With the several changes happening every day in societies and in thoughts say knowledge challenges are increasing day by day which is to be faced by business as well as other organizations. To tackle these challenges many tactics are implemented and are in process to further improve. Handling of these challenges requires a system under which one can work and let adaptation to the changes can be done smoothly. Today majority of business organizations have a knowledge management program in one or another form. Indian business organizations are also feeling the need for new business paradigms. Knowledge management is a systematic process for creating, acquiring, synthesizing, learning, sharing and using knowledge and experience to achieve organizational goals. This paper “Handling Knowledge in Indian Information Technology (IT) Organizations” underscores Knowledge Management practices in business organizations at main cities in India. Papers site an overview of the techniques and also include future improvements that can be done to ameliorate the efficiency of Knowledge Management System.
Hyperelastic constitutive models of soft tissue mechanical behavior are extensively used in applications like computer-aided surgery, injury modeling, etc. While numerous constitutive models have been proposed in the literature, an objective method is needed to select a parsimonious model that represents the experimental data well and has good predictive capability. This is an important problem given the large variability in the data inherent to soft tissue mechanical testing.
In this work, we discuss a Bayesian approach to this problem based on Bayes factors. We propose a holistic framework for model selection, wherein we consider four different factors to reliably choose a parsimonious model from the candidate set of models. These are the qualitative fit of the model to the experimental data, evidence values, maximum likelihood values, and the landscape of the likelihood function. We consider three hyperelastic constitutive models that are widely used in soft tissue mechanics: Mooney-Rivlin, Ogden and exponential. Three sets of mechanical testing data from the literature for agarose hydrogel, bovine liver tissue, porcine brain tissue are used to calculate the model selection statistics. A nested sampling approach is used to evaluate the evidence integrals. In our results, we highlight the robustness of the proposed Bayesian approach to model selection compared to the likelihood ratio, and discuss the use of the four factors to draw a complete picture of the model selection problem.
Starting or growing a co-op/internship program can be intimidating; for both educators and potential
employer partners. In an effort to learn the pain points for both parties, opportunities to break down
barriers and build bridges, and identify actionable steps to get started, faculty from the University of
Cincinnati’s Division of Experience-Based Learning and Career Education conducted a two-year research
project with 65 co-op and internship employers from more 15 unique industry clusters, and 50
university faculty and staff representing 24 unique institutions. This poster will graphically share the
resulting findings from more than 1250 qualitative responses, and generate discussion on the
educational pedagogy of creating best practices for employer partners. Find out what “the survey says”!