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.

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 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.

Effect of laser focus in two-color synthesized waveform on generation of soft x-ray high harmonics

Synthesis of multi-color laser pulses has been developed as a promising way to improve low conversion efficiency of high-order harmonic generation(HHG). Here we systematically study the effect of laser focus in a two-color waveform on generation of macroscopic HHG in soft x-rays. We find that the dependence of HHG yields on laser focus at low or high gas pressure is sensitive to the characteristics of single-atom harmonic response, in which “short”-or “long”-trajectory emissions can be selectively controlled by changing the waveform of two-color synthesized laser pulse. We uncover the phase-matching mechanism of HHG in the gas medium by examining the propagation of the two-color waveform and the evolution of time-frequency emissions of high-harmonic field. We further reveal that the nonlinear effects, such as geometric phase, atomic dispersion, and plasma defocusing, are responsible for modification of two-color waveform upon propagation. This work can be used to find better macroscopic conditions for generating soft x-ray HHG by employing two-color optimized waveforms.

Scaling Laws for Plasma Focus Machines from Numerical Experiments

Numerical experiments carried out systematically using the Lee Model code unveil insightful and practical wide-ranging scaling laws for plasma focus machines for nuclear fusion energy as well as other applications. An essential feature of the numerical experiments is the fitting of a measured current waveform to the computed waveform to calibrate the model for the particular machine, thus providing a reliable and rigorous determination of the all-important pinch current. The thermodynamics and radiation properties of the resulting plasma are then reliably determined. This paper provides an overview of the recently published scaling laws for neutron (Yn) and neon soft x-ray, SXR (Ysxr) yields: Yn = 3.2x1011 Ipinch4.5;Yn = 1.8x1010 Ipeak3.8;Ipeak (0.3 to 5.7), Ipinch (0.2 to 2.4) in MA. Yn^E02.0 at tens of kJ to Yn^E00.84 at MJ level (up to 25MJ) and Ysxr = 8.3x103 Ipinch3.6;Ysxr = 6x102 Ipeak3.2;Ipeak (0.1 to 2.4), Ipinch (0.07 to1.3) in MA. Ysxr^E01.6 (kJ range) to Ysxr^E00.8 (towards MJ).

Focus on machines for high-performance

Monforts will highlight its technologies for special technical textile applications at this year’s various exhibitions around the world.From its headquarters in Monchengladbach,Germany–home also to the Advanced Technology Center which offers customers and potential customers the opportunity to carry out extensive trials in real production conditions–to its Montex factory in St Stephan,Austria,where special machines have been manufactured for more than 35 years,Monforts continues to stake its reputation on a commitment to being always helpful,accurate,fast and reliable.

Polycapillary X-ray lens for the secondary focusing Beijing synchrotron radiation source

According to intensity distribution of the synchrotron radiation source focused by a toroidal mirror at the Beijing synchrotron radiation biological macromolecule station, theoretical modeling of the Beijing synchrotron radiation source is developed for capillary optics. Using this theoretical modeling, the influences of the configuration curve of the polycapillary X-ray lens on transmission efficiency and working distance are analyzed. The experimental results of the transmission efficiency and working distance at the biological macromolecule station are in good agreement with the theoretical results.

Energy spectrum of multi-radiation of X-rays in a low energy Mather-type plasma focus device

The multi-radiation of X-rays was investigated with special attention to their energy spectrum in a Mather-type plasma focus device （operated with argon gas）. The analysis is based on the effect of anomalous resistances. To study the energy spectrum, a four-channel diode X-ray spectrometer was used along with a special set of filters. The filters were suitable for detection of medium range X-rays as well as hard X-rays with energy exceeding 30 keV. The results indicate that the anomalous resistivity effect during the post pinch phase may cause multi-radiation of X-rays with a total duration of 300 ± 50 ns. The significant contribution of Cu-Kα was due to the medium range X-rays, nonetheless, hard X-rays with energies greater than 15 keV also participate in the process. The total emitted X-ray energy in the forms of Cu-K and Cu-K/3 was around 0.14 ± 0.02 （J/Sr） and 0.04 ±0.01 （J/Sr）, respectively. The total energy of the emitted hard X-ray （〉 15 keV） was around 0.12± 0.02 （J/Sr）.

Electron Temperature Measurement Using PIN Diodes as Detectors to Record the X-ray Pulses from a Low-Energy Mather-Type Plasma Focus

In the experiment to determine the plasma electron temperature, a modifiedmultichannel PIN diodes assembly is used as detectors to record the X-ray pulses from a low-energyMather-type plasma focus device energized by a 32μF, 15 kV (3.6kJ) single capacitor, with deuteriumas a filling gas. The ratio of the integrated bremsstrahlung emission transmitting through foils tothe total incident flux as a function of foil thickness at various temperatures is obtained forfoil absorbers of material. Using 3 μm, 6 μm, 9 μm,12 μm,15 μm and 18 μm thick aluminiumabsorbers, the transmitted X-ray flux is detected. By comparing the experimental and theoreticalcurves through a computer program, the plasma electron temperature is determined. Results show thatthe deuterium focus plasma electron temperature is about 800 eV.

Low Energy Plasma Focus as an Intense X-ray Source for Radiography

Study on X-ray emission from a low energy (1.8 kJ) plasma focus devicepowered by a 9 μF capacitor bank, charged at 20 kV and giving peak discharge current of about 175kA by using a lead-inserted copper-tapered anode is reported. The X-ray yield in different energywindows is measured as a function of hydrogen filling pressure. The maximum yield in 4π-geometry isfound to be (27.3+-1.1) J and corresponding wall plug efficiency for X-ray generation is 1.52+-0.06%. X-ray emission, presumably due to bombarding activity of electrons in current sheath at theanode tip was dominant, which is confirmed by the pinhole images. The feasibility of the device asan intense X-ray source for radiography is demonstrated.

Investigation on the hydrodynamics of slab x-ray laser plasma by nonuiform line focused laser irradiation

Based on the two-dimensional model, this paper compares the hydrodynamics of slab x-ray laser plasma produced by different nonuniform line focused irradiations. It finds that the average intensity and the duration of laser pulse and the overall shape of the intensity distribution in the focal line have different influences on the plasma. Calculations show that the evolution of temperature variation is more sensitive to the pulse duration and the electron density variation is more sensitive to the pulse intensity. Pulses with duration of 200 ps to 500 ps and with intensity of 0.2 TW/cm2 to 1.0 TW/cm2 are proved acceptable in slab x-ray lasers.

The Study Internal Structure of the Annual Layers in Lake Sediments Using Synchrotron Radiation with X-ray Focusing Optics

1 Introduction Annually laminated(varve)sedimentary deposits are considered as one of the most important archives,since they offer precise temporal information(years)in combination with high time resolutions.Bottom sediments of the lakes contain detailed geochemical information on

Focusing high-energy x-rays by a P MMA compound x-ray lens on Beijing synchrotron radiation facility

The x-ray compound lens is a novel refractive x-ray optical device. This paper reports the authors＇ recent research on a polymethyl methacrylate （PMMA） compound x-ray lens. Firstly the designing and LIGA fabrication process for the PMMA compound x-ray lens are briefly described. Then, a method for theoretical analysis, as well as the experimental system for measurement is also introduced. Finally, the focusing spots for 8keV monochromatic x-rays by the PMMA compound x-ray lens are measured and analysed. According to the experimental results, it is concluded that the PMMA compound x-ray lens promises a good focusing performance under the high-energy x-rays.

In-Orbit Calibration and Data Analysis of China＇s First Grazing Incidence Focusing X-Ray Pulsar Telescope

The grazing incidence focusing X-ray pulsar telescope(iFXPT), as the main payload of the X-ray Pulsar Navigation Test Satellite(XPNAV-1), will have great significance on China's space scientific exploration and X-ray pulsar navigation. With PSR B0531+21(Crab Pulsar) as the observation target, the pulsar profile has been recovered based on the data obtained by iFXPT, realizing the main objective of "observing" PSR B0531+21 for the first time in China. This payload mainly consists of the Wolter-I X-ray optics, silicon drift detector, magnetic deflector, electronics, high-energy particle shield and high-stability structures. Currently, the iFXPT, with its good in-orbit performance, has obtained a considerable observation data. The effective area, sensitivity and energy response have been calibrated both on ground and in-orbit, demonstrating a high degree of consistency. Meanwhile, the in-orbit observation data and information for pulsar navigation has also been analyzed simultaneously. As a result, the feasibility of the exploration scheme and the performance of the telescope have been fully validated.

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.

电子束曝光机子系统光柱控制器设计

为了提高电子束曝光机光柱控制器稳定性和控制精度,通过多年研究提出了一套功能较为完备的新型光柱控制的硬件设计方案,通过对电子束聚焦、对中、偏转、扫描曝光等参数精准控制,将图形发生器产生各项数字信号变换成对应的模拟量,稳定地控制电子束对承放在激光工作台上的掩模基片进行扫描曝光,从而达到高质量生产。

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.

Annealing treatment of focused gallium ion beam processing of SERS gold substrate

Raman spectroscopy is a type of inelastic scattering spectroscopy that is widely used in determining and analyzing molecular structure.It also has a number of practical applications in evaluating food safety,medicine,and forensics.The Raman spectral signal is weak,but the development of the surface-enhanced Raman scattering(SERS)technique has overcome this problem and led to further developments in Raman spectroscopy.This paper describes a fundamental study of the use of focused ion beam(FIB)direct writing for preparing gold substrates for SERS.Molecular dynamics and Monte Carlo simulation methods are used to investigate the damage induced by gallium ion implantation of a gold substrate.Based on characterization by x-ray photoelectron spectroscopy(XPS)and scanning electron microscopy,the mechanism by which ion implantation and annealing influence the damage induced by a gallium FIB is analyzed.After annealing at 350 XC,a mixture of metallic gallium,its oxide Ga2O3 conforming to the stoichiometric ratio,and its sub-stable oxide(Ga2Ox)in sub-stoichiometric ratio precipitated on the surface are detected by XPS.Annealing treatment can effectively reduce the effect of gallium ion implantation on a SERS substrate fabricated by FIB direct writing.

Role of Ion Beam Irradiation and Annealing Efect on the Deposition of AlON Nanolayers by Using Plasma Focus Device

AlON nanolayers are synthesized on Al substrate by the irradiation of energetic nitrogen ions using plasma focusing. Samples are exposed to multiple （5, 10, 15, 20 and 25） focus shots. Ion energy and ion number density range from 80 keV to 1.4 MeV and 5.6×10^19 m^- 3 to 1.3×10^19 m ^-3, respectively. Moreover, the effect of continuous annealing （473 K and 523 K） on an AlN surface layer synthesized with 25 focus shots is also examined. The main features of the X-ray diffraction （XRD） patterns with increasing focus shots are： （i） variation in the crystallinity of AlN along （111）, （200） and （311） planes, （ii） increasing average crystallite size of AlN （111） plane, and （iii） stress relaxation observed in AlN （111） and （200） planes. The crystallinity of AlN surface layer is comparatively better at 473 K annealing temperature. A broadened diffraction peak related to an aluminium oxide phase showing weak crystallinity is observed for 15 focus shots while non-bounded oxides are present in all other deposited layers. Raman and Fourier transform infrared spectroscopy （FTIR） analysis confirm the presence of AlN and Al203 for the surface layer annealed at 473 K temperature. Raman analysis shows that the overlapping of AlN and Al2Oa results in the development of residual stresses. Scanning electron microscope （SEM） results demonstrate that the formation of rounded grains （range from 20 nm to 200 nm） and variations in their microstructures features depend on the increasing number of focus shots. Decomposition of larger clusters into smaller ones is observed.