Investigation of Peak Separation for X-ray Diffraction Profiles of Spinodal Decomposition by a Kind of Optimized Voigt Function

The intensity and position of sidebands (satellites) on both sides of main diffraction peak in a great number of X-ray diffraction profiles of alloys always change with progress of aging. The sidebands position is determined by a newly optimized Voigt function in present investigation. Furthermore, for Cu-4 wt pet Ti alloy aged at 400℃ for 720 min and 1080 min, after introducing the weight factor of above two satellites intensity, the relative error between the fitting curves and X-ray diffraction profiles is less than 0.185%, which is more precise than the previously calculating result.

A review of intelligent ore sorting technology and equipment development

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.

Enhanced H_(2) permeation and CO_(2) tolerance of self-assembled ceramic-metal-ceramic BZCYYb-Ni-CeO_(2) hybrid membrane for hydrogen separation

Perovskite-type mixed protonic-electronic conducting membranes have attracted attention because of their ability to separate and purify hydrogen from a mixture of gases generated by industrial-scale steam reforming based on an ion diffusion mechanism.Exploring cost-effective membrane materials that can achieve both high H_(2) permeability and strong CO_(2)-tolerant chemical stability has been a major challenge for industrial applications.Herein,we constructed a triple phase(ceramic-metal-ceramic)membrane composed of a perovskite ceramic phase BaZr_(0.1)Ce_(0.7)Y_(0.1)Yb_(0.1)O_(3-δ)(BZCYYb),Ni metal phase and a fluorite ceramic phase CeO_(2).Under H_(2) atmosphere,Ni metal in-situ exsolved from the oxide grains,and decorated the grain surface and boundary,thus the electronic conductivity and hydrogen separation performance can be promoted.The BZCYYbNi-CeO_(2)hybrid membrane achieved an exceptional hydrogen separation performance of 0.53 mL min^(-1)cm^(-2) at 800℃ under a 10 vol% H_(2) atmosphere,surpassing all other perovskite membranes reported to date.Furthermore,the CeO_(2) phase incorporated into the BZCYYb-Ni effectively improved the CO_(2)-tolerant chemical stability.The BZCYYbNi-CeO_(2) membrane exhibited outstanding long-term stability for at least 80 h at 700℃ under 10 vol%CO_(2)-10 vol%H_(2).The success of hybrid membrane construction creates a new direction for simultaneously improving their hydrogen separation performance and CO_(2) resistance stability.

Phase separation in Sr doped BiMnO_3

Phase separation in Sr doped BiMnO3 （Bil_xSrxMnO3, x = 0.4-0.6） was studied by means of temperature-dependent high-resolution neutron powder diffraction （NPD）, high resolution X-ray powder diffraction （XRD）, and physical property measurements. All the experiments indicate that a phase separation occurs at the temperature coinciding with the reported charge ordering temperature （Tco） in the literature. Below the reported TCO, both the phases resulting from the phase separation crystallize in the orthorhombically distorted perovskite structure with space group Imma. At lower temperature, these two phases order in the CE-type antiferromagnetic structure and the A-type antiferromagnetic structure, respectively. However, a scrutiny of the high-resolution NPD and XRD data at different temperatures and the electron diffraction exper- iment at 300 K did not manifest any evidence of a long-range charge ordering （CO） in our investigated samples, suggesting that the anomalies of physical properties such as magnetization, electric transport, and lattice parameters at the TCO might be caused by the phase separation rather than by a CO transition.

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.

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.

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019(COVID-19).The usage of sophisticated artificial intelligence technology(AI)and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages.In this research,the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia,reported COVID-19 disease,and normal cases.The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures.Transfer Learning technique has been implemented in this work.Transfer learning is an ambitious task,but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images.The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection.Since all diagnostic measures show failure levels that pose questions,the scientific profession should determine the probability of integration of X-rays with the clinical treatment,utilizing the results.The proposed model achieved 96.73%accuracy outperforming the ResNet50 and traditional Resnet18 models.Based on our findings,the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.

Development and prospect of separated zone oil production technology

This article outlines the development of separated zone oil production in foreign countries,and details its development in China.According to the development process,production needs,technical characteristics and adaptability of oilfields in China,the development of separate zone oil production technology is divided into four stages:flowing well zonal oil production,mechanical recovery and water blocking,hydraulically adjustable zonal oil production,and intelligent zonal production.The principles,construction processes,adaptability,advantages and disadvantages of the technology are introduced in detail.Based on the actual production situation of the oilfields in China at present,three development directions of the technology are proposed.First,the real-time monitoring and adjustment level of separated zone oil production needs to be improved by developing downhole sensor technology and two-way communication technology between ground and downhole and enhancing full life cycle service capability and adaptability to horizontal wells.Second,an integrated platform of zonal oil production and management should be built using a digital artificial lifting system.Third,integration of injection and production should be implemented through large-scale application of zonal oil production and zonal water injection to improve matching and adjustment level between the injection and production parameters,thus making the development adjustment from"lag control"to"real-time optimization"and improving the development effect.

VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

The coronavirus disease 2019(COVID-19)pandemic has had a devastating impact on the health and welfare of the global population.A key measure to combat COVID-19 has been the effective screening of infected patients.A vital screening process is the chest radiograph.Initial studies have shown irregularities in the chest radiographs of COVID-19 patients.The use of the chest X-ray(CXR),a leading diagnostic technique,has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases.This study introduces a dilated bi-branched convoluted neural network(CNN)architecture,VGG-COVIDNet,to detect COVID-19 cases from CXR images.The front end of the VGG-COVIDNet consists of the first 10 layers of VGG-16,where the convolutional layers in these layers are reduced to two to minimize latency during the training phase.The last two branches of the proposed architecture consist of dilated convolutional layers to reduce the model’s computational complexity while retaining the feature maps’spatial information.The simulation results show that the proposed architecture is superior to all the state-of-the-art architecture in accuracy and sensitivity.The proposed architecture’s accuracy and sensitivity are 96.5%and 96%,respectively,for each infection type.

Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs.Chest X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and portability.In radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer variability.Using lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is advantageous.The current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on optimization.The data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s edges.Then,the OSDL model is applied to classify the CXRs under different severity levels based on CXR data.The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work.OSDL model,applied in this study,was validated using the COVID-19 dataset.The experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

Evaluation method of coal rank based on X-ray diffraction analysis an example from SE Qinshui Basin

Based on analysis on X-ray diffraction, the metamorphic grade of coal in southeast Qinshui Basin was discussed, and a precise evaluation of coal rank through XRD analysis was made, in addition, the correlation of coal rank and vitrinite reflectance （Ro） was compared. XRD spectra of coal shows （002）-band and γ-band, and based on fitting calculation and multi-peak separation methods, the values of 2θ002 and 2θγ can be obtained, as well as corresponding intensities I002 and Iγ, consequently the coal rank can be quantized as the ratio of I002 and Iγ, that is coal rank=I002/Iγ. The research shows that the values of θ002 and θγ increase with the metamorphic grade, and a very good linear positive correlation exists between calculated Coal Rank and Ro.

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.

人工智能驱动新型界面分离材料的创制

计算机技术与人工智能的发展给新材料的开发带来了新的研究范式(第四代),也为新型界面分离材料的创制带来新的发展契机。然而,受界面分离过程机理研究和材料、基团的理化数据的局限性,人工智能在新型界面分离材料的创制中的应用仍处于初期阶段。本文为解决特定体系数据缺失的问题,构建了百万级(10^(6))的样板数据库,为大预测模型与生成模型提供了可靠、规范的示范数据基础;通过从大数据中挖掘分子结构与性质的隐藏关联,并借助分子模拟计算剖析其分子间相互作用机理,解析分子间相互作用机制和重组过程规律,进而实现高性能界面分离材料的构筑。最后,本文浅析了介尺度样板大数据、机器学习势理论、国产软件开发、有机合成、智能算法等多学科交叉在界面分离材料创制中的重要影响与发展机遇,以期推动虚拟实验室前沿发展。

矛盾体分离单元结果演绎方法及应用

一阶逻辑自动定理证明是人工智能领域重要的研究内容。为提高单元结果归结演绎效率,提出了一种新的基于多元、动态、协同的单元结果演绎方法,称为矛盾体分离单元结果演绎方法,并详细地给出了其演绎定义、演绎方法、演绎的优势分析及算法实现;提出的演绎方法允许多个子句同时参与演绎,且允许多个非单元子句参与1次单元结果演绎,能较好地处理长子句;提出的演绎算法能使用策略选定较优的子句和动态设定变元合一的复杂度,并通过回溯机制优化搜索的演绎路径。以近2年国际一阶逻辑自动定理证明器竞赛例(分别为500个)和TPTP问题库中难度系数为1的问题作为测试对象,加入了矛盾体分离单元结果演绎算法的Eprover和原始Eprover相比分别多证明了10个定理,分别能证明Eprover无法证明的17个定理和13个定理,能证明出9个其他所有证明器都无法证明难度系数为1的定理。实验结果表明,提出的矛盾体分离单元结果演绎方法能有效提高一阶逻辑自动定理证明的效率。

基于轻量化CenterNet的智能车辆目标检测算法

针对当前目标检测算法参数较多,计算量较大,导致响应速度较慢,难以推广应用于智能车辆系统的问题,提出一种改进的CenterNet目标检测算法。即应用轻量化MobileNetV3网络替换原ResNet-50网络,降低计算量;应用深度可分离的PANet替换特征增强网络,获得多尺度特征信息融合后的特征,并引入SimAM注意力机制在特征融合前强化目标特征关注度,再用SiLU激活函数替换原目标检测网络中的ReLU激活函数,增强网络学习能力。最后提出CPAN-ASFF模块对深度可分离的PANet输出多尺度特征图进行融合,提高目标检测精度。应用优化后的KITTI数据集对改进后的CenterNet目标检测算法进行训练及检测验证,结果表明:其平均准确率为80.7%,比原始Center⁃Net目标检测算法提高了12个百分点,其检测速度为65 f/s,其参数量为8.91 M,较原算法减少72.73%,改进后的算法在遮挡目标、重叠目标以及与背景相似目标的检测效果上表现更优。且在SODA10M数据集中,文中提出的算法的检测精度与速度也都优于当前主流算法。该研究对算法的优化及实验为智能车辆在实际工程中的应用奠定了技术支撑。

油水渣厨余垃圾智能分离机的设计

针对目前厨余垃圾处理中存在的不足,设计开发一种在现有家用厨余垃圾处理的基础上可以实现提高破碎率、彻底分离油、水、渣三者混合物的分离机构,通过高速双刀螺旋碾磨对厨余垃圾进行破碎,再通过变矩螺杆挤压固液混合垃圾,再利用油水密度的不同进行分离,从而提高破碎率和分离质量。

智情技术赋能的教考分离研究

随着教育现代化的推进,传统的教考制度面临教考内容不对应、评价标准不统一、学习方式应试化等弊端,极大地约束了学生创新能力和综合素质的培养。通过构建智情技术赋能的教考分离模型,分析其在题库管理、混合考试、智能阅卷、成绩分析和试卷归档等多场景中的应用,进而探析智情技术赋能的教考分离价值,从而推动教学改革和课程建设,实现师生智能化考核评价,并增强学生自主学习和个性化学习,以期为高校教考分离改革提供理论指导和实践参考,最终实现全面提高人才培养质量的目标。

膜分离技术在中药现代化中的应用:进展、难点及发展方向

综述了膜技术在中药现代化进程中包括有效成分的提取和浓缩、中药制剂的制备等方面的应用,探讨了膜技术在中药制药中面临的挑战,基于膜污染与使用寿命、规模化应用等提出了解决思路。针对目前膜技术在中药现代化中的应用多数仍处于实验探索研究阶段这一现状,从膜材料创新等角度结合可持续发展、绿色制药等理念对未来的发展方向作了展望。

旋风分离器管屏智能焊接设备的开发与应用

针对目前旋风分离器管屏角焊缝采用机械龙门焊或手工焊接,存在焊缝质量差、焊接飞溅多和焊工作业环境差等问题,设计开发了一种旋风分离器管屏机器人智能焊接设备,可实现对管屏角焊缝的自动定位、自动起弧、自动调整焊枪姿态、自动焊接的全过程智能化控制。对其硬件系统、软件系统和辅助装置进行了介绍,并设计了管屏焊接工艺流程。焊接工艺试验和应用结果表明,设备运行稳定,激光寻位准确,焊缝成形良好,焊接飞溅少,烟尘处理效果好,焊角尺寸满足标准要求,焊接速度可达800 mm/min以上,焊接综合效率提高40%以上。旋风分离器管屏智能焊接设备实现了管屏角焊缝的自动化焊接,提高了焊接效率,降低了劳动强度,改善了作业环境。

基于前后端分离模式的船舶智能机舱系统软件设计

为降低智能机舱系统前后端的耦合度,提高系统开发效率,在B/S(Browser/Server)架构的基础上,采用前后端分离的开发模式确定系统软件的技术框架,明确前端主要界面和后端功能模块的划分,设计一种基于前后端分离模式的智能机舱系统软件。测试结果表明,该系统软件能实现机舱智能管理,满足用户的需求,具有较强的可靠性和稳定性。