Strain Rate Dependence And Impact Behavior Of Abs (acrylonitrile-Butadiene-Styrene) Amorphous Thermoplastic

ABS (acrylonitrile-butadiene-styrene) is an extensively utilized amorphous thermoplastic in numerous engineering applications, such as marine, aerospace, automotive, electronic enclosures and housings because it offers many distinctive material properties, including good impact resistance, high toughness, high stiffness and high compressive strength. The most considerable material quality of ABS is its excellent impact resistance compared to other amorphous thermoplastics and this distinguished material ability makes the ABS very appealing for such unique engineering applications where a good impact resistance is highly needed. Nevertheless, the material behavior of ABS under impact loads is highly complex due to chaotically arranged chain macromolecules and randomly dispersed rubber particles in its structure. Therefore, understanding its impact behavior is currently under a considerable investigation. Particularly, a numerical analysis to accurately predict the impact behavior of ABS has been of very desired industrial interest. Thus, the primary aim of this study was to successfully predict the impact response of ABS subjected to various impact velocities utilizing the semi-empirical material model (SAMP-1) in the explicit solver of LS-DYNA. The material parameters of ABS used as an input in SAMP-1 were obtained through the conducted uniaxial tension tests over a wide range of strain rates varying from low to high, as well as, uniaxial compression and shear tests at different strain rates. Numerical predictions were favorably compared to experimental results and there was a very good agreement found between them. Hence, the impact response of ABS under different impact velocities was numerically predicted with a very high accuracy. Additionally, after the ABS material was subjected to impacts, two powerful non-destructive evaluation methods, such as ultrasonic C-Scan and laser scanning microscopy, were utilized to detect damaged areas and surface imperfections, respectively. The detected damaged areas by ultrasonic C-Scan were also compared to the numerically predicted damaged areas

Free vibration analyses of abs (acrylonitrile-butadiene-styrene) rectangular plates with completely free boundary conditions

Acrylonitrile-Butadiene-Styrene (ABS) represents a family of engineering thermoplastics with a wide field of performance characteristics. ABS materials have been receiving a great deal of attention because of their unique properties, such as outstanding formability, high tensile strength and stiffness, very high impact strength, excellent ductility, excellent high and low temperature performance, and resistant to many chemicals and plasticizers. Particularly, ABS exhibits really high impact strength; therefore, it is used in industry products which require high impact strength materials, such as military helmets and construction safety helmets. The vibration analyses of ABS rectangular plates are incredibly significant for design in military applications. The modal parameters of three various ABS rectangular samples, such as deflection mode shapes, resonance frequencies and damping ratios, were obtained in the frequency range of 0 Hz to 6400 Hz by the PULSE 15.1 software which is produced by the Bruel&Kjaer company. The Lancsoz Eigensolver method in ABAQUS/STANDARD 3D software, which is a powerful tool for extraction of the extreme Eigenvalues, was used to determine the natural frequencies and mode shapes of the ABS rectangular samples. The modal densities of the samples were experimentally determined in the frequency range of 0 Hz to 12800Hz to find out which sample requires a more effort and money in design process. The experimental and finite element results were compared very favorably with one another

Application of analog computers to inventory control problems

LD2668 .T4 1966 A988Master of Scienc

InGaAsP photonic crystal slot nanobeam waveguides for refractive index sensing

Results are presented on the use of InGaAsP photonic crystal nanobeam slot waveguides for refractive index sensing. These sensors are read remote-optically through photoluminescence, which is generated by built-in InGaAs quantum dots. The nanobeams are designed to maximize the electromagnetic field intensity in the slot region, which resulted in record-high sensitivities in the order of 700 nm/RIU (refractive index unit). A cavity, created by locally deflecting the two beams towards each other through overetching, is shown to improve the sensitivity by about 20%

The effects of streptozotocin-induced diabetes on ghrelin expression in rat testis: biochemical and immunohistochemical study

Introduction. Ghrelin is a hormone which has effects on the secretion of growth hormone, gastrointestinal system, cardiovascular system, cell proliferation and reproductive system. The present study we focused on the relation between ghrelin and GHS-R1a gene expression and the regulation of their expression in the testes of diabetic rats. Material and methods. 40 male Wistar albino rats were divided into four groups: control, and sampled 4, 8 and 12 weeks after induction of diabetes by streptozotocin (STZ) intraperitoneal injection (40 mg/kg). The rats were decapitated under ketamine anesthesia and their testes were removed. Blood was obtained from heart and serum follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone levels were measured by ELISA. Tissue ghrelin and GHS-R mRNA levels were determined by qRT-PCR, while ghrelin protein expression was studied by immunohistochemistry. Histopathological damage scores were also assessed. Results. Eight weeks after diabetes induction serum FSH level was increased, whereas LH and testosterone concentrations decreased. The ghrelin and GHS-R1a gene expression and ghrelin immunohistochemistry score first tended to increase after first four weeks of diabetes, and then tended to decrease. Ghrelin-immunopositive cells were detected in Leydig cells in all groups of rats, however, not in the germinal epithelium. Congestion of vessels and hemorrhage, formation of the vacuoles in spermatogonia and spermatocytes, desquamation of spermatids in the lumen and disorganization of seminiferous tubule germinal epithelium were observed in testis of all the diabetic rats. In addition, mean testicular biopsy score and mean seminiferous tubule diameter were getting lower in diabetic animals. Conclusion. Our results suggest that diabetes affects ghrelin expression in rat testis.

Exophytic Giant Cell Glioblastoma in a Patient with Neurofibromatosis Type 1: Case Report

Giant cell glioblastoma multiforme (GCGBM) is an uncommon subtype within the spectrum of glioblastoma multiforme (GBM) tumors. Neurofibromatosis type 1 (NF1) has an increased risk of developing neoplasms that generally are of a benign nature. We report a rare case of an exophytic GCGBM in a 43-year-old woman with NF1. The tumor located in the right frontal region was removed totally and the patient underwent radiotherapy with 60 Gy and chemotherapy with temozolomide

Volume CXIV, Number 4, November 7, 1996

Objective: Turner syndrome (TS) is a chromosomal disorder caused by complete or partial X chromosome monosomy that manifests various clinical features depending on the karyotype and on the genetic background of affected girls. This study aimed to systematically investigate the key clinical features of TS in relationship to karyotype in a large pediatric Turkish patient population.Methods: Our retrospective study included 842 karyotype-proven TS patients aged 0-18 years who were evaluated in 35 different centers in Turkey in the years 2013-2014.Results: The most common karyotype was 45,X (50.7%), followed by 45,X/46,XX (10.8%), 46,X,i(Xq) (10.1%) and 45,X/46,X,i(Xq) (9.5%). Mean age at diagnosis was 10.2±4.4 years. The most common presenting complaints were short stature and delayed puberty. Among patients diagnosed before age one year, the ratio of karyotype 45,X was significantly higher than that of other karyotype groups. Cardiac defects (bicuspid aortic valve, coarctation of the aorta and aortic stenosis) were the most common congenital anomalies, occurring in 25% of the TS cases. This was followed by urinary system anomalies (horseshoe kidney, double collector duct system and renal rotation) detected in 16.3%. Hashimoto's thyroiditis was found in 11.1% of patients, gastrointestinal abnormalities in 8.9%, ear nose and throat problems in 22.6%, dermatologic problems in 21.8% and osteoporosis in 15.3%. Learning difficulties and/or psychosocial problems were encountered in 39.1%. Insulin resistance and impaired fasting glucose were detected in 3.4% and 2.2%, respectively. Dyslipidemia prevalence was 11.4%.Conclusion: This comprehensive study systematically evaluated the largest group of karyotype-proven TS girls to date. The karyotype distribution, congenital anomaly and comorbidity profile closely parallel that from other countries and support the need for close medical surveillance of these complex patients throughout their lifespa

TOWARD AN OPTIMAL ANALYSIS OF HYPERSPECTRAL DATA

In hyperspectral data materials of practical interest usually exist in a number of states and are observed in a number of conditions of illumination. It is thus necessary to characterize them not with a single spectral response but with a family of responses. One of the most versatile means for representing such a family of responses quantitatively is to model each by a normal distribution, as this makes possible classification by assigning the class to a sample based on Bayes rule. This leads to competitive performance only under special circumstances. The purpose of this dissertation is to improve the quantitative definitions of classes by replacing the classical normal model with more flexible and powerful alternatives and thereby investigate the relationship between class definition precision and classification accuracy. One other factor that directly affects the precision of class definitions is the number of labeled samples available for training. Characterizing class data with a limited set of labeled samples may have severe consequences. In a typical setting a remote sensing analyst should either sacrifice from the classifier performance by confining himself to the already available labeled data set or commit more time and effort to acquire more labeled samples both of which comes at a price. The present study also addresses this problem by proposing a semi-supervised binary classifier which seeks to incorporate unlabeled data into the training in order to improve the quantitative definitions of classes and hence improve the classifier performance at no extra cost

Toward an optimal analysis of hyperspectral data

In hyperspectral data materials of practical interest usually exist in a number of states and are observed in a number of conditions of illumination. It is thus necessary to characterize them not with a single spectral response but with a family of responses. One of the most versatile means for representing such a family of responses quantitatively is to model each by a normal distribution, as this makes possible classification by assigning the class to a sample based on Bayes rule. This leads to competitive performance only under special circumstances. The purpose of this dissertation is to improve the quantitative definitions of classes by replacing the classical normal model with more flexible and powerful alternatives and thereby investigate the relationship between class definition precision and classification accuracy. One other factor that directly affects the precision of class definitions is the number of labeled samples available for training. Characterizing class data with a limited set of labeled samples may have severe consequences. In a typical setting a remote sensing analyst should either sacrifice from the classifier performance by confining himself to the already available labeled data set or commit more time and effort to acquire more labeled samples both of which comes at a price. The present study also addresses this problem by proposing a semi-supervised binary classifier which seeks to incorporate unlabeled data into the training in order to improve the quantitative definitions of classes and hence improve the classifier performance at no extra cost