,few studies have quantified the effects of these factors on the main quality components of tea.The study aimed to explore the interactions of C,N,and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method.The results indicated that(1)The contents of C,N,and P in soil,microorganisms,and tea plants were highly correlated and collinear,and showed significant correlations with the main quality components of tea.(2)Optimal regression equations were established to esti-mate tea polyphenol,amino acid,catechin,caffeine,and water extract content based on C,N,and P contents in soil,microorganisms,and tea plants(R^(2)=0.923,0.726,0.954,0.848,and 0.883,respectively).(3)Pathway analysis showed that microbial biomass phosphorus(MBP),root phosphorus,branch nitrogen,and microbial biomass carbon(MBC)were the largest direct impact factors on tea polyphenol,catechin,water extracts,amino acid,and caffeine content,respectively.Leaf carbon,root phosphorus,and leaf nitrogen were the largest indirect impact factors on tea polyphenol,catechin,and water extract content,respectively.Leaf carbon indirectly affected tea polyphenol content mainly by altering MBP content.Root phosphorus indirectly affected catechin content mainly by altering soil organic carbon content.Leaf nitrogen indirectly affected water extract content mainly by altering branch nitrogen content.The research results provide the scientific basis for reasonable fertilization in tea gardens and tea quality improvement.展开更多
Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and protein.ANOVA revealed significant differences for the ...Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and protein.ANOVA revealed significant differences for the quality traits studied.For every trait under study,the phenotypic coefficient of variation was higher than the correspon-dence genotypic coefficient of variation.Heritability in a broad sense varied from 29.75%for grain length to 98.31%for the elongation trait.Hulling percentage recovery had a highly significant positive correlation with milling and head rice percentage.Consequently,milling percentage had a highly positive correlation with head rice percentage.In amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose content.Mineral nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional elements.Among the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new varieties.There was a positive and significant correlation between magnesium Mg,K,Zn and Fe.Consequently,K had a positive correlation with zinc Zn,Fe,and protein percentage.Clustering analysis was divided into two groups:thefirst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.展开更多
Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear ...Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.展开更多
With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in th...With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.展开更多
Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variati...Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variations observed across environments. This study, based on a comprehensive analysis of 256 peanut varieties, selected nine representative varieties(Huayu23, Yuanza9102, Silihong, Wanhua2, Zhonghua6, Zhonghua16, Zhonghua21,Zhonghua215, Zhonghua24) for cultivation in five distinct ecological zones including Chengdu, Hefei, Nanjing,Shijiazhuang, and Wuhan. The yield and quality related phenotypic traits of these varieties were thoroughly assessed, revealing a complex interplay between genetic and environmental factors. Principal component analysis(PCA) effectively distinguished varieties based on yield and quality traits. Strong correlations were observed between specific traits, such as seed size and quality components. The G × E interaction was evident, as some varieties consistently performed better in certain environments. Varieties with lower coefficient of variation(CV)values exhibited stable trait expression, making them reliable choices for broad cultivation. In contrast, varieties with higher CV values displayed greater sensitivity to environmental fluctuations, potentially due to specific genetic factors. Two high oleic acid varieties, Zhonghua24 and Zhonghua215, demonstrated remarkable stability in oleic acid content across diverse environments, suggesting the presence of genetic mechanisms that buffer against environmental variations. Overall, this study underscores the importance of selecting peanut varieties based on their adaptability and performance in specific ecological zones. These findings provide valuable insights for peanut breeders and farmers, facilitating informed decisions for improved crop production and quality.展开更多
Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and ...Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.展开更多
The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms...The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.展开更多
The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate be...The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate between their upper and lower bounds, where the number of oscillations increases as the Rashba interaction strength increases. The exchanging rate of these three quantities depends on the Rashba strength, and whether the entangled state is generated via direct/indirect interaction. Moreover, the coherence parameter can be used as a control parameter to maximize or minimize the three physical quantities.展开更多
The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study del...The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.展开更多
Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key de...Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.展开更多
Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to ...Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to pipeline-riser flow needs evaluation since the flow condition in pipeline-riser is quite different from the original data where they were derived from. In the present study, a comprehensive evaluation of 24prevailing correlation in predicting frictional pressure drop is carried out based on experimentally measured data of air-water and air-oil two-phase flows in pipeline-riser. Experiments are performed in a system having different configuration of pipeline-riser with the inclination of the downcomer varied from-2°to-5°to investigated the effect of the elbow on the frictional pressure drop in the riser. The inlet gas velocity ranges from 0.03 to 6.2 m/s, and liquid velocity varies from 0.02 to 1.3 m/s. A total of885 experimental data points including 782 on air-water flows and 103 on air-oil flows are obtained and used to access the prediction ability of the correlations. Comparison of the predicted results with the measured data indicate that a majority of the investigated correlations under-predict the pressure drop on severe slugging. The result of this study highlights the requirement of new method considering the effect of pipe layout on the frictional pressure drop.展开更多
Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to...Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to differentiate between quantum entanglement and quantum correlation.Nonetheless,this indistinguishability is no longer holds for mixed states.To contribute to a better understanding of this differentiation,we have explored a simple model for both generating and measuring these quantum correlations.Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities,coupled through the photon hopping process.this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes.The key ingredient in analyzing quantum correlation in this system is the global covariance matrix.It forms the basis for computing two essential metrics:the logarithmic negativity(E_(N)^(m))and the Gaussian interferometric power(P_(G)^(m)).These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations,respectively.Our study reveals that the Gaussian interferometric power(P_(G)^(m))proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system,particularly in scenarios featuring resilient photon hopping.展开更多
When evaluating an area's seismic risk or resilience,it is necessary to use the spatial correlation to analyze the ground motion parameters of multiple sites together in an earthquake.These two large earthquakes i...When evaluating an area's seismic risk or resilience,it is necessary to use the spatial correlation to analyze the ground motion parameters of multiple sites together in an earthquake.These two large earthquakes in Türkiye provided the possibility for spatial correlation analysis of ground motion intensity measurements in this area.Based on the strong motion records provided by The Disaster and Emergency Management Authority of Türkiye(AFAD),this study uses the local ground motion prediction equation in Türkiye to give spatial correlation analysis of Intensity Measurements.This study gives an exponential model based on a semivariogram and compares it with the correlation model obtained from previous studies.展开更多
Background: Cardiovascular diseases, such as hypertension and coronary heart disease, are often accompanied by thyroid and mental diseases, the harm of which poses great threats to patients’ health. Objective: To exp...Background: Cardiovascular diseases, such as hypertension and coronary heart disease, are often accompanied by thyroid and mental diseases, the harm of which poses great threats to patients’ health. Objective: To explore the correlation between free triiodothyronine (FT3), free thyroxine (FT4) and hypertension in depression patients with hypothyroidism and its clinical guiding value. Methods: A total of 548 patients diagnosed with hypothyroidism in Wuxue First People’s Hospital of Hubei Province from January 2018 to September 2022 were enrolled. According to whether complicated with depression, they were divided into hypothyroidism without depression group (group A) and hypothyroidism with depression group (group B). The gender, age, comorbidities (such as depression, hypertension, diabetes, dyslipidemia, acute myocardial infarction), FT3, FT4, and thyroid stimulating hormone (TSH) levels were recorded. Spearman rank correlation was used to analyze hypertensive patients with hypothyroidism. Multivariate binary Logistic regression was used to analyze the influencing factors of hypertension in patients with hypothyroidism. Results: The TSH level, the number of hypertension, coronary heart disease and hyperlipidemia in group B were statistically significantly higher than those in group A (P 3 level in group B was statistically significantly lower than that in group A (P s = 0.092), coronary heart disease (rs = 0.000), hyperlipidemia (rs = 0.000), diabetes (rs = 0.000), and age (rs = 0.000), and negatively correlated with FT3 (rs = 0.000) (P 3 and FT4 were the influencing factors of hypertension. The risk of hypertension in patients with coronary heart disease and hyperlipidemia significantly increased by 3.425 and 1.761 times (P 3, the risk of hypertension increased (P 4, the risk of hypertension significantly increased (P 3 and FT4 are the influencing factors of hypertension. The lower the FT3 level, the higher the FT4 level, the higher the risk of hypertension. FT3 and FT4 may be potential biomarkers of depression in hypertensive patients. Thyroid function assessment is recommended in patients with hypertension.展开更多
Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consi...Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.展开更多
With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The networ...With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.展开更多
Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal cha...Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.展开更多
AIM:To assess the repeatability,interocular correlation,and agreement of quantitative swept-source optical coherence tomography angiography(OCTA)optic nerve head(ONH)parameters in healthy subjects.METHODS:Thir ty-thre...AIM:To assess the repeatability,interocular correlation,and agreement of quantitative swept-source optical coherence tomography angiography(OCTA)optic nerve head(ONH)parameters in healthy subjects.METHODS:Thir ty-three healthy subjects were enrolled.The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm×3 mm scanning protocol.Images of the radial peripapillary capillary were analyzed by a customized Matlab program,and the vessel density,fractal dimension,and vessel diameter index were measured.The repeatability of the four scans was determined by the intraclass correlation coefficient(ICC).The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient,ICC and Bland-Altman plots.RESULTS:All swept-source-OCTA ONH parameters exhibited certain repeatability,with ICC>0.760 and coefficient of variation(CoV)≤7.301%.The obvious interocular correlation was observed for papillary vessel density(ICC=0.857),vessel diameter index(ICC=0.857)and fractal dimension(ICC=0.906),while circumpapillary vessel density exhibited moderate interocular correlation(ICC=0.687).Bland-Altman plots revealed an agreement range of-5.26%to 6.21%for circumpapillary vessel density.CONCLUSION:OCTA ONH parameters demonstrate good repeatability in healthy subjects.The interocular correlations of papillary vessel density,fractal dimension and vessel diameter index are high,but the correlation for circumpapillary vessel density is moderate.展开更多
BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturien...BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturient women play a key role in communication and emotional support.This study explores the PPD support relationship with partners and its influencing factors,which is believed to establish psychological well-being and improve maternal partner support.AIM To explore the correlation between PPD and partner support during breastfeeding and its influencing factors.METHODS Convenience sampling was used to select lactating women(200 women)who underwent postpartum examinations at the Huzhou Maternity and Child Health Care Hospital from July 2022 to December 2022.A cross-sectional survey was conducted on the basic information(general information questionnaire),depression level[edinburgh postnatal depression scale(EPDS)],and partner support score[dyadic coping inventory(DCI)]of the selected subjects.Pearson’s correlation analysis was used to analyze the correlation between PPD and DCI in lactating women.Factors affecting PPD levels during lactation were analyzed using multiple linear regression.RESULTS The total average score of EPDS in 200 lactating women was(9.52±1.53),and the total average score of DCI was(115.78±14.90).Dividing the EPDS,the dimension scores were:emotional loss(1.91±0.52),anxiety(3.84±1.05),and depression(3.76±0.96).Each dimension of the DCI was subdivided into:Pressure communication(26.79±6.71),mutual support(39.76±9.63),negative support(24.97±6.68),agent support(6.87±1.92),and joint support(17.39±4.19).Pearson’s correlation analysis demonstrated that the total mean score and individual dimension scores of EPDS during breastfeeding were inversely correlated with the total score of partner support,stress communication,mutual support,and cosupport(P<0.05).The total mean score of the EPDS and its dimensions were positively correlated with negative support(P<0.05).Multiple linear regression analysis showed that the main factors affecting PPD during breastfeeding were marital harmony,newborn health,stress communication,mutual support,negative support,cosupport,and the total score of partner support(P<0.05).CONCLUSION PPD during breastfeeding was associated with marital harmony,newborn health,stress communication,mutual support,negative support,joint support,and the total DCI score.展开更多
Beam splitting is one of the main approaches to achieving x-ray ghost imaging, and the intensity correlation between diffraction beam and transmission beam will directly affect the imaging quality. In this paper, we i...Beam splitting is one of the main approaches to achieving x-ray ghost imaging, and the intensity correlation between diffraction beam and transmission beam will directly affect the imaging quality. In this paper, we investigate the intensity correlation between the split x-ray beams by Laue diffraction of stress-free crystal. The analysis based on the dynamical theory of x-ray diffraction indicates that the spatial resolution of diffraction image and transmission image are reduced due to the position shift of the exit beam. In the experimental setup, a stress-free crystal with a thickness of hundredmicrometers-level is used for beam splitting. The crystal is in a non-dispersive configuration equipped with a double-crystal monochromator to ensure that the dimension of the diffraction beam and transmission beam are consistent. A correlation coefficient of 0.92 is achieved experimentally and the high signal-to-noise ratio of the x-ray ghost imaging is anticipated.Results of this paper demonstrate that the developed beam splitter of Laue crystal has the potential in the efficient data acquisition of x-ray ghost imaging.展开更多
基金This work was supported by Guizhou Provincial Basic Research Program(Natural Science),Grant Number Qiankehejichu-ZK[2021]YB133Guizhou Provincial Scientific and Technological Program,Grant Number Qiankehehoubuzhu[2020]3001National Natural Science Foundation of China-Guizhou Provincial People’s Government Karst Science Research Centre(U1612442).
文摘The contents of carbon(C),nitrogen(N),and phosphorus(P)in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea,such as tea polyphenols,amino acids,and caffeine.However,few studies have quantified the effects of these factors on the main quality components of tea.The study aimed to explore the interactions of C,N,and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method.The results indicated that(1)The contents of C,N,and P in soil,microorganisms,and tea plants were highly correlated and collinear,and showed significant correlations with the main quality components of tea.(2)Optimal regression equations were established to esti-mate tea polyphenol,amino acid,catechin,caffeine,and water extract content based on C,N,and P contents in soil,microorganisms,and tea plants(R^(2)=0.923,0.726,0.954,0.848,and 0.883,respectively).(3)Pathway analysis showed that microbial biomass phosphorus(MBP),root phosphorus,branch nitrogen,and microbial biomass carbon(MBC)were the largest direct impact factors on tea polyphenol,catechin,water extracts,amino acid,and caffeine content,respectively.Leaf carbon,root phosphorus,and leaf nitrogen were the largest indirect impact factors on tea polyphenol,catechin,and water extract content,respectively.Leaf carbon indirectly affected tea polyphenol content mainly by altering MBP content.Root phosphorus indirectly affected catechin content mainly by altering soil organic carbon content.Leaf nitrogen indirectly affected water extract content mainly by altering branch nitrogen content.The research results provide the scientific basis for reasonable fertilization in tea gardens and tea quality improvement.
基金supported and funded by Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,grant number(Grant A410).
文摘Twenty-four rice genotypes were examined to assess genetic variability,heritability,and correlations for seven-grain quality traits,eight nutritional elements,and protein.ANOVA revealed significant differences for the quality traits studied.For every trait under study,the phenotypic coefficient of variation was higher than the correspon-dence genotypic coefficient of variation.Heritability in a broad sense varied from 29.75%for grain length to 98.31%for the elongation trait.Hulling percentage recovery had a highly significant positive correlation with milling and head rice percentage.Consequently,milling percentage had a highly positive correlation with head rice percentage.In amylose percentage,all the genotypes belonged to low amylose except the Hassawi-1 variety,which had intermediate amylose content.Mineral nutrition contents of magnesium(Mg),sodium(Na),potas-sium(K),calcium(Ca),copper(Cu),manganese(Mn),zinc(Zn),iron(Fe),or protein percentage gave different variations for 24 rice genotypes under all the nutritional elements.Among the 24 genotypes,ten rice genotypes–HighNutrient-1,HighNutrient-2,HighNutrient-9,HighNutrient-8,HighNutrient-3,Hassawi-2,HighNutrient-7,HighNutrient-6,Hassawi-1,and HighNutrient-4–had the highest heist value for all nutritional and protein con-tents,and could be used as a donor to improving new varieties.There was a positive and significant correlation between magnesium Mg,K,Zn and Fe.Consequently,K had a positive correlation with zinc Zn,Fe,and protein percentage.Clustering analysis was divided into two groups:thefirst group included all genotypes rich in nutri-ents,while the remaining genotypes with low nutritional content were included in the second group.
基金supported by the Key R&D Project of the Ministry of Science and Technology of China(2020YFB1808005)。
文摘Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.
基金Shanghai Rising-Star Program(Grant No.21QA1403400)Shanghai Sailing Program(Grant No.20YF1414800)Shanghai Key Laboratory of Power Station Automation Technology(Grant No.13DZ2273800).
文摘With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.
基金the National Natural Sciences Foundation of China(32201770)the project of the development for high-quality seed industry of Hubei province(HBZY2023B003)+2 种基金Key Area Research and Development Program of Hubei Province(2021BBA077)the Natural Science Foundation of Hubei Province(22CFB332)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2021-OCRI).
文摘Peanut cultivation in China spans various ecological zones, each with unique environmental conditions. Identifying suitable peanut varieties for these regions has been challenging due to significant phenotypic variations observed across environments. This study, based on a comprehensive analysis of 256 peanut varieties, selected nine representative varieties(Huayu23, Yuanza9102, Silihong, Wanhua2, Zhonghua6, Zhonghua16, Zhonghua21,Zhonghua215, Zhonghua24) for cultivation in five distinct ecological zones including Chengdu, Hefei, Nanjing,Shijiazhuang, and Wuhan. The yield and quality related phenotypic traits of these varieties were thoroughly assessed, revealing a complex interplay between genetic and environmental factors. Principal component analysis(PCA) effectively distinguished varieties based on yield and quality traits. Strong correlations were observed between specific traits, such as seed size and quality components. The G × E interaction was evident, as some varieties consistently performed better in certain environments. Varieties with lower coefficient of variation(CV)values exhibited stable trait expression, making them reliable choices for broad cultivation. In contrast, varieties with higher CV values displayed greater sensitivity to environmental fluctuations, potentially due to specific genetic factors. Two high oleic acid varieties, Zhonghua24 and Zhonghua215, demonstrated remarkable stability in oleic acid content across diverse environments, suggesting the presence of genetic mechanisms that buffer against environmental variations. Overall, this study underscores the importance of selecting peanut varieties based on their adaptability and performance in specific ecological zones. These findings provide valuable insights for peanut breeders and farmers, facilitating informed decisions for improved crop production and quality.
基金funded by the National Natural Science Foundation of China(61991413)the China Postdoctoral Science Foundation(2019M651142)+1 种基金the Natural Science Foundation of Liaoning Province(2021-KF-12-07)the Natural Science Foundations of Liaoning Province(2023-MS-322).
文摘Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.
文摘The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.
文摘The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate between their upper and lower bounds, where the number of oscillations increases as the Rashba interaction strength increases. The exchanging rate of these three quantities depends on the Rashba strength, and whether the entangled state is generated via direct/indirect interaction. Moreover, the coherence parameter can be used as a control parameter to maximize or minimize the three physical quantities.
基金funded by the National Key R&D Program Project(No.2022YFC3103604).
文摘The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.
基金supported by the Hunan Provincial Natrual Science Foundation of China(2022JJ30103)“the 14th Five-Year”Key Disciplines and Application Oriented Special Disciplines of Hunan Province(Xiangjiaotong[2022],351)the Science and Technology Innovation Program of Hunan Province(2016TP1020).
文摘Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.
基金the support of the Opening Fund of State Key Laboratory of Multiphase Flow in Power Engineering(SKLMF-KF-2102)。
文摘Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to pipeline-riser flow needs evaluation since the flow condition in pipeline-riser is quite different from the original data where they were derived from. In the present study, a comprehensive evaluation of 24prevailing correlation in predicting frictional pressure drop is carried out based on experimentally measured data of air-water and air-oil two-phase flows in pipeline-riser. Experiments are performed in a system having different configuration of pipeline-riser with the inclination of the downcomer varied from-2°to-5°to investigated the effect of the elbow on the frictional pressure drop in the riser. The inlet gas velocity ranges from 0.03 to 6.2 m/s, and liquid velocity varies from 0.02 to 1.3 m/s. A total of885 experimental data points including 782 on air-water flows and 103 on air-oil flows are obtained and used to access the prediction ability of the correlations. Comparison of the predicted results with the measured data indicate that a majority of the investigated correlations under-predict the pressure drop on severe slugging. The result of this study highlights the requirement of new method considering the effect of pipe layout on the frictional pressure drop.
文摘Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to differentiate between quantum entanglement and quantum correlation.Nonetheless,this indistinguishability is no longer holds for mixed states.To contribute to a better understanding of this differentiation,we have explored a simple model for both generating and measuring these quantum correlations.Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities,coupled through the photon hopping process.this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes.The key ingredient in analyzing quantum correlation in this system is the global covariance matrix.It forms the basis for computing two essential metrics:the logarithmic negativity(E_(N)^(m))and the Gaussian interferometric power(P_(G)^(m)).These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations,respectively.Our study reveals that the Gaussian interferometric power(P_(G)^(m))proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system,particularly in scenarios featuring resilient photon hopping.
基金jointly supported by the National Natural Science Foundation of China U1901602,U2239252)the National Key R&D Program of China(No.2019YFE0115700)+1 种基金the Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration(Grant No.2021EEEVL0202)the Natural Science Foundation of Heilongjiang Province(LH2020E021)。
文摘When evaluating an area's seismic risk or resilience,it is necessary to use the spatial correlation to analyze the ground motion parameters of multiple sites together in an earthquake.These two large earthquakes in Türkiye provided the possibility for spatial correlation analysis of ground motion intensity measurements in this area.Based on the strong motion records provided by The Disaster and Emergency Management Authority of Türkiye(AFAD),this study uses the local ground motion prediction equation in Türkiye to give spatial correlation analysis of Intensity Measurements.This study gives an exponential model based on a semivariogram and compares it with the correlation model obtained from previous studies.
文摘Background: Cardiovascular diseases, such as hypertension and coronary heart disease, are often accompanied by thyroid and mental diseases, the harm of which poses great threats to patients’ health. Objective: To explore the correlation between free triiodothyronine (FT3), free thyroxine (FT4) and hypertension in depression patients with hypothyroidism and its clinical guiding value. Methods: A total of 548 patients diagnosed with hypothyroidism in Wuxue First People’s Hospital of Hubei Province from January 2018 to September 2022 were enrolled. According to whether complicated with depression, they were divided into hypothyroidism without depression group (group A) and hypothyroidism with depression group (group B). The gender, age, comorbidities (such as depression, hypertension, diabetes, dyslipidemia, acute myocardial infarction), FT3, FT4, and thyroid stimulating hormone (TSH) levels were recorded. Spearman rank correlation was used to analyze hypertensive patients with hypothyroidism. Multivariate binary Logistic regression was used to analyze the influencing factors of hypertension in patients with hypothyroidism. Results: The TSH level, the number of hypertension, coronary heart disease and hyperlipidemia in group B were statistically significantly higher than those in group A (P 3 level in group B was statistically significantly lower than that in group A (P s = 0.092), coronary heart disease (rs = 0.000), hyperlipidemia (rs = 0.000), diabetes (rs = 0.000), and age (rs = 0.000), and negatively correlated with FT3 (rs = 0.000) (P 3 and FT4 were the influencing factors of hypertension. The risk of hypertension in patients with coronary heart disease and hyperlipidemia significantly increased by 3.425 and 1.761 times (P 3, the risk of hypertension increased (P 4, the risk of hypertension significantly increased (P 3 and FT4 are the influencing factors of hypertension. The lower the FT3 level, the higher the FT4 level, the higher the risk of hypertension. FT3 and FT4 may be potential biomarkers of depression in hypertensive patients. Thyroid function assessment is recommended in patients with hypertension.
基金NationalNatural Science Foundation of China,Grant/AwardNumber:61867004National Natural Science Foundation of China Youth Fund,Grant/Award Number:41801288.
文摘Cross-Project Defect Prediction(CPDP)is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project.However,existing CPDP methods only consider linear correlations between features(indicators)of the source and target projects.These models are not capable of evaluating non-linear correlations between features when they exist,for example,when there are differences in data distributions between the source and target projects.As a result,the performance of such CPDP models is compromised.In this paper,this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique(SMOTE)and Deep Canonical Correlation Analysis(DCCA),referred to as S-DCCA.Canonical Correlation Analysis(CCA)is employed to address the issue of non-linear correlations between features of the source and target projects.S-DCCA extends CCA by incorporating the MlpNet model for feature extraction from the dataset.The redundant features are then eliminated by maximizing the correlated feature subset using the CCA loss function.Finally,cross-project defect prediction is achieved through the application of the SMOTE data sampling technique.Area Under Curve(AUC)and F1 scores(F1)are used as evaluation metrics.This paper conducted experiments on 27 projects from four public datasets to validate the proposed method.The results demonstrate that,on average,our method outperforms all baseline approaches by at least 1.2%in AUC and 5.5%in F1 score.This indicates that the proposed method exhibits favorable performance characteristics.
基金This work was supported by the National Natural Science Foundation of China(U2133208,U20A20161).
文摘With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.
基金Taishan Young Scholars Program of Shandong Province,Key Development Program for Basic Research of Shandong Province(ZR2020ZD44).
文摘Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent automation.It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks.However,3D CT blocks necessitate significantly higher hardware resources during the learning phase.Therefore,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks.In this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named TCE-Net.The designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT slices.Besides,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various sizes.Extensive experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.
基金Natural Science Foundation of Guangdong Province(No.2018A0303130306)Shantou Science and Technology Program(No.190917085269835,No.200629165261641).
文摘AIM:To assess the repeatability,interocular correlation,and agreement of quantitative swept-source optical coherence tomography angiography(OCTA)optic nerve head(ONH)parameters in healthy subjects.METHODS:Thir ty-three healthy subjects were enrolled.The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm×3 mm scanning protocol.Images of the radial peripapillary capillary were analyzed by a customized Matlab program,and the vessel density,fractal dimension,and vessel diameter index were measured.The repeatability of the four scans was determined by the intraclass correlation coefficient(ICC).The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient,ICC and Bland-Altman plots.RESULTS:All swept-source-OCTA ONH parameters exhibited certain repeatability,with ICC>0.760 and coefficient of variation(CoV)≤7.301%.The obvious interocular correlation was observed for papillary vessel density(ICC=0.857),vessel diameter index(ICC=0.857)and fractal dimension(ICC=0.906),while circumpapillary vessel density exhibited moderate interocular correlation(ICC=0.687).Bland-Altman plots revealed an agreement range of-5.26%to 6.21%for circumpapillary vessel density.CONCLUSION:OCTA ONH parameters demonstrate good repeatability in healthy subjects.The interocular correlations of papillary vessel density,fractal dimension and vessel diameter index are high,but the correlation for circumpapillary vessel density is moderate.
基金Supported by Medical Health Science and Technology Project of Huzhou City,No.2022GY41.
文摘BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturient women play a key role in communication and emotional support.This study explores the PPD support relationship with partners and its influencing factors,which is believed to establish psychological well-being and improve maternal partner support.AIM To explore the correlation between PPD and partner support during breastfeeding and its influencing factors.METHODS Convenience sampling was used to select lactating women(200 women)who underwent postpartum examinations at the Huzhou Maternity and Child Health Care Hospital from July 2022 to December 2022.A cross-sectional survey was conducted on the basic information(general information questionnaire),depression level[edinburgh postnatal depression scale(EPDS)],and partner support score[dyadic coping inventory(DCI)]of the selected subjects.Pearson’s correlation analysis was used to analyze the correlation between PPD and DCI in lactating women.Factors affecting PPD levels during lactation were analyzed using multiple linear regression.RESULTS The total average score of EPDS in 200 lactating women was(9.52±1.53),and the total average score of DCI was(115.78±14.90).Dividing the EPDS,the dimension scores were:emotional loss(1.91±0.52),anxiety(3.84±1.05),and depression(3.76±0.96).Each dimension of the DCI was subdivided into:Pressure communication(26.79±6.71),mutual support(39.76±9.63),negative support(24.97±6.68),agent support(6.87±1.92),and joint support(17.39±4.19).Pearson’s correlation analysis demonstrated that the total mean score and individual dimension scores of EPDS during breastfeeding were inversely correlated with the total score of partner support,stress communication,mutual support,and cosupport(P<0.05).The total mean score of the EPDS and its dimensions were positively correlated with negative support(P<0.05).Multiple linear regression analysis showed that the main factors affecting PPD during breastfeeding were marital harmony,newborn health,stress communication,mutual support,negative support,cosupport,and the total score of partner support(P<0.05).CONCLUSION PPD during breastfeeding was associated with marital harmony,newborn health,stress communication,mutual support,negative support,joint support,and the total DCI score.
基金Project supported by the National Key Research and Development Program of China (Grant Nos.2022YFF0709103,2022YFA1603601,2021YFF0601203,and 2021YFA1600703)the National Natural Science Foundation of China (Grant No.81430087)the Shanghai Pilot Program for Basic Research-Chinese Academy of Sciences,Shanghai Branch (Grant No.JCYJ-SHFY-2021-010)。
文摘Beam splitting is one of the main approaches to achieving x-ray ghost imaging, and the intensity correlation between diffraction beam and transmission beam will directly affect the imaging quality. In this paper, we investigate the intensity correlation between the split x-ray beams by Laue diffraction of stress-free crystal. The analysis based on the dynamical theory of x-ray diffraction indicates that the spatial resolution of diffraction image and transmission image are reduced due to the position shift of the exit beam. In the experimental setup, a stress-free crystal with a thickness of hundredmicrometers-level is used for beam splitting. The crystal is in a non-dispersive configuration equipped with a double-crystal monochromator to ensure that the dimension of the diffraction beam and transmission beam are consistent. A correlation coefficient of 0.92 is achieved experimentally and the high signal-to-noise ratio of the x-ray ghost imaging is anticipated.Results of this paper demonstrate that the developed beam splitter of Laue crystal has the potential in the efficient data acquisition of x-ray ghost imaging.