BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study aims to investigate the application value of a combined machine learning(ML)based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis(MLM).AIM To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.METHODS We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023.All participants were randomly assigned to the training or validation queue in a 7:3 ratio.We first apply generalized linear regression model(GLRM)and random forest model(RFM)algorithm to construct an MLM prediction model in the training queue,and evaluate the discriminative power of the MLM prediction model using area under curve(AUC)and decision curve analysis(DCA).Then,the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.RESULTS Among the 301 patients included in the study,16.28%were ultimately diagnosed with MLM through pathological examination.Multivariate analysis showed that carcinoembryonic antigen,and magnetic resonance imaging radiomics were independent predictors of MLM.Then,the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation.The prediction performance of GLRM in the training and validation queue was 0.765[95%confidence interval(CI):0.710-0.820]and 0.767(95%CI:0.712-0.822),respectively.Compared with GLRM,RFM achieved superior performance with AUC of 0.919(95%CI:0.868-0.970)and 0.901(95%CI:0.850-0.952)in the training and validation queue,respectively.The DCA indicated that the predictive abil
The behavior of oil sunflower seeds penetrating screen holes is an important factor that affects the screening performance of oil sunflower seeds.In this study,a double-deck reverse-motion vibrating screening device for oil sunflower seed screening was designed.The force condition and motion law of the oil sunflower seeds on the screen surface were analyzed.This study compared the effect of particle filling amount of discrete element model of oil sunflower seeds on the simulation effects.The screening process was numerically simulated using the coupled Discrete Element Method and Multibody Dynamics(DEM-MBD)technique with the screening percentage of oil sunflower seeds as the index.The influence of the operating parameters of the vibrating screen on the screening effect was analyzed using a multiparameter collaborative optimization scheme.The results of this study can provide a reference for the numerical simulation of crop screening behavior and the development of screening devices.
BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance(MR)imaging(MRI)in predicting microvascular invasion(MVI)in preoperative HCC.METHODS This study included 105 patients with pathologically confirmed HCC,categorized into MVI-positive and MVI-negative groups.We employed Original Data Analysis,Principal Component Analysis,Linear Discriminant Analysis(LDA),and Non-LDA(NDA)for texture analysis using multi-parametric MR images to predict preoperative MVI.The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software,with results expressed as the misjudgment rate(MCR).RESULTS Texture analysis using multi-parametric MRI,particularly the MI+PA+F dimensionality reduction method combined with NDA discrimination,demonstrated the most effective prediction of MVI in HCC.Prediction accuracy in the pulse and equilibrium phases was 83.81%.MCRs for the combination of T2-weighted imaging(T2WI),arterial phase,portal venous phase,and equilibrium phase were 22.86%,16.19%,20.95%,and 20.95%,respectively.The area under the curve for predicting MVI positivity was 0.844,with a sensitivity of 77.19%and specificity of 91.67%.CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI,portal venous,and equilibrium phases.This study provides an objective,non-invasive method for preoperative prediction of MVI,offering a theoretical foundation for the selection of clinical therapy.
A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.
Pipe contaminant detection holds considerable importance within various industries,such as the aviation,maritime,medicine,and other pertinent fields.This capability is beneficial for forecasting equipment potential failures,ascertaining operational situations,timely maintenance,and lifespan prediction.However,the majority of existing methods operate offline,and the detectable parameters online are relatively singular.This constraint hampers real-time on-site detection and comprehensive assessments of equipment status.To address these challenges,this paper proposes a sensing method that integrates an ultrasonic unit and an electromagnetic inductive unit for the real-time detection of diverse contaminants and flow rates within a pipeline.The ultrasonic unit comprises a flexible transducer patch fabricated through micromachining technology,which can not only make installation easier but also focus the sound field.Moreover,the sensing unit incorporates three symmetrical solenoid coils.Through a comprehensive analysis of ultrasonic and induction signals,the proposed method can be used to effectively discriminate magnetic metal particles(e.g.,iron),nonmagnetic metal particles(e.g.,copper),nonmetallic particles(e.g.,ceramics),and bubbles.This inclusive categorization encompasses nearly all types of contaminants that may be present in a pipeline.Furthermore,the fluid velocity can be determined through the ultrasonic Doppler frequency shift.The efficacy of the proposed detection principle has been validated by mathematical models and finite element simulations.Various contaminants with diverse velocities were systematically tested within a 14mm diameter pipe.The experimental results demonstrate that the proposed sensor can effectively detect contaminants within the 0.5−3mm range,accurately distinguish contaminant types,and measure flow velocity.
