Technical communication in the global context : a heuristic approach to disciplinary identity and pedagogical practices

There is nothing new or original in stating that the global economy directly impacts the profession of technical communicators. The globalization of the workplace requires that technical communicators be prepared to work in increasingly linguistically and culturally diverse contexts. These new exigencies have natural repercussions on the research and educational practices of the field In this work, I draw on rhetoric, linguistics, and literacy theory to explore the definition, role and meaning of the global context for the disciplinary construction of professional and technical communication. By adopting an interdisciplinary and diachronic perspective, I assert that the global context is a heuristic means for sophisticating the disciplinary identity of the field and for reinforcing its place within the humanities. Consequently, I contend that the globalization of the workplace is a kairotic moment for underscoring the rhetorical dimension of professional and technical communication.

A COMPARATIVE STUDY OF HEURISTIC OPTIMIZATION ALGORITHMS

Heuristic optimization algorithms are of great importance for reaching solutions to various real world problems. These algorithms have a wide range of applications such as cost reduction, artificial intelligence, and medicine. By the term cost, one could imply that that cost is associated with, for instance, the value of a function of several independent variables. Often, when dealing with engineering problems, we want to minimize the value of a function in order to achieve an optimum, or to maximize another parameter which increases with a decrease in the cost (the value of this function). The heuristic cost reduction algorithms work by finding the optimum values of the independent variables for which the value of the function (the “cost”) is the minimum. There is an abundance of heuristic cost reduction algorithms to choose from. We will start with a discussion of various optimization algorithms such as Memetic algorithms, force-directed placement, and evolution-based algorithms. Following this initial discussion, we will take up the working of three algorithms and implement the same in MATLAB. The focus of this report is to provide detailed information on the working of three different heuristic optimization algorithms, and conclude with a comparative study on the performance of these algorithms when implemented in MATLAB. In this report, the three algorithms we will take in to consideration will be the non-adaptive simulated annealing algorithm, the adaptive simulated annealing algorithm, and random restart hill climbing algorithm. The algorithms are heuristic in nature, that is, the solution these achieve may not be the best of all the solutions but provide a means to reach a quick solution that may be a reasonably good solution without taking an indefinite time to implement.