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 proce...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.展开更多
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 mo...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.展开更多
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 ea...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).展开更多
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 ...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.展开更多
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 ke...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.展开更多
Objective:To compare the sensitivity and specificity of direct fecal smear microscopy,culture,and polymerase chain reaction in the detection of Blastocystis sp.in human stool.Methods:Human stool samples were collected...Objective:To compare the sensitivity and specificity of direct fecal smear microscopy,culture,and polymerase chain reaction in the detection of Blastocystis sp.in human stool.Methods:Human stool samples were collected from a community in San Isidro,Rodriguez,Rizal,Philippines.These samples were subjected to direct fecal smear microscopy,culture and polymerase chain reaction to detect the presence of Blastocystis sp.Results:Of the 110 stool samples collected,28(25%)were detected positive for the presence of Blastocystis sp.by two or more tests.Culture method detected the highest number of Blastocystis-positive stool samples(n=36),followed by PCR of DNA extracted from culture(n=26),PCR of DNA extracted from stool(n=10),and direct fecal smear(n=9).Compared to culture,the sensitivity of the other detection methods were 66.7%for PCR from culture and 19.4%for both PCR from stool and direct fecal smear.Specificity of the methods was high,with PCR from culture and direct fecal smear having97.3%,while PCR from stool at 95.9%.Conclusions:In this study,in vitro culture is the best method for detecting Blastocystis sp.in human stool samples.展开更多
Damage smear method(DSM)is adopted to study trans-scale progressive rock failure process,based on statistical meso-damage model and finite element solver.The statistical approach is utilized to reflect the mesoscopic ...Damage smear method(DSM)is adopted to study trans-scale progressive rock failure process,based on statistical meso-damage model and finite element solver.The statistical approach is utilized to reflect the mesoscopic rock heterogeneity.The constitutive law of representative volume element(RVE)is established according to continuum damage mechanics in which double-damage criterion is considered.The damage evolution and accumulation of RVEs are used to reveal the macroscopic rock failure characteristics.Each single RVE will be represented by one unique element.The initiation,propagation and coalescence of meso-to macro-cracks are captured by smearing failed elements.The above ideas are formulated into the framework of the DSM and programed into self-developed rock failure process analysis(RFPA)software.Two laboratory-scale examples are conducted and the well-known engineering-scale tests,i.e.Atomic Energy of Canada Limited’s(AECL’s)Underground Research Laboratory(URL)tests,are used for verification.It shows that the simulation results match with other experimental results and field observations.展开更多
BACKGROUND Endoscopic ultrasonography-guided fine-needle aspiration(EUS-FNA)is a safe and accurate technique to confirm the diagnosis of pancreatic cancers.Recently,numerous studies comparing the diagnostic efficacy o...BACKGROUND Endoscopic ultrasonography-guided fine-needle aspiration(EUS-FNA)is a safe and accurate technique to confirm the diagnosis of pancreatic cancers.Recently,numerous studies comparing the diagnostic efficacy of smear cytology(SC)and liquid-based cytology(LBC)for pancreatic lesions yielded mixed results.AIM To compare and identify the better cytology method for EUS-FNA in pancreatic lesions.METHODS A comprehensive search of PubMed,Embase,and Cochrane was undertaken through July 18,2020.The primary endpoint was diagnostic accuracy(sensitivity and specificity).Secondary outcomes included sample adequacy and post procedure complications.In addition,factors affecting diagnostic efficacy were discussed.RESULTS Data on a total of 1121 comparisons from 10 studies met the inclusion criteria.Pooled rates of sensitivity for SC and LBC were 78%(67%-87%)vs 75%(67%-81%),respectively.In any case,both SC and LBC exhibited a high specificity close to 100%.Inadequate samples more often appeared in LBC compared with SC.However,the LBC samples exhibited a better visual field than SC.Very few post procedure complications were observed.CONCLUSION Our data suggested that for EUS-FNA in pancreatic lesions(particularly solid lesions),SC with Rapid On-Site Evaluation represents a superior diagnostic technique.If Rapid On-Site Evaluation is unavailable,LBC may replace smears.The diagnostic accuracy of LBC depends on different LBC techniques.展开更多
基金The authors wish to acknowledge the generous financial sup-port from the Singapore Maritime Institute(SMI)for this research within the project‘Evaluation of In-situ Consolidation of Dredged and Excavated Materials at Reclaimed Next Generation Tuas Port’(Project ID:SMI-2018-MA-01).
文摘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.
基金This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281768DSR01.
文摘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.
基金This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281768DSR01.
文摘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).
文摘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.
文摘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.
基金supported by a research grant from the Office of the Vice-Chancellor for Research and Development,University of the Philippines-Diliman(Grant No.101007 PNSE)to W.L.R.and H.J.S
文摘Objective:To compare the sensitivity and specificity of direct fecal smear microscopy,culture,and polymerase chain reaction in the detection of Blastocystis sp.in human stool.Methods:Human stool samples were collected from a community in San Isidro,Rodriguez,Rizal,Philippines.These samples were subjected to direct fecal smear microscopy,culture and polymerase chain reaction to detect the presence of Blastocystis sp.Results:Of the 110 stool samples collected,28(25%)were detected positive for the presence of Blastocystis sp.by two or more tests.Culture method detected the highest number of Blastocystis-positive stool samples(n=36),followed by PCR of DNA extracted from culture(n=26),PCR of DNA extracted from stool(n=10),and direct fecal smear(n=9).Compared to culture,the sensitivity of the other detection methods were 66.7%for PCR from culture and 19.4%for both PCR from stool and direct fecal smear.Specificity of the methods was high,with PCR from culture and direct fecal smear having97.3%,while PCR from stool at 95.9%.Conclusions:In this study,in vitro culture is the best method for detecting Blastocystis sp.in human stool samples.
基金supported in part by the National Natural Science Foundation of China (Grant Nos.51679028 and 51879034)Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Grant No. SKLGDUEK1804)the Fundamental Research Funds for the Central Universities (Grant No.DUT18JC10)
文摘Damage smear method(DSM)is adopted to study trans-scale progressive rock failure process,based on statistical meso-damage model and finite element solver.The statistical approach is utilized to reflect the mesoscopic rock heterogeneity.The constitutive law of representative volume element(RVE)is established according to continuum damage mechanics in which double-damage criterion is considered.The damage evolution and accumulation of RVEs are used to reveal the macroscopic rock failure characteristics.Each single RVE will be represented by one unique element.The initiation,propagation and coalescence of meso-to macro-cracks are captured by smearing failed elements.The above ideas are formulated into the framework of the DSM and programed into self-developed rock failure process analysis(RFPA)software.Two laboratory-scale examples are conducted and the well-known engineering-scale tests,i.e.Atomic Energy of Canada Limited’s(AECL’s)Underground Research Laboratory(URL)tests,are used for verification.It shows that the simulation results match with other experimental results and field observations.
文摘BACKGROUND Endoscopic ultrasonography-guided fine-needle aspiration(EUS-FNA)is a safe and accurate technique to confirm the diagnosis of pancreatic cancers.Recently,numerous studies comparing the diagnostic efficacy of smear cytology(SC)and liquid-based cytology(LBC)for pancreatic lesions yielded mixed results.AIM To compare and identify the better cytology method for EUS-FNA in pancreatic lesions.METHODS A comprehensive search of PubMed,Embase,and Cochrane was undertaken through July 18,2020.The primary endpoint was diagnostic accuracy(sensitivity and specificity).Secondary outcomes included sample adequacy and post procedure complications.In addition,factors affecting diagnostic efficacy were discussed.RESULTS Data on a total of 1121 comparisons from 10 studies met the inclusion criteria.Pooled rates of sensitivity for SC and LBC were 78%(67%-87%)vs 75%(67%-81%),respectively.In any case,both SC and LBC exhibited a high specificity close to 100%.Inadequate samples more often appeared in LBC compared with SC.However,the LBC samples exhibited a better visual field than SC.Very few post procedure complications were observed.CONCLUSION Our data suggested that for EUS-FNA in pancreatic lesions(particularly solid lesions),SC with Rapid On-Site Evaluation represents a superior diagnostic technique.If Rapid On-Site Evaluation is unavailable,LBC may replace smears.The diagnostic accuracy of LBC depends on different LBC techniques.
