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.

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.

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.

An Open CAM System for Dentistry on the Basis of China-made 5-axis Simultaneous Contouring CNC Machine Tool and Industrial CAM Software

China-made 5-axis simultaneous contouring CNC machine tool and domestically developed industrial computer-aided manufacture （CAM） technology were used for full crown fabrication and measurement of crown accuracy, with an attempt to establish an open CAM system for dental processing and to promote the introduction of domestic dental computer-aided design （CAD）/CAM system. Commercially available scanning equipment was used to make a basic digital tooth model after preparation of crown, and CAD software that comes with the scanning device was employed to design the crown by using domestic industrial CAM software to process the crown data in order to generate a solid model for machining purpose, and then China-made 5-axis simultaneous contouring CNC machine tool was used to complete machining of the whole crown and the internal accuracy of the crown internal was measured by using 3D-MicroCT. The results showed that China-made 5-axis simultaneous contouring CNC machine tool in combination with domestic industrial CAM technology can be used for crown making and the crown was well positioned in die. The internal accuracy was successfully measured by using 3D-MicroCT. It is concluded that an open CAM system for den-tistry on the basis of China-made 5-axis simultaneous contouring CNC machine tool and domestic industrial CAM software has been established, and development of the system will promote the introduction of domestically-produced dental CAD/CAM system.

Dental Age Estimation Based on X-ray Images

Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated dental age estimation based on machine learning techniques needs more research efforts.In this paper,an automated dental age estimation is proposed using transfer learning.In the proposed approach,features are extracted using two deep neural networks namely,AlexNet and ResNet.Several classifiers are proposed to perform the classification task including decision tree,k-nearest neighbor,linear discriminant,and support vector machine.The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images.The obtained results show that the proposed approach has a promising performance.

On the Friction and Wear Behaviors of Dental Machinable Porcelain

In order to well design tribosystems of dental CAD-CAM restorations, an understanding of the tribological mechanisms of dental machinable porcelain are essential. The friction and wear behavior of new generation industrially prefabricated Cerec Vitablocs Mark II against uniform Si3N4 ball has been performed using a small amplitude reciprocating apparatus under simulating oral conditions. The loads of 10-40 N, reciprocating amplitudes of 100-500 urn, frequencies of 1-4 Hz and two lubrications (no / artificial saliva lubrication) were selected. Tests lasting up to 10 000 cycles were conducted. The results show that Cerec Vitablocs Mark II record a friction coefficient of 0.55-0.84. Artificial saliva plays a lubricant effect during wear process. Among three parameters of the test on friction coefficient and wear depth of dental machinable porcelains, the load effect is prominent. Abrasive wear is the main wear mechanism, but brittle cracks and delamination are more popular especially under unlubricated friction.

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.

State of the Art of Micro-CT Applications in Dental Research

This review highlights the recent advances in X-ray microcomputed tomography （Micro-CT） applied in dental research. It summarizes Micro-CT applications in mea- surement of enamel thickness, root canal morphology, evaluation of root canal preparation, craniofacial skeletalstructure, micro finite element modeling, dental tissue engineering, mineral density of dental hard tissues and about dental implants. Details of studies in each of these areas are highlighted along with the advantages of Micro-CT, and finally a summary of the future applications of Micro-CT in dental research is given.

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.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

乳牙机用镍钛锉联合超声荡洗在乳磨牙根管治疗中的应用效果

目的:分析乳牙机用镍钛锉联合超声荡洗在乳磨牙根管治疗中的应用效果。方法:选取2022年12月—2024年1月于青海红十字医院行乳磨牙根管治疗患儿80例作为研究对象,随机分为对照组和观察组,各40例。对照组实施传统根管治疗,观察组实施乳牙机用镍钛锉联合超声荡洗治疗。比较两组治疗效果、并发症发生率及填充效果。结果:观察组治疗总有效率高于对照组,差异有统计学意义(P=0.043);观察组并发症发生率低于对照组,差异有统计学意义(P=0.038);观察组填充成功率高于对照组,差异有统计学意义(P=0.007)。结论:乳牙机用镍钛锉联合超声荡洗在乳磨牙根管治疗中具有较好的临床效果,能够降低患儿并发症发生率,提高填充效果。

基于深度学习的全景片自动牙位标识

根据国际牙科联盟系统的全景片影像实际特征,提出一种基于参数优化的用于自动牙齿检测和标号分类的方法。运用先进的深度学习方法构建创新以及实用的三阶段牙科全景片牙齿标识方法。使用全景片图像将其分为几个阶段,以SqueezeNet的基于掩膜区域卷积神经网络作为基线模型进行特征提取过程,使用燕群优化算法进行参数优化,应用基于SoftMax分类器的牙齿预测和加权极限学习机的阶段分类模型确定牙齿编号类别标签,在图像数据集上进行评估,所提方法具有性能竞争力。

机器学习在牙体缺损修复中的应用

机器学习作为人工智能的重要分支,其算法的快速发展及应用,契合了口腔修复数智化的需求。本文基于机器学习的基本概念,综述了以往研究中机器学习算法在牙体缺损的修复体设计、牙齿比色、预备体颈缘线自动检测中的应用,并简要分析了其在牙体缺损修复领域中的应用优势及目前研究存在的问题,以期为机器学习算法在口腔修复领域的研究提供参考。

医学统计和机器学习方法在活体年龄推断中的应用

活体年龄推断研究中通常需要对大量的数据进行数理统计分析,合理的医学统计方法在数据整理和分析中发挥着重要作用,选择准确、恰当的统计方法是影响研究结果质量的关键因素之一。本文综述了活体年龄推断研究中描述性统计、差异性分析、一致性检验、多元统计分析等较为常用的医学统计方法以及浅层学习、深度学习等机器学习方法的原理和适用原则,并概括介绍了医学统计方法和机器学习方法之间的关联性和应用前景,旨在为活体年龄推断研究获得更为科学、精准的结果提供技术指引。

Characterization of the Convoluted 3D Internetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning

Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.

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.

可切削的氧化锆陶瓷牙科修复体的制备

通过控制CaO_2-Al_2O_3-SiO_2硅酸盐玻璃粉体,在1300℃低温下液相烧结获得热膨胀系数在7.19×106/℃和8.15×10-6/℃范围内,与修复体饰瓷相近,其强度在340～360MPa,韧性在2.7～3.5 MPa·mI/2,可切削性与In-Ceram相当的氧化锆陶瓷牙科修复体.

齿科可切削渗透陶瓷用低熔生物微晶玻璃

选择了1种SiO_2-MgO-Al_2O_3-K_2O-CaO-P_2O_5-F体系玻璃成分,制备了不同ZnO添加量的齿科渗透陶瓷用可切削生物微晶玻璃。结果表明,添加9%的ZnO,可使母玻璃的熔化澄清温度降至1300℃,降低了干压成型的纳米α-氧化铝多孔坯体的玻璃渗透温度;晶化后的微晶玻璃呈现出独特的球状组织特征,析出相主要为云母、氟磷灰石、假蓝宝石和辉石相等。该体系微晶玻璃兼具良好的切削性能和较高的抗弯强度。

晶化热处理制度对新型牙科云母基陶瓷弯曲强度的影响

目的:分析不同晶化温度和晶化时间对新型牙科云母玻璃陶瓷弯曲强度的影响,探讨该材料适用于牙科的最佳热处理制度。方法:将材料分为7组,采用一段晶化法,分别按照不同晶化温度/不同晶化时间进行热处理。测量其三点弯曲强度,并进行X射线衍射(XRD)及扫描电镜(SEM)观察。结果:随晶化温度升高,晶化时间延长,弯曲强度呈先上升后下降趋势,680℃晶化2h弯曲强度可达到173.68MPa。结论:该材料有优异的加工性能,是一种很有前途的牙科全瓷材料。

可切削渗透陶瓷的渗透方法及性能

目的 :探讨可切削渗透陶瓷 (MIC)的渗透方法 ,了解氧化铝基体的堆积密度对MIC性能的影响。方法 :应用In Ceram渗透技术 ,将云母微晶玻璃渗透入多孔氧化铝基体中 ,形成连续渗透复合体 ,并进行微晶化处理 ,测定 3种不同基体堆积密度MIC的物理性能。结果 :本实验的云母玻璃经 1160℃保持 6h渗透后 ,可获得具有优良性能的MIC复合体 ,基体相对堆积密度约 75%时 ,性能最佳。结论 :MIC是一种性能优良的新型渗透陶瓷材料 ,可满足齿科全瓷修复材料的技术要求 。