Effective separation of coal gasification fine slag: Role of classification and ultrasonication in enhancing flotation

Effective separation of residual carbon and ash is the basis for the resource utilization of coal gasification fine slag(CGFS).The conventional flotation process of CGFS has the bottlenecks of low carbon recovery and high collector dosage.In order to address these issues,CGFS sample taken from Shaanxi,China was used as the study object in this paper.A new process of size classification-fine grain ultrasonic pretreatment flotation(SC-FGUF)was proposed and its separation effect was compared with that of wholegrain flotation(WGF)as well as size classification-fine grain flotation(SC-FGF).The mechanism of its enhanced separation effect was revealed through flotation kinetic fitting,flotation flow foam layer stability,particle size composition,surface morphology,pore structure,and surface chemical property analysis.The results showed that compared with WGF,pre-classification could reduce the collector dosage by 84.09%and the combination of pre-classification and ultrasonic pretreatment could increase the combustible recovery by 17.29%and up to 93.46%.The SC-FGUF process allows the ineffective adsorption of coarse residual carbon to collector during flotation stage to be reduced by pre-classification,and the tightly embedded state of fine CGFS particles is disrupted and surface oxidizing functional group occupancy was reduced by ultrasonic pretreatment,thus carbon and ash is easier to be separated in the flotation process.In addition,some of the residual carbon particles were broken down to smaller sizes in the ultrasonic pretreatment,which led to an increase in the stability of flotation flow foam layer and a decrease in the probability of detachment of residual carbon particles from the bubbles.Therefore,SCFGUF could increase the residual carbon recovery and reduce the flotation collector dosage,which is an innovative method for carbon-ash separation of CGFS with good application prospect.

Ultrasonic signal classification based on ambiguity plane feature

Ambiguity function （AF） is proposed to represent ultrasonic signal to resolve the preprocessing problem of different center frequencies and different arriving times among ultrasonic signals for feature extraction, as well as offer time-frequency features for signal classification. Moreover, Karhunen-Loeve （K-L） transform is considered to extract signal features from ambiguity plane, and then the features are presented to probabilistic neural network （PNN） for signal classification. Experimental results show that ambiguity function eliminates the difference of center frequency and arriving time existing in ultrasonic signals, and ambiguity plane features extracted by K-L transform describe the signal of different classes effectively in a reduced dimensional space. Classification result suggests that the ambiguity plane features obtain better performance than the features extracted by wavelet transform （WT）.

超声弹性成像联合超声BI-RADS评分分级在乳腺肿块良恶性诊断中的应用价值

目的探讨超声弹性成像和超声图像报告与数据系统(BI-RADS评分分级)在乳腺肿块良恶性鉴别诊断中的应用价值。方法 116例患者175个乳腺肿块术前分别行超声弹性成像评分、BI-RADS评分分级及超声弹性成像评分联合BI-RADS评分分级检查,结果与病理对照分析,以评价其对肿块的诊断价值。结果 175个乳腺肿块,术后病理诊断为恶性77个,良性98个。恶性肿块BI-RADS评分分级和超声弹性成像评分明显高于良性肿块(P<0.05);超声弹性成像评分与BI-RADS评分分级在乳腺肿块良恶性诊断中的敏感性、特异性、准确率、阳性预测值及阴性预测值间比较差异均无统计学意义;超声弹性成像评分联合BI-RADS评分分级诊断乳腺肿块良恶性与两种方法单独诊断的敏感性、特异性、准确率、阳性预测值及阴性预测值比较差异均有统计学意义(均P<0.05)。结论超声弹性成像评分与BI-RADS评分分级联合应用有助于鉴别诊断乳腺肿块的良恶性,可提高诊断准确性。

超声BI-RADS分级在社区乳腺癌筛查中的价值

目的探讨乳腺超声影像报告和数据系统（Breast Imaging Reporting and Data System，BI-RADS）在乳腺癌筛查中的应用价值。方法对2011年1月1日～2013年4月30日在我院门诊、体检中心及社区作乳腺检查的1588例女性作了BI-RADS分级，并对其中111例经手术病理证实的患者结果与BI-RADS分级进行相关分析。结果1588例女性中，BI_RADS分级：1级3例（O．19％）；2级680例（42．82％）；3级748例（47．10％）；4级132例（8．31％）；5级25例（1．57％）。手术的111例患者中良性肿块71例，恶性肿块40例，与超声BI-RADS分级密切相关，尤其分级为4级、5级的患者，超声诊断与手术病理结果的符合率分别达到76％与96％。结论超声BI-RADS分级在社区乳腺癌筛查中，不但能提高诊断的特异性，而且还可提高时乳腺恶性肿瘤诊断的敏感性，具有重要的推广应用价值。

超声应变率比值法与BI-RADS分级法在乳腺肿块诊断中的应用

目的对比超声应变率比值法(USR)与BI-RADS分级诊断标准在乳腺肿块诊断中的应用价值。方法选择广东省第二中医院2015年1月至2017年2月收治的乳腺肿块患者192例,乳腺肿块220个,术前分别给予USR及BI-RADS分级诊断,以术后病理结果为金标准计算两种方法对乳腺肿块诊断的灵敏度、特异度、准确性,并进行对比分析。结果 USR对乳腺肿块的诊断灵敏度、特异度、准确性分别为99.1%、98.2%、98.6%,均明显高于BI-RADS分级诊断的85.2%、90.2%、87.7%,差异均具有统计学意义(P<0.05)。结论 USR在乳腺肿块的诊断中具有较高的灵敏度、特异度和准确性,能够提供更多的客观信息,减少超声医师主观诊断因素影响,提高诊断的准确性。

乳腺癌超声BI-RADS分级与病理检查结果对比分析

目的探讨乳腺超声BI-RADS分级与病理检查结果在诊断乳腺癌过程中的相关性及运用价值。方法选取70例有乳腺包块患者,先对其进行超声BI-RADS分级评估,再进行术后病理检查,分析两者的相关性及运用价值。结果70例患者中被评估为5级23例,4a级17例,4b级10例,4c级13例,3级7例,BI-RADS5级和病理检查结果基本一致,BI-RADS3~4级和病理检查结果对比,差异有显著性(P<0.05)。乳腺肿块直径<2cm,纵横比<1的乳腺癌患者与良性乳腺疾病患者的鉴别诊断中,超声BI-RADS分级评估仍然有一定的困难,与病理检查结果对比差异有显著性。结论超声BI-RADS分级对乳腺癌的准确诊断具有重要价值,已逐步成为乳腺癌筛选的常规检查方法。

Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Ultrasonic vocalizations in mice: relevance for ethologic and neurodevelopmental disorders studies

Mice use ultrasonic vocalizations(USVs)to communicate each other and to convey their emotional state.USVs have been greatly characterized in specific life phases and contexts,such as mother isolation-induced USVs for pups or female-induced USVs for male mice during courtship.USVs can be acquired by means of specific tools and later analyzed on the base of both quantitative and qualitative parameters.Indeed,different ultrasonic call categories exist and have already been defined.The understanding of different calls meaning is still missing,and it will represent an essential step forward in the field of USVs.They have long been studied in the ethological context,but recently they emerged as a precious instrument to study pathologies characterized by deficits in communication,in particular neurodevelopmental disorders(NDDs),such as autism spectrum disorders.This review covers the topics of USVs characteristics in mice,contexts for USVs emission and factors that modulate their expression.A particular focus will be devoted to mouse USVs in the context of NDDs.Indeed,several NDDs murine models exist and an intense study of USVs is currently in progress,with the aim of both performing an early diagnosis and to find a pharmacological/behavioral intervention to improve patients’quality of life.

超声BI-RADS分级在乳腺癌筛查中的应用价值

目的 探讨乳腺超声影像报告和数据系统（Breast Imaging Reporting and Data System,BI-RADS）在乳腺癌筛查中的应用价值.方法 回顾性分析从化地区5.5 万名39-59 岁妇女乳腺癌普查的超声BI-RADS 分级资料.结果 0 级55 例（0.1%）,1 级24533 例（44.6%）,2 级16056 例（29.2%）,3 级13780 例（25.1%）,4 级561 例（1.02%）,5 级15 例（0.02%）;4、5 级共576 例（ 占1.04%）.乳腺癌妇女46 例,均经手术病理证实.结论 将超声BI-RADS 分级应用到乳腺癌筛查当中,不但提高诊断的特异性,而且提高对乳腺恶性肿瘤的敏感性,具有较好的推广应用价值.

超声弹性成像与彩色多普勒超声在BI-RADS 4级乳腺肿块中的诊断价值

目的探讨超声弹性成像及彩色多普勒超声在BI-RADS 4级乳腺肿块中的诊断价值。方法选取2014年1月至2015年6月抚顺市第二医院住院患者110例BI-RADS 4级乳腺肿块为研究对象,以病理结果为金标准,对所有肿块的超声弹性成像及彩色多普勒超声进行回顾性分析。结果超声弹性成像诊断敏感性为91.2%(55/61),特异性为94.2%(46/49),准确率91.8%(101/110)。彩色多普勒超声诊断敏感性为79.2%(48/61),特异性为84.2%(44/49),准确率80.9%(89/110)。组间比较诊断准确率差异有统计学意义(P<0.05)。110个肿物中,51例良性,59例恶性,部分良性病变中的声像图表现为恶性,良性肿物患者年龄明显低于恶性患者年龄。结论超声弹性成像技术诊断BI-RADS 4级乳腺肿物准确率高于彩色多普勒超声,可仍然会出现漏诊及误诊现象,在临床工作中联合应用超声弹性成像及彩色多普勒超声提高诊断率。BI-RADS 4级乳腺肿物良恶性与年龄相关,在声像图上良性肿物会有恶性肿物表现。

BI-RADS分类在乳腺肿块超声诊断中的应用

目的:探究BI-RADS分类在乳腺肿块超声诊断中临床应用。方法:抽取我院2016年9月-2017年12月期间收入乳腺肿块患者27例,所有患者均予以彩色多普勒超声检查诊断,并进行BI-RADS分类与手术治疗结果进行对照。结果:27例确诊为肿块患者中,6例纤维瘤,8例乳腺腺病,4例纤维腺瘤伴腺病,1例复杂性硬化性增生伴导管上皮增生,1例导管扩张及积乳、脂肪坏死,1例囊性增生、部分导管上皮增生。共计21例,占77.78%。侵润性导管癌5例,原位癌1例,占22.22%。2级良性人数为1例,3级为14例,4级为4例,5级为2例,恶性2级为0例,3级为1例,4级为2例,5级为3例。结论:乳腺肿块超声诊断中应用BI-RADS分类,利于疾病早期诊断,促进患者后期病情恢复,值得推广。

BI-RADS分类在超声诊断乳腺癌中的应用价值

目的探究BI-RADS分类在超声诊断乳腺癌中的应用价值。方法选取汕尾逸挥基金医院2016年1月—2017年12月间收治的58例乳腺疾病患者作为研究对象,术前对患者进行常规超声检查,予以BI-RADS分类,并进行分析,与术后病理结果进行对比。结果术后病理结果证实,良性结节24个、恶性结节34个,BI-RADS分类诊断乳腺癌的敏感性、特异性分别为91.3%、80.5%。结论超声诊断乳腺癌中,BI-RADS分类的应用具有较高的敏感性,具有较高的应用价值,值得临床广泛推广应用。

超声BI-RADS分类在乳腺导管原位癌早期检出的应用

目的探讨超声乳腺影像报告和数据系统(breast imaging repo rting and data system,BI-RADS)分类在乳腺导管原位癌(ductal carcinoma in situ,DCIS)的应用价值。方法将37例经手术证实的DCIS再次回顾性单盲读图,将声像表现、BI-RADS分类进行统计分析,并与术前超声进行对比。结果BI-RADS4类及以上的纳为真阳性(true positive,TP)患者,并认为TP检出可提高DCIS的诊断准确率,术前和术后再次回顾性读图,TP检出率为分别为80.0%及91.4%,术后再次BI-RADS分类评估准确性高于术前。结论规范化使用美国放射学会提出的乳腺影像报告和数据系统(breast imaging reporting and data system,United States,BI-RADSUS)分类标准,可提高DCIS患者的TP早期检出率,使早期乳腺癌患者获益。

超声BI-RADS分类结合弹性超声对早期乳腺癌的诊断价值分析

目的探求超声乳腺影像报告和数据系统(BI-RADS)分类结合弹性超声对早期乳腺癌的诊断价值。方法选取本院2016年8月至2018年1月收治的乳腺肿瘤患者86例,共120个肿块,所有患者均行超声BI-RADS分类、弹性超声及联合诊断,并行手术病理检查,以手术病理结果为"金标准",评估超声BI-RADS分类、弹性超声及联合诊断的诊断价值。结果本组86例患者共有120个肿块,经手术病理证实良性肿块73个,乳腺癌47个;联合诊断恶性检出率高于超声弹性成像、超声BI-RADS分类单独诊断,差异有统计学意义(P<0.05)。联合诊断灵敏度、特异度、准确度高于超声弹性成像、超声BI-RADS分类单独检测,差异有统计学意义(P<0.05)。结论超声BI-RADS分类结合弹性超声可提升早期乳腺癌诊断灵敏度、特异度、准确度,为治疗方案制定和病情监测提供重要的指导作用。

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

二维超声检查、三维超声能量成像技术和超声BI-RADS分级标准在鉴别诊断乳腺肿块良恶性中的应用价值

目的:分析二维超声检查、三维超声能量成像技术和超声乳腺影像报告-数据系统(BI-RADS)分级标准在鉴别诊断乳腺肿块良恶性中的应用价值。方法:从2015年1月至2017年5月期间在绵竹市人民医院就诊的乳腺肿块患者中选取91例患者作为研究对象。这91例患者的病情均经病理检查被确诊。对这91例患者先进行二维超声检查,用超声BI-RADS分级标准对其病情进行分级。再对这91例患者进行三维超声能量成像检查,根据检查结果对其上述病情分级诊断结果进行调整,然后分析用二维超声检查、三维超声能量成像技术和超声BI-RADS分级标准诊断乳腺肿块良恶性的准确性。结果:这91例患者共有115个乳腺肿块病灶,其中有88个良性肿瘤病灶,有27个恶性肿瘤病灶。与用二维超声检查和超声BI-RADS分级标准鉴别诊断乳腺肿块良恶性的准确率相比,用二维超声检查、三维超声能量成像技术和超声BI-RADS分级标准鉴别诊断其良恶性的准确率较高,P<0.05。结论:二维超声检查、三维超声能量成像技术和超声BI-RADS分级标准在鉴别诊断乳腺肿块良恶性中的应用价值较高。

超声BI-RADS分类评估老年乳腺占位性病变的价值

目的:分析超声BI-RADS分类标准在中老年乳腺占位病变患者术前评估的临床价值,为乳腺占位性病变患者的诊断和手术方案选择提供准确的信息,提高临床诊治质量。方法:选择2013年11月至2015年10月我院收治的中老年乳腺占位性病变患者840例作为研究对象,所有患者均采用超声BI-RADS分类标准对患者病情进行分类,所有患者均实施手术切除治疗,以手术病理诊断结果作为金标准,根据BI-RADS分类系统,将Ⅳ_a级和Ⅳ_b级假设为良性,Ⅳ_c级、Ⅴ级及Ⅵ级假设为恶性,采用四格诊断表,输入数据,计算超声BI-RADS分类方法与手术病理诊断结果的Kappa一致性及BI-RADS分类判断方法的敏感性、特异性和准确性。结果:840例患者超声BI-RADS分类结果:Ⅳ_a级:130例,Ⅳ_b级:150例,Ⅳ_c级:230例,Ⅴ级:150例,Ⅵ级:180例;手术后病理诊断结果恶性患者520例,良性320例。BI-RADS分类划分为恶性者510例,划分为良性者330例,BI-RADS分类法与手术病理诊断的Kappa一致性系数为0.891,该方法和手术病理诊断具有高度的一致性;BI-RADS分类法的敏感性96.15%、准确性96.43%、特异性96.88%。结论:超声BI-RADS分类法对乳腺占位性病变患者术前评估敏感性、准确性和特异性高,对患者手术方法的选择、整体治疗方案的选择具有良好的指导作用,作为乳腺占位性病变患者术前评估具有较高的临床价值。

超声弹性成像评分法与BI-RADS-US分类在乳腺实性肿块良恶性诊断中的临床价值

目的探讨临床诊断鉴别乳腺实性肿块良恶性采用超声弹性成像评分法、BI-RADS-US分类的应用价值。方法选择2021年5月—2022年10月本院接诊治疗乳腺实性肿块患者75例进行回顾性分析,分别接受超声弹性成像评分法、BI-RADS-US分类评估,以手术或活检穿刺结果为金标准,分别记录两种评估方法的结果,同时计算超声弹性成像评分法、BI-RADS-US分类及二者联合诊断乳腺实性肿块良恶性的灵敏度、特异性及准确率情况,进行组间比较。结果75例乳腺实性肿块患者通过手术或活检穿刺诊断结果显示,恶性肿块患者共29例,占比38.67%;良性肿块患者共46例,占比61.33%。经计算结果显示,超声弹性成像评分法联合BI-RADS-US分类评估的灵敏度为96.55%、准确率为94.67%,均明显高于单一评估方式(P<0.05)。结论临床鉴别诊断乳腺实性肿块良恶性可采取超声弹性成像评分法联合BI-RADS-US分类法,相比单一评估更具灵敏性及准确率,值得推广。

超声弹性成像联合BI-RADS分级对乳腺占位病变的鉴别诊断价值研究

目的:探究超声弹性成像(ultrasound elastography,UE)联合超声乳腺影像报告与数据系统(BI-RADS)分级在乳腺占位病变中的鉴别诊断价值。方法:选取2019年8月—2021年7月于临沂市人民医院就诊的乳腺占位性病变患者102例,均行超声检查。将病理检查结果作为诊断“金标准”,分析病理检查结果、UE、BI-RADS分级及联合检查的结果,分析UE、BI-RADS分级及联合检查的诊断价值,并计算各项检查方法与“金标准”的一致性。结果:Kappa检验显示:UE检查、BI-RADS分级与“金标准”的一致性中等(Kappa=0.583、0.584);联合检查与“金标准”结果高度一致(Kappa=0.913)。UE联合BI-RADS分级检查在乳腺占位性病变诊断中临床应用价值均优于UE、BI-RADS单项检查,差异有统计学意义(P<0.05)。结论:乳腺占位病变鉴别诊断中应用UE联合BI-RADS分级检查可获得较好的临床应用价值,其诊断结果与“金标准”一致性较强,可作为诊断如先占位病变的首选方法,为临床诊断提供有效参考,值得推广应用。