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.

Special Issue on Machine Fault Diagnostics and Prognostics

Machine components and systems, such as gears, bearings, pipes, cutting tools and turbines, may experience various types of faults, such as breakage, crack, pitting, wear, corrosion. If not being properly monitored and treated, such faults can propagate and lead to machinery perfor- mance degradation, malfunction, or even severe compo- nent/system failure. It is significant to reliably detect machinery defects, evaluate their severity, predict the fault propagation trends, and schedule optimized maintenance and inspection activities to prevent unexpected failures. Advances in these areas will support ensuring equipment and production reliability, safety, quality and productivity.

Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma(ESCC)that combines plasma metabolomics with machine learning algorithms.Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls.The dataset was split into a training set and a test set.After identification of differential metabolites in training set,single-metabolite-based receiver operating characteristic(ROC)curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls.Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites.Finally,twelve differential plasma metabolites(six up-regulated and six down-regulated)were annotated.The predictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows:arachidonic acid(accuracy:0.887),sebacic acid(accuracy:0.867),indoxyl sulfate(accuracy:0.850),phosphatidylcholine(PC)(14:0/0:0)(accuracy:0.825),deoxycholic acid(accuracy:0.773),and trimethylamine N-oxide(accuracy:0.653).The prediction accuracies of the machine learning models in the test set were partial least-square(accuracy:0.947),random forest(accuracy:0.947),gradient boosting machine(accuracy:0.960),and support vector machine(accuracy:0.980).Additionally,survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor(hazard ratio(HR):1.752),while PC(14:0/0:0)(HR:0.577)was a favorable prognostic factor for ESCC.This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC.

Metabonomic analysis of hepatitis B virus-induced liver failure:identification of potential diagnostic biomarkers by fuzzy support vector machine

Hepatitis B virus （HBV）-induced liver failure is an emergent liver disease leading to high mortality. The severity of liver failure may be reflected by the profile of some metabolites. This study assessed the potential of using metabolites as biomarkers for liver failure by identifying metabolites with good discriminative performance for its phenotype. The serum samples from 24 HBV-indueed liver failure patients and 23 healthy volunteers were collected and analyzed by gas chromatography-mass spectrometry （GC-MS） to generate metabolite profiles. The 24 patients were further grouped into two classes according to the severity of liver failure. Twenty-five eommensal peaks in all metabolite profiles were extracted, and the relative area values of these peaks were used as features for each sample. Three algorithms, F-test, k-nearest neighbor （KNN） and fuzzy support vector machine （FSVM） combined with exhaustive search （ES）, were employed to identify a subset of metabolites （biomarkers） that best predict liver failure. Based on the achieved experimental dataset, 93.62% predictive accuracy by 6 features was selected with FSVM-ES and three key metabolites, glyeerie acid, cis-aeonitie acid and citric acid, are identified as potential diagnostic biomarkers.

Genetic algorithm-optimized backpropagation neural network establishes a diagnostic prediction model for diabetic nephropathy:Combined machine learning and experimental validation in mice

Background:Diabetic nephropathy(DN)is the most common complication of type 2 diabetes mellitus and the main cause of end-stage renal disease worldwide.Diagnostic biomarkers may allow early diagnosis and treatment of DN to reduce the prevalence and delay the development of DN.Kidney biopsy is the gold standard for diagnosing DN;however,its invasive character is its primary limitation.The machine learning approach provides a non-invasive and specific criterion for diagnosing DN,although traditional machine learning algorithms need to be improved to enhance diagnostic performance.Methods:We applied high-throughput RNA sequencing to obtain the genes related to DN tubular tissues and normal tubular tissues of mice.Then machine learning algorithms,random forest,LASSO logistic regression,and principal component analysis were used to identify key genes(CES1G,CYP4A14,NDUFA4,ABCC4,ACE).Then,the genetic algorithm-optimized backpropagation neural network(GA-BPNN)was used to improve the DN diagnostic model.Results:The AUC value of the GA-BPNN model in the training dataset was 0.83,and the AUC value of the model in the validation dataset was 0.81,while the AUC values of the SVM model in the training dataset and external validation dataset were 0.756 and 0.650,respectively.Thus,this GA-BPNN gave better values than the traditional SVM model.This diagnosis model may aim for personalized diagnosis and treatment of patients with DN.Immunohistochemical staining further confirmed that the tissue and cell expression of NADH dehydrogenase(ubiquinone)1 alpha subcomplex,4-like 2(NDUFA4L2)in tubular tissue in DN mice were decreased.Conclusion:The GA-BPNN model has better accuracy than the traditional SVM model and may provide an effective tool for diagnosing DN.

On-line Condition Monitoring and Diagnostic Network System for Rotating Machine Set

Several industrial computers and a server are combined to set up the on-line monitoring and diagnostic system of turbo-generator sets. The main function of the system is to monitor machine sets' running condition. Through analyzing running data, technicians can detect whether there exist faults and where they occur. To share and transmit the dynamic information of the turbo-generator sets, a distributed network system is introduced. NetWare network operating system is used in the LAN (Local Area Network) system. The LAN is extended to realize the sharing of data and remote transmission of information. Furthermore, functions of monitoring and diagnostic clients are listed.

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.

Reconstruction of poloidal magnetic field profiles in field-reversed configurations with machine learning in laser-driven ion-beam trace probe

The diagnostic of poloidal magnetic field(B_(p))in field-reversed configuration(FRC),promising for achieving efficient plasma confinement due to its highβ,is a huge challenge because B_(p)is small and reverses around the core region.The laser-driven ion-beam trace probe(LITP)has been proven to diagnose the B_(p)profile in FRCs recently,whereas the existing iterative reconstruction approach cannot handle the measurement errors well.In this work,the machine learning approach,a fast-growing and powerful technology in automation and control,is applied to B_(p)reconstruction in FRCs based on LITP principles and it has a better performance than the previous approach.The machine learning approach achieves a more accurate reconstruction of B_(p)profile when 20%detector errors are considered,15%B_(p)fluctuation is introduced and the size of the detector is remarkably reduced.Therefore,machine learning could be a powerful support for LITP diagnosis of the magnetic field in magnetic confinement fusion devices.

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.

High-resolution x-ray monochromatic imaging for laser plasma diagnostics based on toroidal crystal

Monochromatic x-ray imaging is an essential method for plasma diagnostics related to density information.Large-field high-resolution monochromatic imaging of a He-like iron(Fe XXV)Kαcharacteristic line(6.701 keV)for laser plasma diagnostics was achieved using a developed toroidal crystal x-ray imager.A high-index crystal orientation Ge(531)wafer with a Bragg angle of 75.37°and the toroidal substrate were selected to obtain sufficient diffraction efficiency and compensate for astigmatism under oblique incidence.A precise offline assembly method of the toroidal crystal imager based on energy substitution was proposed,and a spatial resolution of 3-7μm was obtained by toroidal crystal imaging of a 600 line-pairs/inch Au grid within an object field of view larger than 1.0 mm.The toroidal crystal x-ray imager has been successfully tested via side-on backlight imaging experiments of the sinusoidal modulation target and a 1000 line-pairs/inch Au grid with a linewidth of 5μm using an online alignment method based on dual positioning balls to indicate the target and backlighter.This paper describes the optical design,adjustment method,and experimental results of a toroidal crystal system in a laboratory and laser facility.

Soft x-ray Diode-array Diagnostic System for HT-7 Superconducting Tokamak

A diagnostic system of soft x-ray diode-array was set up for HT-7 superconducting tokamak. The system consists of two slot-aperture cameras and is capable of measuring the soft x-ray emission from the plasma on HT-7 device with a high resolution in space and a high response in time. Both cameras, located separately in a horizontal port and a vertical port each with thirty-seven detectors of An-Si surface-barrier diode (SBD) can view the same toroidal cross-section of the plasma from different poloidal chords. In this paper, the structure, principle and performance of the diagnostic system are described and some experimental results observed are presented.

Self Consistent Calibration of Detectors and Sources for Hard and Soft X-Ray Diagnostics

X-Ray sources, detectors and optical components are now used in a wide range of applications. What is crucial is the absolute calibration of such devices to permit a quantitative assessment of the system under study. A new X-ray laboratory has been built in Frascati (ENEA) to develop diagnostics for nuclear fusion experiments and study applications of these X-ray techniques in other domains, like new material science, non destructive tests and so on. An in-house developed selfconsistent calibration procedure is described that permits the absolute calibration of sources (X-ray emitted fluxes) and detectors (detection efficiencies) as function of the X-ray photon energy, in the range 2 - 120 keV. The calibration procedure involves the use of an in-house developed code that also predicts the spectral response of any detector in any experimental condition that can be setup in the laboratory. The procedure has been then applied for the calibration and characterisation of gas and solid state imaging detectors, such as Medipix-2, GEM gas detector, CCD camera, Cd-Te C-MOS imager, demonstrating the versatility of the method developed here.

Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature

Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.

Diagnosis of the Argon Plasma in a PECVD Coating Machine

A Langmuir probe plasma diagnostic system was developed to measure the plasma parameters in a PECVD vacuum coating machine. The plasma was a capacitively coupled plasma （CCP） driven by a radio-frequency （RF） power supply. To avoid the disturbance of radio-frequency field on the Langmuir probe measurement, a passive compensation method was applied. This method allowed the ＇dc＇ component to be passed and measured in the probe circuit. It was found that the electron temperature in the range from 2.7 eV to 6.4 eV decreased with the increase in RF power. The measured plasma density ranged from 8×10^16 m^-3 to 0.85×10^15 m^-3 and increased with the increase in RF power.

Development of a fast electron bremsstrahlung diagnostic system based on LYSO and silicon photomultipliers during lower hybrid current drive for tokamak

A novel real time fast electron bremsstrahlung (FEB) diagnostic system based on the lutetium yttrium oxyorthosilicate scintillators (LYSO) and silicon photomultipliers (SiPM) has been developed for tokamak.The diagnostic system is dedicated to study the FEB emission in the hard x-ray (HXR) energy range between 10 and 200 keV during the lower hybrid current drive.The system consists of a detection module and three data acquisition and processing (DAP)boards.The detection module consists of annulus LYSO-SiPM detector array and a 12-channel preamplifier module.The DAP boards upload the data to the host computer for displaying and storing through PXI bus.The time and space resolutions of the system are 10 ms and 4 cm,respectively.The experimental results can show the evolution over time and the spatial distribution of FEB.This paper presents the system performance and typical discharge results.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

Machine learning research and applications in fusion plasma experiments are one of the main subjects on J-TEXT.Since 2013,various kinds of traditional machine learning,as well as deep learning methods have been applied to fusion plasma experiments.Further applications in the real-time experimental environment have proved the feasibility and effectiveness of the methods.For disruption prediction,we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation system.After years of study,nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning time.Furthermore,cross-device disruption prediction methods have obtained promising results.Interpretable analysis of the models are studied.For diagnostics data processing,efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic system.Models based on both traditional machine learning and deep learning have been applied to real-time experimental environments.The models have been cooperating with the plasma control system and other systems,to make joint decisions to further support the experiments.

MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration

BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is a malignant tumor of the hepatobiliary system with concealed onset,strong invasiveness and poor prognosis.AIM To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration.METHODS We downloaded two ICC-related human gene expression profiles from GEO database as the training group(GSE26566 and GSE32958 datasets)for difference analysis,and performed enrichment analysis on differential genes.The least absolute shrinkage and selection operator(LASSO),support vector machinerecursive feature elimination(SVM-RFE)and random forest(RF),three machine learning algorithms,were used to screen the characteristic genes.Double verification was carried out on GSE107943 and The Cancer Genome Atlas,two verification groups.Receiver operating characteristic curve and area under the curve(AUC)were used to evaluate the diagnostic efficacy of genes for ICC.CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern.Human Protein Atlas(HPA)was used to analyze the protein expression level of the target gene.RESULTS A total of 1091 differential genes were obtained in the training group.Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism,complement and coagulation cascade,bile secretion and other functions and pathways.Twentyfive characteristic genes were screened by LASSO regression,19 by SVM-RFE algorithm,and 30 by RF algorithm.Three algorithms were used in combination to determine the characteristic gene of ICC:MMP14.The verification group confirmed that the genes had a high diagnostic accuracy(AUC values of the training group and the verification group were 0.960,0.999,and 0.977,respectively).Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes,activated memory CD4 T cells,resting memory CD4 T cells,and other immune cells,and was closely related to the expression of CD200,cytotoxic T-lymphocyteassociated antigen 4,CD14,CD44,and other immune checkpoints.The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC.CONCLUSION MMP14 can be used as a disease characteristic gene of ICC,and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment,which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.

Diagnostic accuracy of thoracic imaging modalities for the detection of COVID-19

The ongoing coronavirus disease 2019(COVID-19)pandemic continues to present diagnostic challenges.The use of thoracic radiography has been studied as a method to improve the diagnostic accuracy of COVID-19.The‘Living’Cochrane Systematic Review on the diagnostic accuracy of imaging tests for COVID-19 is continuously updated as new information becomes available for study.In the most recent version,published in March 2021,a meta-analysis was done to determine the pooled sensitivity and specificity of chest X-ray(CXR)and lung ultrasound(LUS)for the diagnosis of COVID-19.CXR gave a sensitivity of 80.6%(95%CI:69.1-88.6)and a specificity of 71.5%(95%CI:59.8-80.8).LUS gave a sensitivity rate of 86.4%(95%CI:72.7-93.9)and specificity of 54.6%(95%CI:35.3-72.6).These results differed from the findings reported in the recent article in this journal where they cited the previous versions of the study in which a metaanalysis for CXR and LUS could not be performed.Additionally,the article states that COVID-19 could not be distinguished,using chest computed tomography(CT),from other respiratory diseases.However,the latest review version identifies chest CT as having a specificity of 80.0%(95%CI:74.9-84.3),which is much higher than the previous version which indicated a specificity of 61.1%(95%CI:42.3-77.1).Therefore,CXR,chest CT and LUS have the potential to be used in conjunction with other methods in the diagnosis of COVID-19.

Optimized online filter stack spectrometer for ultrashort X-ray pulses

Currently,with the advent of high-repetition-rate laser-plasma experiments,the demand for online diagnosis for the X-ray spectrum is increasing because the laser-plasma-generated X-ray spectrum is very important for characterizing electron dynamics and applications.In this study,scintillators and silicon PIN(P-type–intrinsic-N-type semiconductor)diodes were used to construct a wideband online filter stack spectrometer.The X-ray sensor and filter arrangement was optimized using a genetic algorithm to minimize the condition number of the response matrix.Consequently,the unfolding error was significantly reduced based on numerical experiments.The detector responses were quantitatively calibrated by irradiating the scintillator and PIN diode with various nuclides and comparing the measuredγ-ray peaks.A prototype 15-channel spectrometer was developed by integrating an X-ray detector with front-and back-end electronics.The prototype spectrometer could record X-ray pulse signals at a repetition rate of 1 kHz.Furthermore,an optimized spectrometer was employed to record the real-time spectra of laser-driven bremsstrahlung sources.This optimized spectrometer offers a compact solution for spectrum diagnostics of ultrashort X-ray pulses,exhibiting improved accuracy in terms of spectrum measurements and repetition rates,and could be widely used in next-generation high-repetition-rate high-power laser facilities.