Teaching in a Teacher centric manner has been the mainframe teaching style in engineering education, however students feel it as a single sided approach and feel they are only passive listeners thus this style has now paved way to a Learner centric style of teaching-learning which is ACTIVE LEARNING, wherein every student is actively involved in one or the other form of learning and thus gets a chance to develop the key aspects of the course either on their own or by being a member of an active-learning group. They thus not only learn and practice the course contents but also learn managerial and team skills which are of much importance in present scenario in regard to Industries and companies where these students will be ultimately hired as employees. Professional education is making one’s students ready for the profession which includes team work, management and technical skills, thus Active learning has emerged as a mainframe tool for cherishing this aim of professional education, especially Science, Technology, Engineering and Management (STEM) education. This paper aims to focus on a few facets of this active learning process and give an overview to the teaching faculty as well as students on what their individual roles must be like in this process for getting the most out of this process.

The design of the manipulator makes it easy to deploy for performing chest compressions at the correct compression rate and depth, as outlined in the 2010 resuscitation guidelines.

A genetic fuzzy system has been developed to solve the forward and reverse mapping problems of green sand mould systems. The performance of a fuzzy logic (FL) system depends on its knowledge base, which consists of a database and a rule base. A binary coded genetic algorithm (GA) has been used to optimise the knowledge base for the FL based approaches. Two approaches have been developed for each model (i.e. forward and reverse modelling). In the first approach, a manually compiled database and rule base of the FL system are optimised by GA, whereas in the second approach, the GA is used to evolve the optimal FL system automatically. The membership function distributions of the FL system are assumed to be asymmetric triangular. The first approach is found to perform better than the latter in terms of accuracy in prediction of the responses.

A heat pipe (HP) is a device transferring large quantities of heat through a small area of the cross‐section with very small deviations in temperature. The thermal impedance of HP is lower while thermal conductance is higher. HPs are designed for controlling temperature, amplification of heat flux, and diminution. HPs are being used in the cooling of aircraft and electronics, solar energy, systems of heat recovery, and nuclear reactors. Complex mathematical formulation demands experimentation to acquire the physical phenomena. Performing experiments is a tedious task with increasing the working parameters and their assigned levels. In such situations, a systematic statistical approach (viz., the Taguchi method) has to be adopted to minimize the number of experiments and to provide the information for the full factorial design of experiments. This paper adopts the modified Taguchi method and applies a simple and reliable multiobjective optimization concept to determine the optimal HP working parameters (viz., heat input, inclination angle, and flow rate). In the optimization process, efficiency, thermal resistance, and overall heat transfer coefficient are the performance indicators (PIs). Empirical relations are developed and validated for the PIs in terms of the HP working parameters. The recommended Taguchi's orthogonal array to perform a few tests may not have the set of optimal working parameters. Additional tests are to be performed to confirm the estimates of the PIs for the optimal working parameters. Confirmation test results in the present study indicate close‐to/within the estimated range.

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