A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely investigated.In many industrial scenarios,contactless sensors are more preferred.The event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R~2)values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

A Possible X-ray Quasi-periodic Oscillation in the Narrow-line Seyfert 1 Galaxy Mrk 142

A possible quasi-periodic oscillation(QPO) at frequency 7.045 × 10^(-5) Hz is found in the narrow-line Seyfert 1galaxy Mrk 142 in the data of XMM-Newton collected on 2020 April 11.We find that the QPO signal is statistically significantly larger than the 5σ level and highly coherent with quality factor Q > 5 at the 0.3–10 keV band by using the method of the Lomb–Scargle Periodogram,the Weighted Wavelet Z-transform and the REDFIT.We analyze the data in 0.3–0.6 keV,0.6–1 keV,1–3 keV and 3–10 keV energy bands,and find obvious QPO signals at 0.3–0.6 keV and 1–3 keV bands.We then analyze the time-average spectra and time variability at the QPO frequency of 7.045 × 10^(-5) Hz,and use a model to fit them.We find that the QPO signal mainly comes from the X-ray hot corona.

GENERALIZATION PERFORMANCE OF MULTI-CATEGORY KERNEL MACHINES——In Memory of Professor Sun Yongsheng

Support vector machines are originally designed for binary classification. How to effectively extend it for multi-class classification is still an on-going research issue. In this paper, we consider kernel machines which are natural extensions of multi-category support vector machines originally proposed by Crammer and Singer. Based on the algorithm stability, we obtain the generalization error bounds for the kernel machines proposed in the paper.

Chest Radiography: General Practitioners’ Compliance with Recommendations

Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Approximate entropy and support vector machines for electroencephalogram signal classification

The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

Mock X-Ray Observations of Hot Gas with L-Galaxies Semi-analytic Models of Galaxy Formation

We create mock X-ray observations of hot gas in galaxy clusters with a new extension of the L-Galaxies semianalytic model of galaxy formation,which includes the radial distribution of hot gas in each halo.Based on the model outputs,we first build some mock light cones,then generate mock spectra with the SOXS package and derive the mock images in the light cones.Using the mock data,we simulate mock X-ray spectra for the ROSAT all-sky survey,and compare the mock spectra with the observational results.Then,we consider the design parameters of the HUBS mission and simulate the observation of the halo hot gas for HUBS as an important application of our mock work.We find:(1)our mock data match the observations by current X-ray telescopes.(2)The survey of hot baryons in resolved clusters by HUBS is effective below redshift 0.5,and the observations of the emission lines in point-like sources at z>0.5 by HUBS help us understand the hot baryons in the early universe.(3)By taking advantage of the large simulation box and flexibility in semi-analytic models,our mock X-ray observations provide the opportunity to select targets and observation strategies for forthcoming X-ray facilities.

Spin Evolution of the Magnetar SGR J1935+2154

Fast radio bursts(FRBs)are short pulses observed in radio frequencies usually originating from cosmological distances.The discovery of FRB 200428 and its X-ray counterpart from the Galactic magnetar SGR J1935+2154suggests that at least some FRBs can be generated by magnetars.However,the majority of X-ray bursts from magnetars are not associated with radio emission.The fact that only in rare cases can an FRB be generated raises the question regarding the special triggering mechanism of FRBs.Here we report long time spin evolution of SGR J1935+2154 until the end of 2022.According to v and v,the spin evolution of SGR J1935+2154 could be divided into two stages.The first stage evolves relatively steady evolution until 2020 April 27.After the burst activity in2020,the spin of SGR J1935+2154 shows strong variations,especially for v.After the burst activity in 2022October,a new spin-down glitch with△v/v=(-7.2±0.6)×10^(-6)is detected around MJD 59876,which is the second event in SGR J1935+2154.At the end,spin frequency and pulse profile do not show variations around the time of FRB 200428 and radio bursts 221014 and 221021,which supply strong clues to constrain the trigger mechanism of FRBs or radio bursts.

X-ray image distortion correction based on SVR

X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.

Machine learning-based classification of rock discontinuity trace:SMOTE oversampling integrated with GBT ensemble learning

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique(SMOTE),random search(RS)hyper-parameters optimization algorithm and gradient boosting tree(GBT)to achieve efficient and accurate rock trace identification.A thirteen-dimensional database consisting of basic,vector,and discontinuity features is established from image samples.All data points are classified as either‘‘trace”or‘‘non-trace”to divide the ultimate results into candidate trace samples.It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4.Then,sixteen classifiers generated from four basic machine learning(ML)models are applied for performance comparison.The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and nontrace classifications.Finally,discussions on feature importance,generalization ability and classification error are conducted for the proposed classifier.The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features.Besides,cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance.The proposed method provides a new alternative approach for the identification of 3D rock trace.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

A simulation package—SASAL for X-ray and EUV spectroscopy of astrophysical and laboratory plasmas

For the laboratory astrophysics community, those spectroscopic modeling codes extensively used in astronomy, e.g. Chianti, AtomDB, Cloudy and Xstar, cannot be directly applied to analyzing laboratory measurements due to their discrepancies from astrophysical cases. For example, plasma from an electron beam ion trap has an electron energy distribution that follows a Gaussian profile, instead of a Maxwellian one. The laboratory miniature for a compact object produced by a laser-driven implo- sion shows a departure from equilibrium, that often occurs in celestial objects, so we setup a spectral analysis system for astrophysical and laboratory （SASAL） plasmas to act as a bridge between them, which benefits the laboratory astrophysical community.

A NEW HYPERSPHERE SUPPORT VECTOR MACHINE ALGORITHM

The hypersphere support vector machine is a new algorithm in pattern recognition. By studying three kinds of hypersphere support vector machines, it is found that their solutions are identical and the margin between two classes of samples is zero or is not unique. In this letter, a new kind of hypersphere support vector machine is proposed. By introducing a parameter n(n>1), a unique solution of the margin can be obtained. Theoretical analysis and experimental results show that the proposed algorithm can achieve better generaliza-tion performance.

Very Early Optical Afterglows for Geometric Models of X-ray Flashes and X-ray Rich GRBs

If X-ray flashes （XRFs） and X-ray rich Gamma-ray Bursts （XRRGs） have the same origin as the Gamma-ray bursts （GRBs） but are viewed off-center from structured jets, their early afterglows may differ from those of GRBs, and when the ultra-relativistic outflow inter- acts with the surrounding medium, there are two shocks formed, a forward shock （FS）, and a reverse shock （RS）. We calculate numerically the early afterglow powered by uniform jets, Gaussian jets and power-law jets in the forward-reverse shock scenario. A set of differential equations govern the dynamical evolution. The synchrotron self-Compton effect has been taken into account in the calculation. In the uniform jets, the very early afterglows of XRRGs and XRFs are significantly lower than the GRBs and the observed peak times of RS emission are later in the interstellar medium environment. The RS components in XRRGs and XRFs are difficult to detect, but in the stellar wind environment, the reduction of the very early flux and the delay of the RS peak time are not so remarkable. In nonuniform jets （Gaussian and power-law jets）, where there are emission materials on the line of sight, the very early light curve resembles equivalent isotropic ejecta in general although the RS flux decay index shows notable deviations if the RS is relativistic （in stellar wind）.

The composite X-ray spectra of radio-loud and radio-quiet SDSS quasars

We present a study of the X-ray emission for a sample of radio-detected quasars constructed from the cross-matches between SDSS,FIRST catalogs and XMM-Newton archives.A sample of radio-quiet SDSS quasars without FIRST radio detection is also assembled for comparison.We construct the optical and X-ray composite spectra normalized at rest frame 4215 A(or 2200 A)for both radio-loud quasars(RLQs)and radio-quiet quasars(RQQs)at z≤3.2,with matched X-ray completeness of 19%,redshift and optical luminosity.While the optical composite spectrum of RLQs is similar to that of RQQs,we find that RLQs have a higher X-ray composite spectrum than RQQs,consistent with previous studies in the literature.By dividing the radio-detected quasars into radio loudness bins,we find the X-ray composite spectra are generally higher with increasing radio loudness.Moreover,a significant correlation is found between the optical-to-X-ray spectral index and radio loudness,and there is a unified multi-correlation between the radio and X-ray luminosities and radio loudness in radio-detected quasars.These results could be possibly explained with the corona-jet model,in which the corona and jet are directly related.

Detectability of rotation-powered pulsars in future hard X-ray surveys

Recent INTEGRAL/IBIS hard X-ray surveys have detected about 10 young pulsars. We show hard X-ray properties of these 10 young pulsars, which have a luminosity of 10^33 - 10^37 erg s^-1 and a photon index of 1.6-2.1 in the energy range of 20-100 keV. The correlation between X-ray luminosity and spin-down power of Lx ∝ Lsd^1.31 suggests that the hard X-ray emission in rotation-powered pulsars is dominated by the pulsar wind nebula （PWN） component. Assuming spectral properties are similar in 20-100keV and 2-10 keV for both the pulsar and PWN components, the hard X-ray luminosity and flux of 39 known young X-ray pulsars and 8 millisecond pulsars are obtained, and a correlation of Lx ∝ Lsd^1.5 is derived. About 20 known young X-ray pulsars and 1 millisecond pulsars could be detected with future INTEGRAL and HXMT surveys. We also carry out Monte Carlo simulations of hard X-ray pulsars in the Galaxy and the Gould Belt, assuming values for the pulsar birth rate, initial position, proper motion velocity, period, and magnetic field distribution and evolution based on observational statistics and the Lx - Lsd reltions：Lx ∝Lsd^1.31 and Lx∝Lsd^1.5 More than 40 young pulsars （mostly in the Galactic plane） could be detected after ten years of INTEGRAL surveys and the launch of HXMT. So, the young pulsars would be a significant part of the hard X-ray source population in the sky, and will contribute to unidentified hard X-ray sources in present and future hard X-ray surveys by INTEGRAL and HXMT.