Study of the application of the high performance liquid chromatography-particle beam interface-mass spectrometry technique on some pesiticides and the reduction phenomena in the system

This work includes two major parts. The first part of the work concentrated on the studies of the application of the highperfonnance liquid chromatography-particle beam interface-mass spectrometry system of some pesticides. Factors that have effects on the detection sensitivity were studied. The linearity ranges and detection limits of ten pesticides are also given in this work. The second part of the work concentrated on the studies of the reduction phenomena of nitro compounds in the HPLC-PB-MS system. Direct probe mass spectrometry and gas chromatography-mass spectrometry techniques were also used in the work. Factors that have effects on the reduction of the nitro compounds were studied, and the possible explanation is proposed. The final part of this work included the studies of reduction behavior of some other compounds in the HPLC-PB-MS system, included in them are: quinones, sulfoxides, and sulfones.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.