Stochastically Finite Element Buckling and Post Buckling Analysis of Laminated Composite Plates with Foundation in Thermal Environment using Micromechanical Model

This Paper presents the effect of random system properties on thermal buckling response of laminated composite plate using micromechanical approach. The system properties such as thermo-material properties, fiber volume fractions of respective fiber and matrix constituents and foundation parameters are modeled as independent random variables. The temperature field considered to be uniform temperature distributions over the plate surface and through the plate thickness. The material properties of the composite are affected by the variation of temperatures and based on micromechanical model. The basic formulation is based on higher order shear deformation plate theory and general von-Karman types of nonlinearity. A direct iterative based C0 nonlinear finite element method in conjunction mean centered first order perturbation technique is out lined and solved the stochastic linear generalized Eigen value problem. The developed stochastic procedure is usefully used for thermally induced problem based on micromechanical approach with a reasonable accuracy. Parametric studies are carried out to see the effect of volume fractions, amplitude ratios, temperature increments, temperature distributions geometric parameters, lay-ups, boundary conditions and foundation parameters on the mean and variance of plate frequency. The present outlined approach has been validated with those available results in literatures

Bertini type results and their applications

We prove Bertini type theorems and give some applications of them. The applications are in the context of Lefschetz theorem for Nori fundamental group for normal varieties as well as for geometric formal orbifolds. In another application, it is shown that certain class of a smooth quasi-projective variety contains a smooth curve such that irreducible lisse \ell-adic sheaves on the variety with ``ramification bounded by a branch data'' remains irreducible when restricted to the curve

Etiopathological study of oral and oropharyngeal carcinoma

Background: Oral cancer is thought to be the sixth most common form of cancer causing upto 50% of all malignancies in parts of India and South-East Asia, and an increasing trend in oral cancer mortality have been observed in several countries. The aim of the study was to evaluate the etiological factors contributing to oral and oropharyngeal carcinomas and its association with histopathological findings.Methods: This one year duration study was carried out on 100 patients diagnosed as oral and oropharyngeal cancer attending the Department of Otorhinolaryngology and Head and Neck Surgery, Gajra Raja Medical College and J.A. Group of Hospitals, Gwalior, Madhya Pradesh, India.Results: 98% of the patients were histologically squamous cell carcinomas, with well differentiated carcinoma being the most common i.e. 59%. The etiological factors which were found to have statistically significant association in oral cancers were poor oral hygiene, tobacco chewing and pan chewing. Other factors like cigarette/bidi smoking, alcoholism were also common.Conclusions: Any irritation or ulceration in the mouth not attributed to a recognizable causal factor and not healing within four weeks, especially in presence of risk factors must be investigated for its malignant potential. There is a need for improvement in early detection of oral and oropharyngeal carcinomas, because in the initial stages, treatment is more effective and the morbidity is minimal. Keywords:

Quot schemes and Fourier-Mukai transformation

We consider several related examples of Fourier-Mukai transformations involving the quot scheme. A method of showing conservativity of these Fourier-Mukai transformations is described.Comment: Final versio

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies