This study generates a substantive theory of how engineering students approach engineering mathematics learning in a polytechnic in Singapore. This thesis adopts a symbolic interactionist perspective and engages grounded theory methodology in its investigation. The main source of data comes from a series of in-depth face to face interviews with a group of 21 engineering students in the case Polytechnic. This is supplemented by data gathered from the students’ reflection journals and informal interviews with their teachers. The first major outcome of this study is the generation of the theory of Selective Intentionality in engineering mathematics learning that describes how engineering students approach mathematics learning through a series of socio-psychological processes. The core category in this study is the category of intending that is surrounded by the other four categories of gathering, analysing, actualising and regulating. Another major outcome that arises from this study is the development of a typology of students with regards to how they experience and manage mathematics learning in their engineering courses in the case Polytechnic. The typology is based on the predominant distinctions among the participants according to their responses in the analysing, intending, actualising and regulating processes in the theory of Selective Intentionality. Accordingly, the students may be broadly classified into five types of learners: idealistic learners, competitive learners, pragmatic learners, fatalistic learners and dissonant learners. In short, this study provides a fresh perspective on how engineering students approach mathematics learning that is very important in their courses. At the same time, it has implications for the development of theory, practice and future research.

The category game models the communication protocols between two agents who aim to match category labels with elements encountered in a simulated ()r real world. In the language games community the expressions available to an agent has traditionally centered on category label assertions. This thesis presents a new representation for category labels, in which category definitions explicitly Incorporate semantic uncertainty and typicality. More specifically, a conceptual model based on prototype and random set theory is proposed, in which categories are defined within a metric conceptual space. It is argued that this conceptual framework is both expressive and naturally generates robust assertion and concept updating models. This is demonstrated by the development of assertion and updating rules which allow compound logical expressions of category labels to be used in agent interaction. In particular, in multi-agent simulations of the category game under this framework, agents use a mixture of category labels and negated labels. The results of these simulations are presented, where a multi-agent system evolves through pairwise language games, implementing several different assertion and updating algorithms. The performance is measured in terms of the average distance between agents' conceptual interpretations, and results suggest that, within this framework, a mixture of both positive and negative assertions are required in order for the multi-agent systems to evolve so that agents share sufficiently similar interpretations covering the space, which are also able to discriminate between different cases. These results are supported in subsequent chapters where two further language games are implemented, suggesting that using a mixture of assertions shows the best possible communication success between agents. An investigation into the role of feedback in the category game compares simulations in which agents use feedback with the results of simulations without feedback. All results presented suggest that using feedback allows agents to develop a set of interpretations which are more similar than if feedback was not being implemented, and also allows agents to obtain a higher value for average communication success. Finally, we introduce a bipolar mode~ of assertion. This allows agents to make an assertion with two degrees of celtainty, and may be used as a strategy to gain higher reward and lower punishment in a category game implementing a feedback model.

This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of this project is minimization of the active power transmission losses by optimally setting the control variables within their limits and at the same time making sure that the equality and inequality constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA) algorithms which are nature-inspired algorithms have become potential options to solving very difficult optimization problems like ORPD. Although PSO requires high computational time, it converges quickly; while BA requires less computational time and has the ability of switching automatically from exploration to exploitation when the optimality is imminent. This research integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3) benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to be superior to those of the PSO and BA methods. In order to check if there will be a further improvement on the performance of the HPSOBA, the HPSOBA was further modified by embedding three new modifications to form a modified Hybrid approach denoted as MHPSOBA. This MHPSOBA was validated using IEEE 30-bus test system to solve ORPD problem and the results show that the HPSOBA algorithm outperforms the modified version (MHPSOBA). / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)