Modelling smear effect of vertical drains using a diameter reduction method

Vertical drains are used to accelerate consolidation of clays in ground improvement projects.Smear zones exist around these drains,where permeability is reduced due to soil disturbance caused by the installation process.Hansbo solution is widely used in practice to consider the effects of drain discharge capacity and smear on the consolidation process.In this study,a computationally efficient diameter reduction method(DRM)obtained from the Hansbo solution is proposed to consider the smear effect without the need to model the smear zone physically.Validated by analytical and numerical results,a diameter reduction factor is analytically derived to reduce the diameter of the drain,while achieving similar solutions of pore pressure dissipation profile as the classical full model of the smear zone and drain.With the DRM,the excess pore pressure u obtained from the reduced drain in the original un-disturbed soil zone is accurate enough for practical applications in numerical models.Such performance of DRM is independent of soil material property.Results also show equally accurate performance of DRM under conditions of multi-layered soils and coupled radial-vertical groundwater flow.

MayGAN:Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Leukemia,often called blood cancer,is a disease that primarily affects white blood cells(WBCs),which harms a person’s tissues and plasma.This condition may be fatal when if it is not diagnosed and recognized at an early stage.The physical technique and lab procedures for Leukaemia identification are considered time-consuming.It is crucial to use a quick and unexpected way to identify different forms of Leukaemia.Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment.Various deep-learning(DL)model-based segmentation and categorization techniques have already been introduced,although they still have certain drawbacks.In order to enhance feature extraction and classification in such a practical way,Mayfly optimization with Generative Adversarial Network(MayGAN)is introduced in this research.Furthermore,Generative Adversarial System(GAS)is integrated with Principal Component Analysis(PCA)in the feature-extracted model to classify the type of blood cancer in the data.The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia.Acute lymphocytic Leukaemia(ALL),acute myelogenous Leukaemia(AML),chronic lymphocytic Leukaemia(CLL),chronic myelogenous Leukaemia(CML),and aberrant White Blood Cancers(WBCs)are all successfully classified by the proposed MayGAN model.The proposed MayGAN identifies the abnormal activity in the WBC,considering the geometric features.Compared with the state-of-the-art methods,the proposed MayGAN achieves 99.8%accuracy,98.5%precision,99.7%recall,97.4%F1-score,and 98.5%Dice similarity coefficient(DSC).

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

In recent years,Peripheral blood smear is a generic analysis to assess the person’s health status.Manual testing of Peripheral blood smear images are difficult,time-consuming and is subject to human intervention and visual error.This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques.Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images.In order to mitigate this issue Deep Convolution Neural Network(DCNN)based automatic classification technique is introduced with the classification of eight groups of peripheral blood cells such as basophil,eosinophil,lymphocyte,monocyte,neutrophil,erythroblast,platelet,myocyte,promyocyte and metamyocyte.The proposed DCNN model employs transfer learning approach and additionally it carries three stages such as pre-processing,feature extraction and classification.Initially the pre-processing steps are incorporated to eliminate noisy contents present in the image by using Histogram Equalization(HE).It is enclosed to improve an image contrast.In order to distinguish the dissimilar class and segmentation approach is carried out with the help of Fuzzy C-Means(FCM)model whereas its centroid point optimality method with Slap Swarm based optimization strategy.Moreover some specific set of Gray Level Co-occurrence Matrix(GLCM)features of the segmented images are extracted to augment the performance of proposed detection algorithm.Finally the extracted features are recorded by DCNN and the proposed classifier has the capability to extract their own features.Based on this the diverse set of classes are classified and distinguished from qualitative abnormalities found in the image.

Cervical Cancer Prevention Challenges and Barriers to Cervical Cancer Screening and HPV Vaccinations in Ukraine and Eastern Europe

Objectives: To identify the obstacles and issues that hinder effective cancer prevention efforts in Ukraine. The study aims to provide a comprehensive assessment of the barriers to cancer prevention, including both infrastructure and behavioral factors, and identify potential solutions to address these challenges. Study Design: Comprehensive literature review. Methods: The following databases were searched: National Center for Biotechnology Information (NCBI) and PubMed (U.S. National Library of Medicine at the National Institutes of Health). The keywords used in the search included “Cervical Cancer”, “Human Papillomavirus Vaccination (HPV)”, “Ukraine”, “Eastern Europe”, “Healthcare Infrastructure in Disasters”, “Cervical Cancer Prevention”, “Pap Smear”, and “HPV Testing”. Results: A total of 3500 articles were screened. A total of 65 articles met the inclusion criteria. Limited public awareness and limited access to vaccination and screening, combined with inadequate treatment facilities lead to higher rates of cervical cancer. The COVID pandemic, war with Russia, and the Chernobyl disaster are significant factors for the low level of vaccination in Ukraine. Conclusion: The prevention and treatment of cervical cancer in Ukraine face significant challenges due to the inadequate HPV vaccination rates and screening by cytology. Efforts to improve funding and increase education of both the population and health care providers are necessary to increase interventions such as HPV vaccination, cervical cytology, and HPV testing to reduce cervical cancer rates in Ukraine.

Perceived Barriers to Cervical Cancer Screening Using Pap Smear Test among Women Attending Saad Abu Al Ella Hospital in Khartoum State, 2022

Background: Cervical cancer is the second common cancer among women worldwide. It is a preventable cancer, and early detection of precancerous conditions through the Papanicolaou cytology screening (Pap smear) is a key aspect of prevention;it is accepted worldwide as an efficient tool for secondary prevention. While the PS test is simple, inexpensive, and relatively reliable as a method of diagnosing cervical cancer, most women do not take the test. Therefore, this study is sought to describe the barriers to pap smear uptake among Sudanese women. Materials and Method: This total coverage observational, analytical and cross sectional, hospital-based study was conducted in Saad Abu El Ella Hospital in April 2022. The study was conducted using an anonymous questionnaire to assess the perceived barriers of 93 participants. All data were computerized using Microsoft Excel’17 and the data were described and analyzed using statistical package for social science (SPSS23). Results: The findings revealed that the mean age of the participants was 39.5 years and only 3.2% had ever undergone a pap smear test. Identified barriers were lack of information, not knowing where to go, and fear of pain. The majority, 72% are willing to routinely perform a pap smear test if well informed about it. The study also demonstrates that there is a significant correlation between perceived barriers score and willingness to perform the pap smear test (p value = 0.008), and between the perceived barriers score and the sociodemographic factors: Age (p value = 0.006), educational level (p value = 0.028) and occupation (p value = 0.040), but no association with the economic status was found (p value = 0.378). Conclusion: The detection rate is too low compared to the national target of over 70%. Therefore, more work is needed to reduce perceived barriers to cervical cancer screening by providing education/raising for popular awareness;addressing misconceptions and false beliefs;informing women about the necessity and importance of Pap smear;and health promotion using mass media such as national television, social media, radio, billboards, and newspapers and other print media.

Study on Fault Lateral Sealing Based on SGR Method

Bozhong A Oilfield is located in the northeast of Huanghekou Sag in the Bohai Bay Basin, and is a complex fault block oilfield. The main oil bearing horizon is the lower Minghuazhen Member, belonging to shallow water delta sedimentation. For the rolling exploration and evaluation of adjacent fault blocks, this paper uses the mudstone smear method to analyze and accurately characterize the lithological contact relationship between the two walls of the fault. Based on the establishment of different reservoir docking modes on the two walls of the fault, the sealing parameters are calculated to evaluate the sealing ability of the fault, thereby evaluating its reservoir formation characteristics, and predicting the height of the oil column. The above methods provide geological basis for further rolling expansion and potential tapping in the study area and surrounding areas, and have guiding significance for exploration and rolling potential tapping in similar blocks in the Bohai Sea.

基于联合有限元-离散元的混凝土重力坝破坏三维仿真模拟

水利工程中混凝土重力坝在地震等作用下的三维破坏形式以及破坏过程对大坝安全影响重大。本文建立了固体材料的联合有限元-离散元(Combined Finite-Discrete Element)三维数学模型,并引入Single/Smeared断裂损伤模型,模拟混凝土材料的破坏。数学模型经验证后,用于地震作用下混凝土重力坝破坏的三维数值模拟,结果表明,联合有限元-离散元方法能够有效模拟坝体的破坏模式和破坏阈值条件,且具有将坝体连续变形与不连续断裂有机结合的优点。通过一系列数值模拟工况的对比,分析了模型网格分辨率对破坏条件、破坏模式以及程序计算效率的影响,研究结果能够为大坝的抗震设计提供有价值的参考。

星图中基于小波变换的CCD传感器Smear现象消除方法

帧转移型CCD传感器在拍摄星空背景图像时,视场中高亮度恒星会产生Smear现象,从而对目标检测有严重影响.为了消除Smear现象,提出了一种星图中基于小波变换的Smear消除方法.首先根据Smear产生机理及星空图像特性,建立星图Smear问题的模型;然后使用多层二维Haar小波分解,把Smear亮线分离到低频分量及高频垂直分量中分别进行Smear消除处理;最后重构出消除Smear的图像.实验结果表明:该方法能有效去除Smear,最大限度保留图像原有信息,并可增强Smear区域弱小目标信噪比.

天文观测CCD相机中Smear效应的消除

为了有效消除帧转移型相机的Smear效应,针对其成像机理及特性,提出了一种基于背景估计的适用于序列图像中Smear效应的消除方法。该方法通过对含有Smear效应的原始图像灰度分布进行统计分析,自适应判定Smear效应的发生位置;然后,采用截尾均值滤波(ATMF)技术高精度估计Smear效应的强度;最后,通过差分的方式消除Smear效应。实验结果表明,该方法在有效保留弱小目标和背景信息的基础上,可较好地消除图像中的单个或多个Smear效应,提高了图像的视觉质量,基本满足天文观测相机的观测要求。

天文观测星图中亮线的去除方法

星空中的CCD图像由于帧转移的固有特性,当星图中有亮星出现时,会在亮星所在的行或列产生一条亮线,称为Smear效应,使覆盖区域的图像特性发生改变并对目标检测造成干扰,必须对其进行消除。本文在对Smear产生机理的分析上,提出一种有效去除亮线并保留弱小目标的方法。首先通过局部直方图Gauss拟合的方法对背景参数进行估计,利用中值滤波和多阈值的设定保留了弱小目标。最后通过亚采样方法对目标进行增强,四邻域滤波对图像进行修复,提高了图像的视觉质量。通过实验验证了该方法的有效性。

紫外CCD敏感器头部电路系统的研究

介绍了我国探月工程之嫦娥一号卫星的紫外CCD敏感器系统的CCD电路的软硬件的开发研究．紫外CCD敏感器由光学结构、 CCD及处理线路、数据处理单元及其软件组成．入射光经光学系统后,照射在 CCD敏感元件,经视频处理电路处理后形成数字图像信号．数字图像信号保存在数据存储器中,由数据处理器进行分析处理．计算得到月球的中心并转换为探测器对月姿态角,其中紫外CCD敏感器头部电路包括CCD电路、时序电路、驱动电路、视频处理电路和电源电路．核心器件CCD采用E2V公司的CCD48-20芯片,文中重点介绍该CCD的时序、驱动和Smear等难点问题．

行间转移大面阵CCD相机的Smear噪声实时去除

针对面阵CCD成像过程中产生的Smear特有噪声,以行间转移面阵CCD为例,提出了利用行间转移面阵CCD暗像元区域提取Smear的方法。介绍了行间转移面阵CCD的工作原理以及Smear产生机理,分析了Smear噪声的组成,利用相关双采样,哑像元校正等方法消除了Smear中的KTC噪声及暗电流噪声。提出了基于中值的快速均值滤波方法,消除了光粒子散粒噪声。最后,利用差分方法将滤波后的Smear从原始图像数据中减除,并采用双三次插值对消除Smear后的图像区域进行补偿。设计了以现场可编程门阵列+数字信号处理器(FPGA+DSP)为核心处理器件的硬件实时处理系统,当相机工作在最高速工况3 frame/s时,系统可在1.265 ms内完成Smear提取及滤波,消除Smear后的图像区域灰度方差减小了95.34%。经过成像实验验证,该系统集成度高,满足实时需要,彻底消除了Smear噪声。

采用帧转移CCD的Smear校正通道恢复饱和图像通道的方法研究

海洋水色遥感应用对光学遥感仪器提出了高灵敏度与高精度的要求。本文研究的可见近红外成像光谱仪采用推扫扫描的方式,从而获得了较高的灵敏度;通过对系统进行杂散光定标和校正实现高精度测量。杂散光校正算法要求图像中所有目标信号不能饱和。由于仪器的动态范围针对水色目标设置,因而大部分图像通道高亮度的云目标信号会饱和,导致杂散光校正算法无法获得较好效果。以一台基于帧转移面阵CCD的推扫式可见近红外成像光谱仪为研究对象,通过分析系统设置的CCD Smear校正通道的成像机理,论证了在不同光源下Smear通道和各图像通道间均存在线性相关性,进而提出了一种利用Smear校正通道来恢复各图像通道饱和信号的方法,为星上高光谱图像的杂散光校正提供有效的数据源,也为饱和图像恢复提供了一种较为可行的方法。

行间转移型CCD相机的Smear现象研究

行间转移型面阵CCD相机因其诸多优点而得到了广泛的使用,但它们生成的图像通常都会存在smear现象。分析了smear现象形成的机理,并提出了两种解决smear现象的方法,通过试验验证了该方法的有效性。

环境减灾二号A/B卫星高光谱成像仪CCD的Smear效应快速校正方法

环境减灾二号A/B卫星高光谱成像仪采用大面阵帧转移CCD作为成像和存储介质,以满足其高性能成像需求。然而,帧转移型CCD的Smear效应会造成干涉图像累积误差,导致干涉信息不准确,进而引起目标反演光谱失真。为了消除Smear效应影响,传统方法常利用行间信息迭代运算进行校正,导致计算耗时随着探测器面阵变大而增加。文章根据Smear效应产生的机理,推导出一种基于矩阵运算的Smear效应快速校正模型,并通过图形处理器(GPU)并行运算进行加速,以达到高效消除Smear效应的目的。试验结果表明:文章提出的方法能够很好地校正Smear效应引起的误差,同时利用GPU加速后计算速度是传统校正方法的632.4倍。

漂移扫描相机中拖尾现象快速消除方法

对同步卫星进行观测时,由于漂移扫描CCD相机的帧转移特性,若所拍摄星空中出现亮星,则会出现贯穿星图的smear拖尾现象。通过分析smear拖尾现象的成像机理,提出一种快速smear拖尾消除方法。首先,利用多项式拟合消除背景不均匀性;然后,计算并拟合星图每列灰度平均值,通过比较拟合前后的灰度平均值判断拖尾所在列;最后,通过将拖尾像素赋拟合后的灰度平均值消除拖尾。实验结果表明,该算法不但能够有效去除拖尾,减小星图背景均值差,而且其处理时间仅为常规smear拖尾去除算法的20%,验证了算法的有效性。

一种帧转移型CCD传感器Smear效应消除方法

CCD传感器在进行帧转移过程中会产生Smear效应,对航天、遥感、空间侦察应用来讲,必须对它进行消除,以避免影响目标检测。在对Smear效应产生机理以及星图成像性质进行分析的基础上,提出了一种帧转移型CCD传感器Smear效应消除方法。利用Smear效应对应行列灰度和高于其他区域的特征检测出Smear效应位置,并排除Smear效应位置处的星点,通过双三次插值方法消除Smear效应。实验证明,该方法能有效去除Smear效应。

帧转移型CCD图像去smear效应研究

作为无快门CCD的固有缺陷,Smear效应严重影响其在遥感成像探测领域的应用。通过对帧转移型CCD成像过程中smear效应产生机理的分析,提出了一种减小甚至消除smear效应的方法,即下传虚拟参考行法。利用该方法对一台成像光谱仪输出信号进行校正,很好地实现了smear效应影响的消除,从而证明该方法的可行性。同时,也证明了下传虚拟参考行法对其他任何类型CCD的适用性。

4种根管冲洗液清除根管玷污层的研究

目的评价4种根管冲洗液清除根管玷污层的效果。方法 20个无龋单根前牙随机分为4组,A组:60℃、3%次氯酸钠(Na Cl O)和MTAD冲洗;B组:60℃、3%Na Cl O和Smear Clear冲洗;C组:60℃、3%Na Cl O和17%乙二胺四乙酸(ethylene diamine tetraacetic acid,EDTA)冲洗;D组:3%双氧水(H2O2)和生理盐水(0.9%Na Cl)冲洗。扫描电镜下观察根尖1/3、根中1/3和根上1/3玷污层清除情况,按照根管玷污层清除分级计分标准进行统计学分析。结果 A、B、C、D组根上1/3区玷污层分值为1.18±0.21、1.10±0.15、1.20±0.18、3.52±0.11;根中1/3区玷污层分值为1.63±0.48、1.83±0.24、1.73±0.15、4.01±0.29;根尖1/3区玷污层分值为1.71±0.45、1.74±0.14、3.33±0.24、4.25±0.32。在根上1/3和根中1/3区A、B、C各组间差异无统计学意义(P>0.05),3组与D组差异有统计学意义(P<0.05);根尖1/3区A、B组间差异无统计学意义(P>0.05),A、B组与C、D组差异有统计学意义(P<0.05)。结论 60℃、3%Na Cl O与MTAD或Smear Clear联合使用均能有效去除根管玷污层。