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Complex field conditions always create different interferences during seismic data acquisition, and there exist several types of noise in the recorded data, which affect the subsequent data processing and interpretation. To separate an effective signal from the noisy data, we adopted a pattern-based method with a two-step strategy, which involves two adaptive prediction-error filters (APEFs) corresponding to a nonstationary data pattern and noise pattern. By introducing shaping regularization, we first constructed a least-squares problem to estimate the filter coefficients of the APEF. Then, we solved another constrained least-square problem corresponding to the pattern-based signal–noise separation, and different pattern operators are adopted to characterize random noise and ground-roll noise. In comparison with traditional denoising methods, such as FXDECON, curvelet transform and local time–frequency (LTF) decomposition, we examined the ability of the proposed method by removing seismic random noise and ground-roll noise in several examples. Synthetic models and field data demonstrate the validity of the strategy for separating nonstationary signal and noise with different patterns.

Organophosphorus nerve agents (OPNAs) covalently bind to tyrosine 411 of human serum albumin (HSA) and the formed adducts are stable biomarkers of OPNA exposure. The detection of these adducts has been limited to mass spectrometry techniques combined with protein digestion. Here, we developed indirect competitive ELISA (icELISA) methods to verify OPNA exposure by the detection of OPNA-phosphonylated adducts at tyrosine 411 residue (OPNA-HSA adducts), in which monoclonal antibodies (mAbs) against phosphonylation sites at tyrosine 411 were introduced. The two mAbs were prepared by the fourth generation of rabbit mAb technology using the phosphonylated peptides of LVRY

More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.

We reported a layer-controlled transition from the soft to hard ferromagnetic state in atomically thin CrSiTe3 flakes. Our study paves the way towards exploring and learning much more atomically thin and layered intrinsic ferromagnets.

Buried craters within tens of meters of lunar regolith are rarely studied but are significant for understanding the evolution of surface processes on the Moon. Here, we first report the evidence of an intact buried crater within the layered strata at Chang'E-4 (CE-4) landing site revealed by the lunar penetrating radar (LPR). The time-frequency comparative analysis method based on the variational mode decomposition (VMD) and the rock quantitative analysis method based on the local unit correlation (LUC) are proposed and applied to the processing and analysis of LPR data within 15 lunar days. The results presented by the two methods provide evidence of a buried crater at the CE-4 landing site and simultaneously reveal the rock-concentrated structure within the buried crater. According to the results, it is considered that the filling materials within the buried crater have survived the impaction and gardening during the formation of the overlying fine-grained regolith. Recent works have proposed that the near-surface material at the CE-4 landing site is mainly the lunar mantle materials excavated from the nearby Finsen crater. Therefore, the buried crater probably preserves the initial lunar mantle materials.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss-Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Background NT Pro‐BNP is a blood marker secreted by cardiomyocytes. Myocardial stretch is the main factor to stimulate NT Pro‐BNP secretion in cardiomyocytes. NT Pro‐BNP is an important risk factor for cardiac dysfunction, stroke, and pulmonary embolism. So does atrial myocyte stretching occur when patients have atrial fibrillation (AF)? Whether atrial muscle stretch induced by AF leads to increased NT Pro‐BNP remains unclear. The purpose of this study is to investigate the relationship between NT Pro‐BNP and AF. Hypothesis AF can cause changes in myocardial tension. Changes in myocardial tension may lead to increased secretion of NT Pro‐BNP. We hypothesize that NT Pro‐BNP may increase in AF with or without LAD enlargement. Methods This clinical study is an observational study and has been approved by the Ethics Committee of the First Affiliated Hospital of Xi'an Jiaotong University. Ethical approval documents is attached. The study retrospectively reviewed 1345 patients with and without AF. After excluding 102 patients who were not eligible, the final total sample size was 1243 cases: AF group 679 patients (378, 55.7% males) and non‐AF group 564 patients (287, 50.8% males). NT Pro‐BNP was observed in AF group and non‐AF group with or without LAD. After adjusting for age, gender, BMI, left atrial diameter, hypertension, diabetes, coronary heart disease, and cerebral infarction, NT Pro‐BNP remains statistically significant with AF. Conclusion NT Pro‐BNP can be used as a risk predictor of AF with or without left atrial enlargement.

SUMMARY Surface conditions and economic factors restrict field geometries, so seismic data acquisition typically obtains field data with irregular spatial distribution, which can adversely affect the subsequent data processing and interpretation. Therefore, data interpolation techniques are used to convert field data into regularly distributed data and reconstruct the missing traces. Recently, the mainstream methods have implemented iterative algorithms to solve data interpolation problems, which require substantial computational resources and restrict their application in high dimensions. In this study, we proposed the f–x and f–x–y streaming prediction filters (SPFs) to reconstruct missing seismic traces without iterations. According to the streaming computation framework, we directly derived an analytic solution to the overdetermined least-squares problem with local smoothness constraints for estimating SPFs in the frequency domain. We introduced different processing paths and filter forms to reduce the interference of missing traces, which can improve the accuracy of filter coefficients. Meanwhile, we utilized a two-step interpolation strategy to guarantee the effective interpolation of the irregularly missing traces. Numerical examples show that the proposed methods effectively recover the missing traces in seismic data when compared with the traditional Fourier Projection Onto Convex Sets method. In particular, the frequency domain SPFs are suitable for high-dimensional seismic data interpolation with the advantages of low computational cost and reasonable non-stationary signal reconstruction.

The nearly perfectly matched layer (NPML) is a non-split perfectly matched layer that directly transforms the wave field. Compared to other non-split perfectly matched layers, NPML is computationally efficient and has advantages such as not changing the form of the equation and being easy to implement. However, in TTI medium simulations it is found that the NPML is very unstable and cannot absorb near-grazing incident waves. This explains why the CPML is currently used more frequently. In this study, the complex frequency shifted transform is used to enhance the absorption of near-grazing incident waves with NPML and to avoid the generation of low-frequency singular values. At the same time, the double damping profile is used to improve the stability of the boundary we called MCFS-NPML. Subsequently, this method is also applied to seismic wave equations in poroelastic media. In order to further improve the absorption capacity of the boundary and weaken the deviation caused by the discrete difference, a new attenuation function is proposed.

Deconvolution mainly improves the resolution of seismic data by compressing seismic wavelets, which is of great significance in high-resolution processing of seismic data. Prediction-error filtering/least-square inverse filtering is widely used in seismic deconvolution and usually assumes that seismic data is stationary. Affected by factors such as earth filtering, actual seismic wavelets are time- and space-varying. Adaptive prediction-error filters are designed to effectively characterise the nonstationarity of seismic data by using iterative methods, however, it leads to problems such as slow calculation speed and high memory cost when dealing with large-scale data. We have proposed an adaptive deconvolution method based on a streaming prediction-error filter. Instead of using slow iterations, mathematical underdetermined problems with the new local smoothness constraints are analytically solved to predict time-varying seismic wavelets. To avoid the discontinuity of deconvolution results along the space axis, both time and space constraints are used to implement multichannel adaptive deconvolution. Meanwhile, we define the parameter of the time-varying prediction step that keeps the relative amplitude relationship among different reflections. The new deconvolution improves the resolution along the time direction while reducing the computational costs by a streaming computation, which is suitable for handling nonstationary large-scale data. Synthetic model and field data tests show that the proposed method can effectively improve the resolution of nonstationary seismic data, while maintaining the lateral continuity of seismic events. Furthermore, the relative amplitude relationship of different reflections is reasonably preserved.

The full-polarimetric ground-penetrating radar (FP-GPR) can obtain the polarimetric attributes of targets and achieve more accurate identification compared with traditional GPRs. However, in most cases, the polarimetric signals collected by GPRs are not only from the targets but also from the ground surface. According to the classical Fresnel formulas, the polarized directions of the waves will change after transmissions due to the difference in the transmission coefficients between horizontally and vertically polarized waves. This effect, called induced field rotation (IFR), will interfere with the acquisition of polarimetric attributes and also exists in the field measurements on a rough surface. In this study, we have derived the association between the measured FP-GPR data and transmission coefficients of the rough surface provided by the small perturbation method (SPM). The effects of IFRs from the rough surface on H-Alpha decomposition are analyzed later. The results show that the parameters of the rough surface will affect the values of components in the scattering matrix, but will not change the matrix and H-Alpha decomposition result; the incident angle and relative permittivity play main roles; for the H-Alpha decomposition results of three typical targets, the flat and dihedral are little influenced by IFRs, but the cylinder is seriously affected; simultaneously, a template for discriminating whether the calibration for H-Alpha decomposition is necessary is established. Both numerical and experimental tests validate the conclusions. Finally, a novel application strategy of H-Alpha decomposition is proposed, which has taken IFR from the rough surface into considerations.

ObjectiveThe purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.MethodsPatients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.ResultsA total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.ConclusionThis study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making

Topological states of matter possess bulk electronic structures categorized by topological invariants and edge/surface states due to the bulk-boundary correspondence. Topological materials hold great potential in the development of dissipationless spintronics, information storage, and quantum computation, particularly if combined with magnetic order intrinsically or extrinsically. Here, we review the recent progress in the exploration of intrinsic magnetic topological materials, including but not limited to magnetic topological insulators, magnetic topological metals, and magnetic Weyl semimetals. We pay special attention to their characteristic band features such as the gap of topological surface state, gapped Dirac cone induced by magnetization (either bulk or surface), Weyl nodal point/line, and Fermi arc, as well as the exotic transport responses resulting from such band features. We conclude with a brief envision for experimental explorations of new physics or effects by incorporating other orders in intrinsic magnetic topological materials., 32 pages, 15 figures

Layered ferromagnets with strong magnetic anisotropy energy (MAE) have special applications in nanoscale memory elements in electronic circuits. Here, we report a strain tunability of perpendicular magnetic anisotropy in van der Waals (vdW) ferromagnets VI

The effects of doped CeO2 on the magnetic properties and microstructure are studied in 2:17 type SmCo magnets. With the increase of CeO2 from 0 wt.% to 3 wt.%, the coercivity of magnets increases from 22.22 kOe to over 29.37 kOe, which is an increase of more than 30%. When the doping content is lower than 1 wt.%, the remanence and magnetic energy product of the magnet remain almost constant. Both decrease sharply as the doping concentration further increases. After CeO2 doping, the oxide content in the magnet increases significantly and the Ce element is uniformly distributed in the magnet. Observing the magnetic domain reveals that doping CeO2 can refine the magnetic domains and make the magnetic domain wall more stable, resulting in a significant increase in the coercivity of the magnets.

Increased water temperature is considered an important cause of the loss of seagrass beds. This paper quantified the interactive influence of different combinations of water temperature and duration on the responses of Zostera marina plants in terms of survivorship, morphology, growth and physiology. The LT50 (lethal temperature that caused an increase in mortality to 50% of that of the control) and ET50 (effect time that caused a decrease in growth to 50% of that of the control) were calculated to reveal the quantitative relationship between temperature and duration that resulted in limiting effects on the survival and growth of Z. marina plants. Z. marina plants were exposed to different combinations of water temperature [23 (control), 25, 27, 29, and 31 °C] and duration (5, 10, 15 and 20 days), and then the plants were transferred to the control condition for over 30 days under laboratory conditions. The results showed that the survival rate of plants at the end of recovery were significantly lower than those of plants at the end of direct impact under the temperature levels of 29 and 31 °C in each duration, indicating that short-term periods of obviously increased water temperature would lead to long-term effects on the survival of Z. marina plants. Regression analysis revealed that the relationship between water temperature and duration that resulted in limiting effects on the survival and growth of Z. marina could be described as a strong power function. Pearson correlation analysis showed that the survival and growth of Z. marina plants exposed to different temperature levels were significantly correlated with leaf soluble sugar contents. This study will further develop our understanding of the degradation and disappearance of seagrass beds induced by increased temperature.