Mechanical and thermal stability investigation of functionally graded plates resting on visco-Pasternak foundation
-
- Steel and Composite Structures
- /
- v.46 no.6
- /
- pp.839-856
- /
- 2023
This work presents a simple four-unknown refined integral plate theory for mechanical and thermal buckling behaviors of functionally graded (FG) plates resting on Visco-Pasternak foundations. The proposed refined high order shear deformation theory has a new displacement field which includes indeterminate integral variables and contains only four unknowns in which any shear correction factor not used, with even less than the conventional theory of first shear strain (FSDT). Governing equations are deduced from the principle of minimum total potential energy and a Navier type analytical solution is adopted for simply supported FG plates. The Visco-Pasternak foundations is considered by adding the impact of damping to the usual foundation model which characterized by the linear Winkler's modulus and Pasternak's foundation modulus. The accuracy of the present model is demonstrated by comparing the computed results with those available in the literature. Some numerical results are presented to show the impact of material index, elastic foundation type, and damping coefficient of the foundation, on the mechanical and thermal buckling behaviors of FG plates.
Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.
This study introduces a novel analytical method for the assessment of pralsetinib impurities and degradation products (DPs), addressing critical gaps in existing methodologies. This research aims to develop a robust HPLC method for impurity analysis, characterize degradation products using LC-MS, and evaluate the environmental impact of the method. The study began by optimizing HPLC conditions with various columns and buffers, ultimately achieving successful separation using an XBridge® RP-C18 column with ethanol as solvent A and 50 mM formic acid at pH 2.9. This setup provided excellent peak resolution and symmetry, essential for reliable stability studies. The developed HPLC method was then adapted for HPLC-MS/MS, enhancing sensitivity and detection efficiency of DPs. Stress degradation studies of pralsetinib under different conditions (acidic, basic, oxidative, thermal, and photolytic) revealed significant degradation under acidic (29.3 %) and basic (21.5 %) conditions, with several DPs identified. Oxidative stress resulted in 19.8 % degradation, while thermal and photolytic conditions caused minimal degradation. HPLC-MS/MS analysis identified structures of five degradation products, providing detailed insights into pralsetinib's stability and degradation pathways. Method validation followed ICH guidelines Q2(R1), confirming method's specificity, selectivity, sensitivity, linearity, accuracy, precision, and robustness. The method exhibited strong linearity with a coefficient of determination (r2) greater than 0.999 for pralsetinib and its impurities. This method advances impurity detection and DPs characterization, ensuring the quality and safety of pralsetinib. Additionally, method's environmental impact was assessed, aligning with sustainable analytical practices. These findings provide essential data on pralsetinib's stability, guiding storage conditions and ensuring its efficacy and safety in pharmaceutical applications.
Introduction: Diffusion is process by which an innovation is communicated through certain channel overtime among the members of a social system(Rogers 1983). Bass(1969) suggested the Bass model describing diffusion process. The Bass model assumes potential adopters of innovation are influenced by mass-media and word-of-mouth from communication with previous adopters. Various expansions of the Bass model have been conducted. Some of them proposed a third factor affecting diffusion. Others proposed multinational diffusion model and it stressed interactive effect on diffusion among several countries. We add a spatial factor in the Bass model as a third communication factor. Because of situation where we can not control the interaction between markets, we need to consider that diffusion within certain market can be influenced by diffusion in contiguous market. The process that certain type of retail extends is a result that particular market can be described by the retail life cycle. Diffusion of retail has pattern following three phases of spatial diffusion: adoption of innovation happens in near the diffusion center first, spreads to the vicinity of the diffusing center and then adoption of innovation is completed in peripheral areas in saturation stage. So we expect spatial effect to be important to describe diffusion of domestic discount store. We define a spatial diffusion model using multinational diffusion model and apply it to the diffusion of discount store. Modeling: In this paper, we define a spatial diffusion model and apply it to the diffusion of discount store. To define a spatial diffusion model, we expand learning model(Kumar and Krishnan 2002) and separate diffusion process in diffusion center(market A) from diffusion process in the vicinity of the diffusing center(market B). The proposed spatial diffusion model is shown in equation (1a) and (1b). Equation (1a) is the diffusion process in diffusion center and equation (1b) is one in the vicinity of the diffusing center.