Maximum Correlation Research Articles

Multi‐Domain Detection Computational Imaging via Reconfigurable Metasurfaces MIMO System

AbstractA reconfigurable transmission metasurfaces (RTMs) excited by a multiple‐input multiple‐output (MIMO)( RTMs‐MIMO) antenna is designed to implement multi‐domain metamaterial computational imaging (MCI). Based on the receiver‐transmitter metasurfaces structure, four PIN diodes are applied to control the operating state of the RTMs. It enables arbitrary switching among four polarization transmission waves. A reverse‐induced current can be generated by adjusting the PIN diodes in the symmetrical position, resulting in a perfect 1‐bit phase modulation with a 3dB‐loss bandwidth of 15.38~19.89 GHz. A microstrip antenna with mushroom‐shaped parasitic patches is employed to implement the MIMO feeds. The ‐10dB bandwidth of MIMO antenna is compatible with the RTMs, and the maximum envelope correlation coefficient of 0.16 demonstrates good isolation between the MIMO feeds. In the MCI system constructed by the RTMs‐MIMO antennas, a transmission matrix H can be achieved by MIMO space beams, metasurfaces phase, metasurfaces polarization, and frequency modes. The correlation coefficient µg = 0.018 is achieved by the multi‐domain integration, allowing H to be highly non‐correlated. Based on the detection results of H, an imaging result with a relative reconstruction error of 0.372 is achieved. The designed MCI system therefore has unparalleled application prospects in the broad field of medical and military detection.

Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque.

Carotid atherosclerotic ischemic stroke threatens human health and life. The aim of this study is to establish a radiomics model of perivascular adipose tissue (PVAT) around carotid plaque for evaluation of the association between Peri-carotid Adipose Tissue structural changes with stroke and transient ischemic attack. A total of 203 patients underwent head and neck computed tomography angiography examination in our hospital. All patients were divided into a symptomatic group (71 cases) and an asymptomatic group (132 cases) according to whether they had acute/subacute stroke or transient ischemic attack. The radiomic signature (RS) of carotid plaque PVAT was extracted, and the minimum redundancy maximum correlation, recursive feature elimination, and linear discriminant analysis algorithms were used for feature screening and dimensionality reduction. It was found that the RS model achieved the best diagnostic performance in the Bagging Decision Tree algorithm, and the training set (AUC, 0.837; 95%CI: 0.775, 0.899), testing set (AUC, 0.834; 95%CI: 0.685, 0.982). Compared with the traditional feature model, the RS model significantly improved the diagnostic efficacy for identifying symptomatic plaques in the testing set (AUC: 0.834 vs. 0.593; Z = 2.114, p = 0.0345). The RS model of PVAT of carotid plaque can be used as an objective indicator to evaluate the risk of plaque and provide a basis for risk stratification of carotid atherosclerotic disease.

Experiment on long-term forecasting of geomagnetic activity based on nonlocal correlations

The experiment was performed on the use of advanced macroscopic nonlocal correlations to forecast slow random fluctuations of the Dst index of geomagnetic activity. The global maximum correlation of Dst with the signal of the electrode detector reaches 0.97, which is sufficient for the forecast, and its time shift corresponds to the advance of the detector signal relative to Dst by 329 days. The large magnitude of the time shift is due to the slow diffusion mechanism of entanglement swapping between the detector and the source. At the same time, the position of the global maximum of the correlation function coincides with the position of the global minimum of the entropy independence function, which confirms its undistorted by possible nonlinearity of the relationship and determines the optimal lead time of the forecast. Long series of test forecasts Dst have been calculated using data from a nonlocal correlation detector with a fixed lead time using three methods: current regression, current impulse transient response and current neural network. The accuracy of the forecasts is sufficient for all practical purposes.

MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed.Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.

Determination of process parameters for selective laser melting of inconel 718 alloy through evolutionary multi-objective optimization

ABSTRACT Selective laser melting (SLM) is a sustainable process that offers various environmental benefits. However, the parts produced from SLM process require post-processing treatments that increase the energy consumption. Therefore, there is a need for optimization of SLM input parameters to minimize the same. For this purpose, the data set on selectively laser-melted Inconel 718 parts was obtained from the reference. An evolutionary neural net has been employed to model the objective functions: specific energy consumption, relative density and surface roughness in the present study. The neural net strategy was successful in capturing the important trend of the three objectives by achieving a maximum correlation coefficient of 85% in each of them. Subsequently, the trained model is used in tri-objective optimization to yield the optimum input parameters. A close agreement is observed between the predicted optimum parameters and experimentally obtained parameters, proving the formulated strategy to be reliable and effective.

A Rational Design of Electrochemically and Photophysically Tunable Triarylamine Luminophores by Consecutive (Pseudo-)Four-Component Syntheses.

The concatenation of Suzuki coupling and two-fold Buchwald-Hartwig amination in sequentially palladium-catalyzed consecutive multicomponent syntheses paves a concise, convergent route to diversely functionalized para-biaryl-substituted triarylamines (p-bTAAs) from simple, readily available starting materials. An extensive library of p-bTAAs permits comprehensive investigations of their electronic properties by absorption and emission spectroscopy, cyclic voltammetry, and quantum chemical calculations, which contribute to a deep understanding of their electronic structure. The synthesized p-bTAAs exhibit tunable fluorescence from blue to yellow upon photonic excitation with quantum yields up to 98 % in solution and 92 % in the solid state. Furthermore, a pronounced bathochromic shift of the emission maxima by increasing solvent polarity indicates positive emission solvatochromism. Aggregation-induced enhanced emission (AIEE) in dimethyl sulfoxide (DMSO)/water mixtures causes the formation of intensely blue fluorescent aggregates. Cyclic voltammetry shows reversible first and second oxidations of p-bTAAs at low potentials, which are tunable by variation of the introduced para substituents. 3D Hammett plots resulting from the correlation of oxidation potentials and emission maxima with electronic substituent parameters emphasize the rational design of tailored p-bTAAs with predictable electrochemical and photophysical properties.

Biomechanical properties of nasal tissues

Introduction. Plastic rhinosurgery and augmentation rhinoplasty are very relevant today. Especially in relation to patients with congenital saddle deformity of the nasal dorsum, as well as patients with iatrogenic disorders of the nose shape, which dramatically reduce the quality of human life. There are violations of the function of the nose. The purpose of the work. Purpose. Within the framework of the necessity of performing silicone implantation rhinoplasty, the strength, elastic and hyperelastic properties of the soft tissues of the nasal fascia and periosteum were studied, the dynamic viscosity of the tissues of the nasal fascia and periosteum was evaluated, determining their relaxation properties using elastic, hyperelastic and rheological models. Methods. Linear, bilinear (with two Young modules), exponential, hyperelastic (neohookean, Mooney-Rivlin, Ogden, Yeoh, polynomial and Veronda-Westmann) and elastic-viscous (Maxwell) models of biological tissues were used. We used the Mathcad 15.0 computer algebra system and the universal package of interdisciplinary programs ANSYS Multiphysics Software (version 2022 R2). The accuracy of replacing the properties of real tissues with the results of calculations was calculated on the basis of indicators of descriptive statistics (standard deviation, maximum absolute error, maximum relative error and correlation coefficient). Results. It is proved that the bilinear model for the exact reproduction of the stress-strain curve assumes at the molecular level of tissues an initial linear reaction of elastin fibers, passing at ε=εcr into the final linear reaction of the collagen matrix. It was found that the properties of the periosteum (correlation coefficient R=0.9999) and nasal fascia (correlation coefficient R=0.9999) are described most adequately by the 5-parametric polynomial model of the 2nd order and the Yeoh model of the 3rd order (R=0.999 and R=1, respectively), the least accurately by the simple 1-parameter neohookean model (R=0.898 for the periosteum and R=0.905 for the fascia). Among elastic models, the exponential dependence characterizes the behavior of nasal materials quite well. Conclusion. Biomechanically verified that the periosteum is thicker, stronger and more rigid than the fascia. The established very high viscosity values indicate the slowness of relaxation processes in tissues. The periosteum relaxes tension more slowly, which improves the result of implant fixation. On the contrary, the relaxation time of the periosteum is higher than that of the fascia, from which it is concluded that it is preferable not to cut the periosteum during subcostal implantation.

A Measure of China's Housing Price Bubble from the Perspective of Dynamic House-price-to-income Ratio

The objective of this study is to improve the current model for determining the upper limit of the static price-to-income ratio by considering multiple elements that impact the growth of residents' income throughout the duration of their mortgage. The technique being offered presents a model that incorporates a dynamic house-price-to-income ratio. The underlying principle of this concept is derived from the theoretical framework of the equitable maximum correlation between property prices and income. The range of the measured dynamic ratio between house prices and income spans from 2.0755 to 4.3662. The calculation of this ratio should be based on the average interest rate of loans for commercial real estate, the ratio of mortgage repayment to income, and the average rate of growth in per capita disposable income for Chinese banks. The study employed the dynamic house-price-to-income ratio approach to calculate the dynamic house-price-to-income ratio for 31 provinces in China. This was done by considering the provinces with the lowest per capita living area and those with a moderately livable area. The findings of the comparison demonstrate that, once the essential criteria for a reasonably adequate per capita living area are met, the housing prices in all provinces, with the exception of Inner Mongolia, Shandong, and Hunan, exceed the acceptable threshold of the dynamic ratio between income and house prices.

Analyzing domain features of small proteins using a machine-learning method.

Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs. The purpose was to identify important functional domains that can indicate the essential differences between small and large protein sequences. First, the small and large proteins were represented by their functional domains via a one-hot scheme. Then, the MaxRel method was adopted to evaluate the relationships between each domain and the target variable, indicating small or large protein. The top 36 domain features were selected for further investigation. Among them, 14 were deemed to be highly related to SPs because they were annotated to SPs more frequently than large proteins. We found the involvement of functional domains, such as ubiquitin-conjugating enzyme/RWD-like, nuclear transport factor 2 domain, and alpha subunit of guanine nucleotide-binding protein (G-protein) in regulating the biological function of SPs. The involvement of these domains has been confirmed by other recent studies. Our findings indicate that protein functional domains may regulate small protein-related functions and predict their biological activity.

Wavelength selection method for near-infrared spectroscopy based on Max-Relevance Min-Redundancy

Near-infrared spectroscopy (NIRS) is a rapid, nondestructive analytical technique utilized in various fields. However, the NIR data, which consists of hundreds of dimensions, may exhibit considerable duplication in the spectrum information. This redundancy might impair modeling effectiveness. As a result, feature selection on the spectral data becomes critical. The Max-Relevance Min-Redundancy (mRMR) method stands out among the different feature selection techniques for dimensional reduction. The approach depends on mutual information (MI) between random variables as the basis for feature selection and is unaffected by modeling methods. However, it is necessary to clarify the benefits of the maximum correlation minimal redundancy algorithm in the context of near-infrared spectral feature selection, as well as its adaptability to various modeling methods. This research focuses on the NIR spectral dataset of maize germination rate, and the mRMR method is utilized to select spectral features. Based on the preceding foundation, we create models for Support Vector Regression, Gaussian Process Regression, Random Forest, and Neural Networks. The experimental findings demonstrate that, among the feature selection methods employed in this paper, the Max-Relevance Min-Redundancy algorithm outperforms others regarding the corn germination rate dataset.

A novel method of rolling bearings fault diagnosis based on singular spectrum decomposition and optimized stochastic configuration network

Fault diagnosis of rolling bearings is essential for the safe operation of rotating machinery. However, in the production process, the rolling bearings have a complex working environment embedded with weak fault signals and a large number of interfering signals, which is a considerable challenge to automatically and accurately detect bearing fault type from the actual vibration signal. Therefore, a novel fault diagnosis scheme is proposed based on singular spectrum decomposition (SSD) and an optimized stochastic configuration network (SCN). Firstly, SSD is used to pre-process the original rolling bearings vibration signal to obtain several singular spectral components (SSCs) and the practical component is selected according to the maximum correlation coefficient for signal reconstruction. Furthermore, time domain and power spectrum entropy (PSE) features of the reconstructed signal are extracted to obtain a fault information-rich feature sets. In addition, the parameters of the SCN are optimized by marine predators algorithm (MPA) to enhance the learning ability and generalization performance of the SCN. Finally, the feature sets are input into MPA-SCN to achieve fault classification. Experimental results exhibit that the proposed method has higher accuracy in rolling bearings fault diagnosis compared with other methods, which provides a high-efficiency solution for rolling bearings fault diagnosis.

Numerical simulation of wall shear stress and boundary layer flow from jetting cavitation bubble on unheated and heated surfaces

The physics of wall shear stress and boundary layer flow from jetting cavitation bubbles on unheated and heated surfaces was investigated numerically using a homogeneous mixture model based on a fully compressible Navier–Stokes solver. A spatial–temporal shear stress map and velocity field on unheated surfaces under several standoffs were considered first. Complex boundary layer flow, including separation vortex caused by an adverse pressure gradient and the generation of a thermal boundary layer because of skin friction with the shear flow, was discussed in detail. The tendency of different maximum shear stress for each standoff section was discussed with liquid film thickness and jet velocity. Also, we suggested maximum shear stress correlation equation for minimum film thickness and wall impact pressure as dimensionless form. As an expansion of previous studies, we examined bubble collapse–induced wall shear stress on a heated surface under various surface temperatures. The peak shear stress decreased with the lower Prandtl number flow. In the collapse phase, surface cooling by downward microjet and heat transfer with ring vortex were also captured. Consequently, we suggested correlation equations for primary and secondary peak shear stresses as function of Prandtl number and confirmed that the boundary layer flow including heat transfer can be well simulated.

Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.

Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

Capacity degradation analysis and knee point prediction for lithium-ion batteries

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.

Visualization and quantification of microphase separation in thermoplastic polyurethanes under different hard segment contents and its effect on the mechanical properties

The microstructure and macroscopic properties of thermoplastic polyurethanes (TPUs) are significantly affected by their microphase separation. It is therefore of great interest to quantitatively characterize the microphase separation of TPUs and explore its connection with their mechanical properties. In this work, a method based on atomic force microscopy is proposed to visualize and quantify the microphase separation of polyether TPUs with different hard segment contents. The lateral correlation length was proposed to describe the scale of microphase separation and the microscopic Young's modulus distributions were also obtained for comparison. Results show that the macroscopic Young's modulus of the polyether TPU is consistent with the microscopic Young's modulus only when the lateral correlation length reaches a certain threshold. Besides, the studied polyether TPU exhibits the best overall mechanical properties when it reaches the maximal lateral correlation length. The proposed AFM-based method demonstrates a versatile analytical tool for quantitatively assessing the microphase separation and unveiling the relationship between the microphase separation, microscopic mechanical properties, and the macroscopic properties of TPUs.

A novel time-frequency slice extraction method for target recognition and local enhancement of non-stationary signal features

Blind deconvolution can remove the effects of complex paths and extraneous disturbances, thus recovering simple features of the original fault source, and is used extensively in the field of fault diagnosis. However, it can only identify and extract the statistical mean of the fault impact features in a single domain and is unable to simultaneously highlight the local features of the signal in the time-frequency domain. Therefore, the extraction effect of weak fault signals is generally not ideal. In this paper, a new time-frequency slice extraction method is proposed. The method first computes a high temporal resolution spectrum of the signal by short-time Fourier transform to obtain multiple frequency slices with distinct temporal waveforms. Subsequently, the constructed harmonic spectral feature index is used to quantify and target the intensity of feature information in each frequency slice and enhance their fault characteristics using maximum correlation kurtosis deconvolution. Enhancing the local features of selected frequency slice clusters can reduce noise interference and obtain signal components with more obvious fault signatures. Finally, the validity of the method was confirmed by a simulated signal and fault diagnosis of the rolling bearing outer and inner rings was accomplished sequentially. Compared with other common deconvolution methods, the proposed method obtains more accurate and effective results in identifying fault messages.

Sensitivity and reliability analysis to MSBAS regularization for the estimation of surface deformation over a mine

To systematically and thoroughly analyze the sensitivity and reliability of the MSBAS regularization for the estimation of surface deformation over a mine in combination with an application example, this study processed 101 Sentinel-1A/B SAR images, constructed and solved the 2D deformation models using SVD and Tikhonov regularization methods with different orders and parameters, and estimated the vertical and east-west surface deformation time series in a mine of China. Then, this study collected the leveling-monitoring vertical surface deformation data on three leveling points, and compared and analyzed the sensitivity and reliability of the MSBAS regularization methods for estimating vertical surface deformation. The results indicate that different regularization orders and parameters can lead to thousands of times differences in condition numbers and significant differences in ill-posed degree of the deformation models. The zero-order Tikhonov regularized deformation model with regularization parameter of 0.1 has the minimum condition number and the equation is not ill-posed. The first-order Tikhonov regularized deformation model with regularization parameter of 0.001 has the maximum condition number and the equation is seriously ill-posed. As a result, the estimates of 2D surface deformation models with different parameters and orders are also different in terms of numerical values and accuracy. Compared with the leveling-monitoring data, the first- and second-order MSBAS regularization methods with parameter 0.1 have the minimum fluctuation and the maximum correlation coefficients between the estimated values and the leveling-monitoring values, and are also closest to the leveling-monitoring results with the highest accuracy.

Predicting nickel concentration in soil using fractional-order derivative and visible-near-infrared spectroscopy indices.

Accurate monitoring and estimation of heavy metal concentrations is an important process in the prevention and treatment of soil pollution. However, the weak correlation between spectra and heavy metals in soil makes it difficult to use spectroscopy in predicting areas with a risk of heavy metal pollution. In this paper, a method for detection of Ni in soil in eastern China using the fractional-order derivative (FOD) and spectral indices was proposed. The visible-near-infrared (Vis-NIR) spectra were preprocessed using the FOD (range: 0 to 2, interval: 0.1) to solve the problems of baseline drift and overlapping peaks in the original spectra. The product index (PI), ratio index (RI), sum index (SI), difference index (DI), normalized difference index (NDI), and brightness index (BI) were applied and compared. The results showed that the spectral detail increased as the FOD increased, and the interference of the baseline drift and overlapping peaks was eliminated as the spectral reflectance decreased. Furthermore, the FOD extracted the spectral sensitivity information more effectively and improved the correlation between the Vis-NIR spectra and the Ni concentration, and the NDI had a maximum correlation coefficient (r) of 0.803 for order 1.9. The estimation model based on the NDI dataset constructed after FOD processing had the best performance, with a validation accuracy [Formula: see text] of 0.735, RMSE of 3.848, and RPD of 2.423. In addition, this method is easy to carry out and suitable for estimating other heavy metal elements in soil.

Causal relationship between seasonal variation in surface roughness and dust and sandstorm occurrence in arid and semi-arid Mongolia

In this study, the causal relationship between seasonal variations in surface conditions and DSS generation was investigated through the integrated use of the C-band backscatter coefficient (J 0) of the Sentinel 1 microwave satellite from the optical satellite sensor (Terra/MODIS). Because of the very high reflectance of the background soil in sparsely vegetated bare land, it has been difficult to rely solely on optical satellite data for accurate surface condition assessment in previous studies. Therefore, we used SAR and optical data to represent the spatial variability of backscatter coefficients for different surface conditions in the Gobi Desert and Steppe areas, and employed the C-band backscatter coefficient from the Sentinel-1 microwave satellite as a key parameter. The results show that in the Gobi Desert region, the correlation between the seasonal variation of backscatter coefficients and NDVI is highly significant, with a maximum correlation coefficient in fall (R=0.645, p<0.001) an inverse correlation in summer (R=-0.216, p<0.001), indicating little seasonal variation between NDVI and backscatter coefficients. In conclusion, surface roughness (σ0) is relatively low and stable from winter to the following spring and increases with plant growth in summer. In the Gobi Desert region, surface roughness changes little throughout the year, but in the steppe region, surface roughness increases with vegetation growth during the rainy season, which suppresses the generation of DSS.