Large and nonlinear electric field response in a two-dimensional ferroelectric Rashba material

The achievement of electrical spin control is highly desirable.One promising strategy involves electrically mod-ulating the Rashba spin orbital coupling effect in materials.A semiconductor with high sensitivity in its Rashba constant to external electric fields holds great potential for short channel lengths in spin field-effect transistors,which is crucial for preserving spin coherence and enhancing integration density.Hence,two-dimensional(2D)Rashba semiconductors with large Rashba constants and significant electric field responses are highly desirable.Herein,by employing first-principles calculations,we design a thermodynamically stable 2D Rashba semiconductor,YSbTe_(3),which possesses an indirect band gap of 1.04 eV,a large Rashba constant of 1.54 eV·Åand a strong electric field response of up to 4.80 e·Å^(2).In particular,the Rashba constant dependence on the electric field shows an unusual nonlinear relationship.At the same time,YSbTe_(3)has been identified as a 2D ferroelectric material with a moderate polarization switching energy barrier(~0.33 eV per formula).By changing the electric polarization direction,the Rashba spin texture of YSbTe_(3)can be reversed.These out-standing properties make the ferroelectric Rashba semiconductor YSbTe_(3)quite promising for spintronic applications.

Computational Simulation of Aptamer-target Binding Mechanisms

Aptamers are a type of single-chain oligonucleotide that can combine with a specific target.Due to their simple preparation,easy modification,stable structure and reusability,aptamers have been widely applied as biochemical sensors for medicine,food safety and environmental monitoring.However,there is little research on aptamer-target binding mechanisms,which limits their application and development.Computational simulation has gained much attention for revealing aptamer-target binding mechanisms at the atomic level.This work summarizes the main simulation methods used in the mechanistic analysis of aptamer-target complexes,the characteristics of binding between aptamers and different targets(metal ions,small organic molecules,biomacromolecules,cells,bacteria and viruses),the types of aptamer-target interactions and the factors influencing their strength.It provides a reference for further use of simulations in understanding aptamer-target binding mechanisms.

Design and characterization of biodegradable Mg-Zn-Ag metallic glasses

In order to develop the Mg-Zn-Ag metallic glasses(MGs)for biodegradable implant applications,the glass formation ability(GFA)and biocompatibility of Mg-Zn-Ag alloys were investigated using a combination of the calculation of phase diagrams(CALPHAD)and experimental measurements.High GFA potentiality of two alloy series,specifically Mg_(96-x)Zn_xAg_(4)and Mg_(94-x)Zn_xAg_6(x=17,20,23,26,29,32,35),was predicted theoretically and then substantiated through experimental testing.X-ray diffraction(XRD)and differential scanning calorimetry(DSC)techniques were used to evaluate the crystallinity,GFA,and crystallization characteristics of these alloys.The results showed that compositions between Mg_(73)Zn_(23)Ag_(4)and Mg_(64)Zn_(32)Ag_(4)for Mg_(96-x)Zn_xAg_4,Mg_(66)Zn_(28)Ag_(6)and Mg_(63)Zn_(31)Ag_(6for)Mg_(94-x)Zn_xAg_(6)displayed a superior GFA.Notably,the GFA of the Mg_(96-x)Zn_xAg_(4)series was better than that of the Mg_(94-x)Zn_xAg_(6)series.Furthermore,the Mg_(70)Zn_(26)Ag_4,Mg_(74)Zn_(20)Ag_6,and Mg_(71)Zn_(23)Ag_(6)alloys showed acceptable corrosion rates,good cytocompatibility,and positive effects on cell proliferation.These characteristics make them suitable for applications in medical settings,potentially materials as biodegradable implants.

Syntheses,crystal structures,and quantum chemistry calculation of two Ni(Ⅱ)coordination polymers

Two new coordination polymers,[Ni(Hpdc)(bib)(H_(2)O)]_(n)(1)and{[Ni(bib)_(3)](ClO_(4))_(2)}_(n)(2),were prepared by mixing Ni^(2+),3,5⁃pyrazoledicarboxylic acid(H3pdc)/p⁃nitrobenzoic acid and 1,4⁃bis(imidazol⁃1⁃ylmethyl)butane(bib)by a hydrothermal method,respectively.X⁃ray crystallography reveals a 2D network constructed by six⁃coordinated Ni(Ⅱ)centers,bib,and Hpdc2-ligands in complex 1,while a 2D network is built by Ni(Ⅱ)and bib ligands in 2.Furthermore,the quantum⁃chemical calculations have been performed on‘molecular fragments’extracted from the crystal structure of 1 using the PBE0/LANL2DZ method in Gaussian 16 and the VASP program.CCDC:2343794,1;2343798,2.

Research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer

Dual-layer spectral detector CT is a new spectrum CT imaging technology based on detector being able to obtain both images similar to true plain and spectral images in one time scanning.The reconstructed multi-parameter spectral images can not only improve image quality,enhance tissue contrast,increase the visualization and detection ability of occult lesions,but also provide qualitative and quantitative analysis of the lesions,so as to provide more imaging information and multi-dimensional diagnostic basis.The research progresses of dual-layer spectral detector CT for preoperative evaluation on colorectal cancer were reviewed in this article.

Preoperative CT radiomics models for predicting composition of in vivo urinary calculi

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7∶3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A—E corresponding to group A—E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860—0.948 in training set and of 0.856—0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25%,83.79%,46.23%and 0.596 in training set,respectively,while was 80.63%,75.26%,43.48%and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

Artificial intelligence models based on non-contrast chest CT for measuring bone mineral density

Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.

Drishti Paint 3.2:a new open-source tool for both 2D and 3D segmentation

X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.

Atomic orbitals modulated dual functional bimetallic phosphides derived from MOF on MOF structure for boosting high efficient overall water splitting

The electronic modulation characteristics of efficient metal phosphide electrocatalysts can be utilized to tune the performance of oxygen evolution reaction(OER).However,improving the overall water splitting performance remains a challenging task.By building metal organic framework(MOF)on MOF heterostructures,an efficient strategy for controlling the electrical structure of MOFs was presented in this study.ZIF-67 was in-situ synthesized on MIL-88(Fe)using a two-step self-assembly method,followed by low-temperature phosphorization to ultimately synthesize FeP-CoP_(3)bimetallic phosphides.By combining atomic orbital theory and theoretical calculations(density functional theory),the results reveal the successful modulation of electronic orbitals in FeP-CoP_(3)bimetallic phosphides,which are synthesized from MOF on MOF structure.The synergistic impact of the metal center Co species and the phase conjugation of both kinds of MOFs are responsible for this regulatory phenomenon.Therefore,the catalyst demonstrates excellent properties,demonstrating HER 81 mV(η10)in a 1.0 mol L^(−1)KOH solution and OER 239 mV(η50)low overpotentials.The FeP-CoP_(3)linked dual electrode alkaline batteries,which are bifunctional electrocatalysts,have a good electrocatalytic ability and may last for 50 h.They require just 1.49 V(η50)for total water breakdown.Through this technique,the electrical structure of electrocatalysts may be altered to increase catalytic activity.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

A hybrid ventilation scheme applied to bidirectional excavation tunnel construction with a long inclined shaft

The breakage and bending of ducts result in a difficulty to cope with ventilation issues in bidirectional excavation tunnels with a long inclined shaft using a single ventilation method based on ducts.To discuss the hybrid ventilation system applied in bidirectional excavation tunnels with a long inclined shaft,this study has established a full-scale computational fluid dynamics model based on field tests,the Poly-Hexcore method,and the sliding mesh technique.The distribution of wind speed,temperature field,and CO in the tunnel are taken as indices to compare the ventilation efficiency of three ventilation systems(duct,duct-ventilation shaft,duct–ventilated shaft-axial fan).The results show that the hybrid ventilation scheme based on duct-ventilation shaft–axial fan performs the best among the three ventilation systems.Compared to the duct,the wind speed and cooling rate in the tunnel are enhanced by 7.5%–30.6%and 14.1%–17.7%,respectively,for the duct-vent shaft-axial fan condition,and the volume fractions of CO are reduced by 26.9%–73.9%.This contributes to the effective design of combined ventilation for bidirectional excavation tunnels with an inclined shaft,ultimately improving the air quality within the tunnel.

Ab initio molecular dynamics simulation reveals the influence of entropy effect on Co@BEA zeolite-catalyzed dehydrogenation of ethane

The C–H bond activation in alkane dehydrogenation reactions is a key step in determining the reaction rate.To understand the impact of entropy,we performed ab initio static and molecular dynamics free energy simulations of ethane dehydrogenation over Co@BEA zeolite at different temperatures.AIMD simulations showed that a sharp decrease in free energy barrier as temperature increased.Our analysis of the temperature dependence of activation free energies uncovered an unusual entropic effect accompanying the reaction.The unique spatial structures around the Co active site at different temperatures influenced both the extent of charge transfer in the transition state and the arrangement of 3d orbital energy levels.We provided explanations consistent with the principles of thermodynamics and statistical physics.The insights gained at the atomic level have offered a fresh interpretation of the intricate long-range interplay between local chemical reactions and extensive chemical environments.

Chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease:Correlations with blood eosinophil level

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA%)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA%)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA%of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LD grade 4,middle lobe of right lung WT grade 5,upper lobe of right lung WA grade 4,middle lobe of right lung WA grade 5 and lower lobe of left lung WA grade 3 in low EOS group were all higher than those in high EOS group(all P<0.05).Except for the upper lobe of right lung LD grade 4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P<0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA%,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Attention Mechanism-Based Method for Intrusion Target Recognition in Railway

The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.

Automated body composition analysis system based on chest CT for evaluating content of muscle and adipose

Objective To establish a body composition analysis system based on chest CT,and to observe its value for evaluating content of chest muscle and adipose.Methods T7—T8 layer CT images of 108 pneumonia patients were collected(segmented dataset),and chest CT data of 984 patients were screened from the COVID 19-CT dataset(10 cases were randomly selected as whole test dataset,the remaining 974 cases were selected as layer selection dataset).T7—T8 layer was classified based on convolutional neural network(CNN)derived networks,including ResNet,ResNeXt,MobileNet,ShuffleNet,DenseNet,EfficientNet and ConvNeXt,then the accuracy,precision,recall and specificity were used to evaluate the performance of layer selection dataset.The skeletal muscle(SM),subcutaneous adipose tissue(SAT),intermuscular adipose tissue(IMAT)and visceral adipose tissue(VAT)were segmented using classical fully CNN(FCN)derived network,including FCN,SegNet,UNet,Attention UNet,UNET++,nnUNet,UNeXt and CMUNeXt,then Dice similarity coefficient(DSC),intersection over union(IoU)and 95 Hausdorff distance(HD)were used to evaluate the performance of segmented dataset.The automatic body composition analysis system was constructed based on optimal layer selection network and segmentation network,the mean absolute error(MAE),root mean squared error(RMSE)and standard deviation(SD)of MAE were used to evaluate the performance of automatic system for testing the whole test dataset.Results The accuracy,precision,recall and specificity of DenseNet network for automatically classifying T7—T8 layer from chest CT images was 95.06%,84.83%,92.27%and 95.78%,respectively,which were all higher than those of the other layer selection networks.In segmentation of SM,SAT,IMAT and overall,DSC and IoU of UNet++network were all higher,while 95HD of UNet++network were all lower than those of the other segmentation networks.Using DenseNet as the layer selection network and UNet++as the segmentation network,MAE of the automatic body composition analysis system for predicting SM,SAT,IMAT,VAT and MAE was 27.09,6.95,6.65 and 3.35 cm 2,respectively.Conclusion The body composition analysis system based on chest CT could be used to assess content of chest muscle and adipose.Among them,the UNet++network had better segmentation performance in adipose tissue than SM.

0D-1D coupling model and 3D fluid-structure interaction model based on coronary CT angiography for displaying hemodynamic characteristics of coronary artery stenosis

Objective To observe value of 0D-1D coupling model and 3D fluid-structure interaction(FSI)model based on coronary CT angiography(CCTA)for displaying hemodynamic characteristics of coronary artery stenosis.Methods Based on CCTA data of the stenosed left anterior descending branch(LAD)in a patient with coronary heart disease,an 0D-1D coupling model and 3D FSI model were built,respectively.Then hemodynamic characteristic indexes,including the pressure,flow velocity and wall shear stress(WSS)were obtained in every 0.01 s during 1 s at 5 sampling points(i.e.sampling point 1—5)using these 2 models,respectively,and the consistencies of the results between models were evaluated with Spearman correlation coefficient r s.Results The time consuming for construction of 0D-1D coupling model and 3D FSI model was 0.033 min and 704 min,respectively.Both models showed basically distribution of the pressure,flow velocity and WSS of the stenosed LAD.For more details,the pressure at the stenosed segment of LAD and the proximal segment of stenosis were both higher,which gradually decreased at the distal segment of stenosis,and the flow velocity at the proximal segment of stenosis was in a relatively slow and uniform condition,with significantly increased flow velocity and WSS at the stenosed segment.Compared with 3D FSI model,0D-1D vascular coupling model was relatively unrefined and lack of distal flow lines when displaying blood flow velocity.For sampling point 2 at the stenosed segment of LAD,no significant consistency for pressure between 2 models was found(P=0.118),but strong consistency for the flow velocity and WSS(r s=0.730,0.807,both P<0.05).The consistencies of pressure,flow velocity and WSS between 2 models at the proximal and distal segment of stenosis,i.e.1,3—5 sampling points were week to moderate(r s=0.237—0.669,all P<0.05).Conclusion 0D-1D coupling model exhibited outstanding computational efficiency and might provide relatively reasonable results,while 3D FSI model showed higher accuracy for details and streamline when simulating LAD stenosis.

Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.

Scoring and Consequential Validity Evidence of Computer-and Paper-Based Writing Tests in Times of Change

Little is known about how the assessment modality,i.e.,computer-based(CB)and paper-based(PB)tests,affects language teachers’scorings,perceptions,and preferences and,therefore,the validity and fairness of classroom writing assessments.The present mixed-methods study used Shaw and Weir’s(2007)sociocognitive writing test validation framework to examine the scoring and consequential validity evidence of CB and PB writing tests in EFL classroom assessment in higher education.Original handwritten and word-processed texts of 38 EFL university students were transcribed to their opposite format and assessed by three language lecturers(N=456 texts,152 per teacher)to examine the scoring validity of CB and PB tests.The teachers’perceptions of text quality and preferences for assessment modality accounted for the consequential validity evidence of both tests.Findings revealed that the assessment modality impacted teachers’scorings,perceptions,and preferences.The teachers awarded higher scores to original and transcribed handwritten texts,particularly text organization and language use.The teachers’perceptions of text quality differed from their ratings,and physical,psychological,and experiential characteristics influenced their preferences for assessment modality.The results have implications for the validity and fairness of CB and PB writing tests and teachers’assessment practices.

Deep learning model based on PET/CT and combination with Cox proportional hazard model for predicting progression of lung invasive adenocarcinoma after surgery

Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.

In-situ Micro-CT analysis of deformation behavior in sandwich-structured meta-stable beta Ti−35Nb alloy

Beta Ti−35Nb sandwich-structured composites with various reinforcing layers were designed and produced using additive manufacturing(AM)to achieve a balance between light weight and high strength.The impact of reinforcing layers on the compressive deformation behavior of porous composites was investigated through micro-computed tomography(Micro-CT)and finite element method(FEM)analyses.The results indicate that the addition of reinforcement layers to sandwich structures can significantly enhance the compressive yield strength and energy absorption capacity of porous metal structures;Micro-CT in-situ observation shows that the strain of the porous structure without the reinforcing layer is concentrated in the middle region,while the strain of the porous structure with the reinforcing layer is uniformly distributed;FEM analysis reveals that the reinforcing layers can alter stress distribution and reduce stress concentration,thereby promoting uniform deformation of the porous structure.The addition of reinforcing layer increases the compressive yield strength of sandwich-structured composite materials by 124%under the condition of limited reduction of porosity,and the yield strength increases from 4.6 to 10.3 MPa.