Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone...Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone wound healing. The findings of recent studies suggest that the use of selective serotonin reuptake inhibitors(SSRIs) can reduce bone mass, precipitate osteoporotic fractures and increase the rate of dental implant failure. With 10% of Americans prescribed antidepressants, the potential of SSRIs to impair bone healing may adversely affect millions of patients’ ability to heal after sustaining trauma. Here, we investigate the effect of the SSRI sertraline on bone healing through pre-treatment with(10 mg·kg-1sertraline in drinking water, n = 26) or without(control, n = 30) SSRI followed by the creation of a 5-mm calvarial defect. Animals were randomized into three surgical groups:(a) empty/sham,(b) implanted with a DermaMatrix scaffold soak-loaded with sterile PBS or(c) DermaMatrix soak-loaded with542.5 ng BMP2. SSRI exposure continued until sacrifice in the exposed groups at 4 weeks after surgery. Sertraline exposure resulted in decreased bone healing with significant decreases in trabecular thickness, trabecular number and osteoclast dysfunction while significantly increasing mature collagen fiber formation. These findings indicate that sertraline exposure can impair bone wound healing through disruption of bone repair and regeneration while promoting or defaulting to scar formation within the defect site.展开更多
In integrated circuits, the defects associated with photolithography are assumed to be in the shape of circular discs in order to perform the estimation of yield and fault analysis. However,real defects exhibit a grea...In integrated circuits, the defects associated with photolithography are assumed to be in the shape of circular discs in order to perform the estimation of yield and fault analysis. However,real defects exhibit a great variety of shapes. In this paper,a novel yield model is presented and the critical area model of short circuit is correspondingly provided. In comparison with the circular model corrently available, the new model takes the similarity shape to an original defect, the two-dimensional distributional characteristic of defects, the feature of a layout routing and the character of yield estimation into account. As for the aspect of prediction of yield, the experimental results show that the new model may predict the yield caused by real defects more accurately than the circular model does. It is significant that the yield is accurately estimated and improved using the proposed model.展开更多
When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect pr...When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .展开更多
Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
Studies on ZnO ceramic varistors by deep heat treatment at 650–900 C are reported. The current creep time curve exhibits a peak during the continuous action of a dc biasing voltage; the forwardV-l characteristic is i...Studies on ZnO ceramic varistors by deep heat treatment at 650–900 C are reported. The current creep time curve exhibits a peak during the continuous action of a dc biasing voltage; the forwardV-l characteristic is improved rather than degraded after the action of the biasing voltage. We assume that the zinc interstitial cations Zni are out diffused rapidly and the concentration of Zni in the depletion layer is decreased rapidly during deep heat treatment; the oxygen anions O’o could be accumulated at the grain interface if the out diffusion quantity of Zni is not enough to react with the O’o; the current creep phenomenon above results from the migration of the interface O’o by the biasing voltage. We suggest an improved grain boundary defect model for the ZnO varistors by deep heat treatment, and examine the model using the experimental data of lifetime positron-annihilation spectroscopy.展开更多
The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects ba...The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.展开更多
The pile, as an important foundation style, is being used in engineering practice. Defects of different types and damages of different degrees easily occur during the process of pile construction. So, dietecting defec...The pile, as an important foundation style, is being used in engineering practice. Defects of different types and damages of different degrees easily occur during the process of pile construction. So, dietecting defects of the pile is very important. As so far, there are some difficult problems in pile defect detection. Based on stress wave theory, some of these typical difficult problems were studied through model tests. The analyses of the test results are carried out and some significant results of the low-strain method are obtained, when a pile has a gradually-decreasing crosssection part, the amplitude of the reflective signal originating from the defect is dependent on the decreasing value of the rate of crosssection β. No apparent signal reflected from the necking appeares on the velocity response curve when the value of β is less than about 3. 5 %.展开更多
Contaminated surfaces of the feedstock materials in aluminum alloy casting processes often produce various types of defects which can affect the tensile properties of the final products as well as their fatigue reliab...Contaminated surfaces of the feedstock materials in aluminum alloy casting processes often produce various types of defects which can affect the tensile properties of the final products as well as their fatigue reliabilities.Semi-solid processing takes advantage of a much higher apparent viscosity of the die cast materials by limiting the risk of oxides formed at the free surfaces to become incorporated into the casting when the material is injected into the die.Most of existing semi-solid processes that use billets as feedstock material are however tied up with a different type of contaminated surface.During the injection phase,the external-skin on the periphery of the billet,which has been in contact with air and lubricant during the transfer in the shot sleeve,can be incorporated into the casting.When subjected to a heat treatment,the lubricant is decomposed and produces lens shape porosities.This might be a cause of reject for most structural parts.To avoid this kind of defects,the paths along which the billet skin evolves must be controlled during filling.In order to investigate the possibility of skin inclusion into cast parts during injection of the billet,a two-phase finite element mixture model is employed to model the metal flow.The formation of a skin on the periphery of the billet is modeled by setting an initial solid phase concentration profile in the radial direction.Microscopic observations of the real castings show that the approach is able to model the shear layers and to predict the paths along which the"lens porosity"defects could be formed.An Arbitrary Eulerian-Lagangian(ALE) method is also investigated and appears to be very promising to follow the skin movement in the casting.展开更多
The shrinkage defect of a ductile iron casting is attributed to the volume variations occurring in solidification, which consist of liquid contraction, solidification shrinkage, graphitization expansion, and mold cavi...The shrinkage defect of a ductile iron casting is attributed to the volume variations occurring in solidification, which consist of liquid contraction, solidification shrinkage, graphitization expansion, and mold cavity enlargement. Based on this understanding, a mathematical model for predicting the shrinkage defect of the casting is developed, in which the volume variations of the casting in soli- dification are numerically simulated, especially, the mold cavity enlargement is quantitatively calculated. Moreover, the reliability of the model is verified in production and experiment.展开更多
针对金属涂层缺陷图像分割中存在特征提取能力弱和分割精度低的问题,提出了一种改进的U^(2)-Net分割模型。首先,在U型残差块(RSU)中嵌入改进的增大感受野模块(receptive field block light,RFB_l),组成新的特征提取层,增强对细节特征的...针对金属涂层缺陷图像分割中存在特征提取能力弱和分割精度低的问题,提出了一种改进的U^(2)-Net分割模型。首先,在U型残差块(RSU)中嵌入改进的增大感受野模块(receptive field block light,RFB_l),组成新的特征提取层,增强对细节特征的学习能力,解决了网络由于感受野受限造成分割精度低的问题;其次,在U^(2)-Net分割模型的解码阶段引入有效的边缘增强注意力机制(contour enhanced attention,CEA),抑制网络中的冗余特征,获取具有详细位置信息的特征注意力图,增强了边界与背景信息的差异性,从而达到更精确的分割效果。实验结果表明,该模型在两个金属涂层剥落与腐蚀数据集上的平均交并比、准确率、查准率、召回率和F_1-measure分别达到80.36%、96.29%、87.43%、84.61%和86.00%,相比于常用的SegNet、U-Net以及U^(2)-Net分割网络的性能都有较大提升。展开更多
基金supported by a grant from the Musculoskeletal Transplant Foundation (JC)the National Institute of Health, the National Institute of Aging [NIH-NIA PO1-AG036675] (ME, WDH)+4 种基金in part by the Department of Veterans Affairs (VA Merit Award BX000333, ACL 1I01CX000930-01, WDH)funded through a training grant from the National Institutes of Health National Institute of Dental and Craniofacial Research [5T32DE017551]S.H. is funded through a fellowship from the National Institutes of Health National Institute of Dental and Craniofacial Research [5F32DE02471202]supported by the National Institutes of Health National Institute of General Medicine [P30GM103331]
文摘Bone wound healing is a highly dynamic and precisely controlled process through which damaged bone undergoes repair and complete regeneration. External factors can alter this process, leading to delayed or failed bone wound healing. The findings of recent studies suggest that the use of selective serotonin reuptake inhibitors(SSRIs) can reduce bone mass, precipitate osteoporotic fractures and increase the rate of dental implant failure. With 10% of Americans prescribed antidepressants, the potential of SSRIs to impair bone healing may adversely affect millions of patients’ ability to heal after sustaining trauma. Here, we investigate the effect of the SSRI sertraline on bone healing through pre-treatment with(10 mg·kg-1sertraline in drinking water, n = 26) or without(control, n = 30) SSRI followed by the creation of a 5-mm calvarial defect. Animals were randomized into three surgical groups:(a) empty/sham,(b) implanted with a DermaMatrix scaffold soak-loaded with sterile PBS or(c) DermaMatrix soak-loaded with542.5 ng BMP2. SSRI exposure continued until sacrifice in the exposed groups at 4 weeks after surgery. Sertraline exposure resulted in decreased bone healing with significant decreases in trabecular thickness, trabecular number and osteoclast dysfunction while significantly increasing mature collagen fiber formation. These findings indicate that sertraline exposure can impair bone wound healing through disruption of bone repair and regeneration while promoting or defaulting to scar formation within the defect site.
文摘In integrated circuits, the defects associated with photolithography are assumed to be in the shape of circular discs in order to perform the estimation of yield and fault analysis. However,real defects exhibit a great variety of shapes. In this paper,a novel yield model is presented and the critical area model of short circuit is correspondingly provided. In comparison with the circular model corrently available, the new model takes the similarity shape to an original defect, the two-dimensional distributional characteristic of defects, the feature of a layout routing and the character of yield estimation into account. As for the aspect of prediction of yield, the experimental results show that the new model may predict the yield caused by real defects more accurately than the circular model does. It is significant that the yield is accurately estimated and improved using the proposed model.
文摘When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
文摘Studies on ZnO ceramic varistors by deep heat treatment at 650–900 C are reported. The current creep time curve exhibits a peak during the continuous action of a dc biasing voltage; the forwardV-l characteristic is improved rather than degraded after the action of the biasing voltage. We assume that the zinc interstitial cations Zni are out diffused rapidly and the concentration of Zni in the depletion layer is decreased rapidly during deep heat treatment; the oxygen anions O’o could be accumulated at the grain interface if the out diffusion quantity of Zni is not enough to react with the O’o; the current creep phenomenon above results from the migration of the interface O’o by the biasing voltage. We suggest an improved grain boundary defect model for the ZnO varistors by deep heat treatment, and examine the model using the experimental data of lifetime positron-annihilation spectroscopy.
基金The sustentation fund come fron China Academy of Engineering Physics 2003-421050504-12-02
文摘The importance of software residual defects and some prediction residual defects models are introduced. The problem that is not easy adapted to a general model is discussed. The model of prediction residual defects based on BBNs is proposed and the detailed processes of the approach are given.
文摘The pile, as an important foundation style, is being used in engineering practice. Defects of different types and damages of different degrees easily occur during the process of pile construction. So, dietecting defects of the pile is very important. As so far, there are some difficult problems in pile defect detection. Based on stress wave theory, some of these typical difficult problems were studied through model tests. The analyses of the test results are carried out and some significant results of the low-strain method are obtained, when a pile has a gradually-decreasing crosssection part, the amplitude of the reflective signal originating from the defect is dependent on the decreasing value of the rate of crosssection β. No apparent signal reflected from the necking appeares on the velocity response curve when the value of β is less than about 3. 5 %.
文摘Contaminated surfaces of the feedstock materials in aluminum alloy casting processes often produce various types of defects which can affect the tensile properties of the final products as well as their fatigue reliabilities.Semi-solid processing takes advantage of a much higher apparent viscosity of the die cast materials by limiting the risk of oxides formed at the free surfaces to become incorporated into the casting when the material is injected into the die.Most of existing semi-solid processes that use billets as feedstock material are however tied up with a different type of contaminated surface.During the injection phase,the external-skin on the periphery of the billet,which has been in contact with air and lubricant during the transfer in the shot sleeve,can be incorporated into the casting.When subjected to a heat treatment,the lubricant is decomposed and produces lens shape porosities.This might be a cause of reject for most structural parts.To avoid this kind of defects,the paths along which the billet skin evolves must be controlled during filling.In order to investigate the possibility of skin inclusion into cast parts during injection of the billet,a two-phase finite element mixture model is employed to model the metal flow.The formation of a skin on the periphery of the billet is modeled by setting an initial solid phase concentration profile in the radial direction.Microscopic observations of the real castings show that the approach is able to model the shear layers and to predict the paths along which the"lens porosity"defects could be formed.An Arbitrary Eulerian-Lagangian(ALE) method is also investigated and appears to be very promising to follow the skin movement in the casting.
文摘The shrinkage defect of a ductile iron casting is attributed to the volume variations occurring in solidification, which consist of liquid contraction, solidification shrinkage, graphitization expansion, and mold cavity enlargement. Based on this understanding, a mathematical model for predicting the shrinkage defect of the casting is developed, in which the volume variations of the casting in soli- dification are numerically simulated, especially, the mold cavity enlargement is quantitatively calculated. Moreover, the reliability of the model is verified in production and experiment.
基金supported by the National Natural Science Foundation of China (No.12202190)Outstanding Postdoctoral Program in Jiangsu Province (No.2022ZB233)Research Start-up Funding from Nanjing University of Aeronautics and Astronautics (No.90YAH21131)。
