This paper studies “creativity” in engineering education, by examining the perception of instructors and students. We aim to identify factors that impede a creative environment (creativity blockers). The study entails a review of established research in the fields of psychology and educational psychology to identify factors which create an educational environment conducive to creativity. These factors are formalized in the Ten Maxims of Creativity in Education, a set of criteria that constitute an educational environment conducive to fostering creativity in students. These maxims form the basis for our work in examining the contemporary engineering education. Extensive surveys are designed, created, distributed, and statistically quantified to study the perceptions of engineering educators and students, in comparison to nonengineering educators and students. The results unfortunately show that current engineering students experience almost none of the Ten Maxims of Creativity as part of their academic experiences.

In redundancy resolution of robotic manipulators with more than the required degrees of freedom at the kinematic level, the minimization of the Euclidean norm of the joint velocities at the dynamic level, and the minimization of the kinetic energy of the manipulator at any moment of the task execution have long been suggested in the literature. In this paper the global optimization of the above characteristics are worked out by using integral-type criteria. The results are in surprisingly simple and elegant forms and give new insights to the redundancy resolution of robotic manipulators. The relations between local and global optimization are discussed.

This paper presents an efficient and novel computational protein prediction methodology called kineto-static compliance method. Successive kineto-static fold compliance is a methodology for predicting a protein molecule’s motion under the effect of an inter-atomic force field without the need for molecular-dynamic simulation. Instead, the chain complies under the kineto-static effect of the force field in such a manner that each rotatable joint changes by an amount proportional to the effective torque on that joint. This process successively iterates until all of the joint torques have converged to a minimum. This configuration is equivalent to a stable, globally optimized potential energy state of the system or, in other words, the final conformation of the protein. This methodology is implemented in a computer software package named PROTOFOLD. In this paper, we have used PROTOFOLD to predict the final conformation of a small peptide chain segment, an alpha helix, and the Triponin protein chains from a denatured configuration. The results show that torques in each joint are minimized to values very close to zero, which demonstrates the method’s effectiveness for protein conformation prediction.

A reliable prediction of 3D protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this article, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU/GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels; namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules.