Selective serotonin re-uptake inhibitor sertraline inhibits bone healing in a calvarial defect model

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.

Yield Modeling of Rectangular Defect Outline

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.

A grain boundary defect model for ZnO ceramic varistors by deep heat treatment

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.

Software Residual defects Prediction Model based on BBNs

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.

A Grain Boundary Defect Model for ZnO Ceramic Varistors by Deep Heat Treatment

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Model Tests of Pile Defect Detection

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 %.

Prediction of shear-related defect locations in semi-solid casting using numerical flow models

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.

A Mathematical Model for Predicting Shrinkage Defect of Ductile Iron Castings

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.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

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. .

诱导膜技术在临界尺寸骨缺损修复中的应用:优势与未来发展

背景:诱导膜技术(Masquelet技术)是一种两阶段手术重建大段骨缺损的新技术,在临床中应用越来越广泛。然而,该技术修复骨缺损的机制尚不十分明确。目的:就诱导膜技术的产生背景、修复机制和优势、诱导膜的特征、膜-植骨通讯、动物模型的选择、骨水泥的种类、形貌、所载抗生素对诱导膜的影响、固定方式的选择和骨组织工程材料方面进行综述,以期为未来临界尺寸骨缺损的治疗和诱导膜技术的改进提供新思路。方法:检索PubMed、Web of Science数据库和中国知网(CNKI)中1986-2024年出版的文献,共检索到890篇参考文献。按与诱导膜技术基础研究相关的入选标准进行人工筛选和分析,排除与主题相关性差和重复的文献,纳入研究的文献包括实验类研究原著、综述、荟萃分析等,最终纳入72篇文献进行归纳和分析。结果与结论:①诱导膜技术修复骨缺损的机制尚不明确,但膜和植骨两者缺一不可;②诱导膜是一种富含多种骨形成相关细胞、生长因子和血管的分层较明显组织,其血管化和生长因子的分泌随时间而动态变化;③对于动物模型选择,从解剖结构、负重模式和骨重塑的相似度来讲,羊更为接近;从饲养成本和难度、造模周期来讲,大鼠更合适;④聚甲基丙烯酸甲酯(PMMA)骨水泥不是唯一可以用做诱导生物膜的材料,可能有更加合适的材料可以诱导出更高质量的生物膜;骨水泥负载抗生素的剂量(主要是万古霉素)为每40 g的聚甲基丙烯酸甲酯负载1-4 g抗生素;⑤对于动物(特别是大鼠)固定方式而言,钢板使用更为广泛,固定方式更加可靠,可重复性更高;⑥未来可能会有新的材料替代自体骨促进Masquelet技术的骨修复能力。

An Approach to Detect Structural Development Defects in Object-Oriented Programs

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.

Statistical Characteristics of Partial Discharge Caused by Typical Defects in Cable Joint under Oscillating Voltage

The oscillating voltage test is a nondestructive detection method for partial discharge of XLPE （cross linked polyethylene） cable and has been applied recently. This paper made three kinds of varying severity artificial defect models of cable joints in 10 kV XLPE cable. Oscillating voltage is applied to the model, by use of pulse current method to detect partial discharge signals. In order to study the statistical characteristics of partial discharge of cable joint under the oscillating voltage, three-dimensional statistical map has been made. The results show that for the same kind of defects, with the increases of the defect severity, the discharge interval extended, the magnitude and the number of partial discharge increase, for different kinds of defects, obvious differences exist among the maps, this may established a foundation for the further study of the partial discharge pattern recognition of XLPE cable under oscillating voltage.

A Framework for Software Defect Prediction Using Neural Networks

Despite the fact that a number of approaches have been proposed for effective and accurate prediction of software defects, yet most of these have not found widespread applicability. Our objective in this communication is to provide a framework which is expected to be more effective and acceptable for predicting the defects in multiple phases across software development lifecycle. The proposed framework is based on the use of neural networks for predicting defects in software development life cycle. Further, in order to facilitate the easy use of the framework by project managers, a software graphical user interface has been developed that allows input data (including effort and defect) to be fed easily for predicting defects. The proposed framework provides a probabilistic defect prediction approach where instead of a definite number, a defect range (minimum, maximum, and mean) is predicted. The claim of efficacy and superiority of proposed framework is established through results of a comparative study, involving the proposed frame-work and some well-known models for software defect prediction.

Critical area computation for real defects and arbitrary conductor shapes

In current critical area models, it is generally assumed the defect outlines are circular and the conductors to be rectangle or the merger of rectangles. However, real defects and conductors associated with optimal layout design exhibit a great variety of shapes. Based on mathematical morphology, a new critical area model is presented, which can be used to estimate the critical area of short circuit, open circuit and pinhole. Based on the new model, the efficient validity check algorithms are explored to extract critical areas of short circuit, open circuit and pinhole from layouts. The results of experiment on an approximate layout of 4 × 4 shifts register show that the new model predicts the critical areas accurately. These results suggest that the proposed model and algorithm could provide new approaches for yield prediction.

Effect of Exposure to Trace Elements in the Soil on the Prevalence of Neural Tube Defects in a High-Risk Area of China

Objective Our objective is to build a model that explains the association between the exposure to trace elements in the soil and the risk of neural tube defects. Methods We built a function with different parameters to describe the effects of trace elements on neural tube defects. The association between neural tube defects and trace element levels was transformed into an optimization problem using the maximum likelihood method. Results Tin, lead, nickel, iron, copper, and aluminum had typical layered effects （dosage effects） on the prevalence of neural tube defects. Arsenic, selenium, zinc, strontium, and vanadium had no effect, and molybdenum had one threshold value that affected the prevalence of birth defects. Conclusion As an exploratory research work, our model can be used to determine the direction of the effect of the trace element content of cultivated soil on the risk of neural tube defects, which shows the clues by the dosage effect of their toxicological characteristics. Based on our findings, future biogeochemical research should focus on the direct effects of trace elements on human health.

Main Features of a Complete Ultrasonic Measurement Model: Formal Aspects of Modeling of Both Transducers Radiation and Ultrasonic Flaws Responses

This paper aims at describing the theoretical fundamentals of a reciprocity-based ultrasonic measurement model. This complete inspection simulation can be decomposed in two modeling steps, one dedicated to transducer radiation and one to flaw scattering and echo synthesis. The physical meaning of the input/output signals used in these two modeling tools is defined and the theoretical principles of both field calculation and echo computation models are then detailed. The influence on the modeling results of some changes in the simulated configuration (as the incident angle) or some input signal parameters (like the frequency) are studied: it is thus theoretically established that the simulated results can be compared between each other in terms of amplitude for numerous applications when changing some inspection parameters in the simulation but that a calibration for echo calculation is generally required.

Atomistic simulation of topaz:Structure,defect,and vibrational properties

The clay force field（CLAYFF） was supplemented by fluorine potential parameters deriving from experimental structures and used to model various topazes. The calculated cell parameters agree well with the observed structures. The quasi-linear correlation of the b lattice parameter to different F/OH ratios calculated by changing fluorine contents in OH-topaz supports that the F content can be measured by an optical method. Hydrogen bond calculations reveal that the hydrogen bond interaction to H1 is stronger than that to H2, and the more fluorine in the structure, the stronger the hydrogen bond interaction of hydroxyl hydrogen. Defect calculations provide the formation energies of all common defects and can be used to judge the ease of formation of them. The calculated vibrational frequencies are fairly consistent with available experimental results, and the 1080-cm^-1frequency often occurring in natural OH-topaz samples can be attributed to Si–F stretching because of the F substitution to OH and the Al–Si exchange.

Role of defects in magnetic properties of Fe-doped SnO_2 films fabricated by the Sol-Gel method

This paper obtains the room temperature ferromagnetism in Sn1-x FexO2 films fabricated by the Sol-Gel method. X-ray diffraction results show that Fe doping inhibits the growth of SnO2 and Fe^3＋ ions occupy the Sn sites. The measurement of resistance excludes the free carrier inducing ferromagnetism. Moreover, the temperature dependence of magnetization has been better fitted by the Curie Weiss law and bound magnetic polaron （BMP） theory. An enhancement of ferromagnetism is achieved by annealing the samples with x = 7.1% in H2, and a decrease of oxygen flow rate. All these results prove that the BMP model depending on defects can explain ferromagnetism in diluted magnetic oxides.

Polymorphisms of the maternal Slug gene in fetal neural tube defects in a Chinese population

Several studies have demonstrated that Slug,which encodes a zinc finger of the Snail family of transcription factors,is a potential risk factor for neural tube defects.Neural tube defects tend to occur with a high rate in Shanxi province,China.The present case-control study investigated genotypic distributions and allele frequencies of Slug C1548A polymorphisms in DNA samples from59 women with a history of neural tube defect pregnancies and 73 controls during the same period from Shanxi Province,China.Results demonstrated that women with a history of neural tube defect pregnancies had significantly greater genotypic distributions of Slug AA genotypes and A allele frequencies compared with controls,and A allele Slug C1548A was a risk factor for neural tube defects（odds ratio = 3.444;95% confidence interval;2.021-5.868,P 〈 0.05）.Three-dimensional structure prediction revealed that Slug C1548A resulted in transition of aspartic acid into glutamate at position 119.This indicated that these mutations could lead to damaged protein structure and function.These findings suggest that Slug C1548A gene polymorphism is closely related to neural tube defects in a population of Han Chinese origin from Shanxi Province,China

Dynamic Control of Defective Gap Mode Through Defect Location

A one dimensional model is developed for defective gap mode（DGM）with two types of boundary conditions：conducting mesh and conducting sleeve.For a periodically modulated system without defect,the normalized width of spectral gaps equals to the modulation factor,which is consistent with previous studies.For a periodic system with local defects introduced by the boundary conditions,it shows that the conducting-mesh-induced DGM is always well confined by spectral gaps while the conducting-sleeve-induced DGM is not.The defect location can be a useful tool to dynamically control the frequency and spatial periodicity of DGM inside spectral gaps.This controllability can be potentially applied to the interaction between gap eigenmodes and energetic particles in fusion plasmas,and optical microcavities and waveguides in photonic crystals.