The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. First, the developed algorithm is applied to a real-life software development project. Second, performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

Adverse outcome pathways (AOPs) provide a framework to organize and weigh the evidence linking a toxicant’s initial interactions with molecules in the cell to adverse outcomes of regulatory concern. AOPs are constructed in modules that include key events (KEs) and key event relationships (KERs). Quantitative understanding of the KERs is critical for the development of predictive toxicological models. The objective of this project was to investigate the ability to define the quantitative associations of the KERs upstream, and contained in, an existing AOP (#296): “Oxidative DNA Damage Leading to Chromosomal Aberrations and Mutations”. The data supporting quantitative associations between these KERs was gathered through literature review and experimental methods. I first used systematic literature review tools to develop and apply a pragmatic and transparent method to search the literature for AOP evidence. A broad search, covering all of the KERs of interest, was initially conducted. This search, which retrieved more than 230 thousand articles, demonstrates the data-rich nature of the AOP. An artificial intelligence informed prioritization of the top 100 articles were then examined in detail. This approach identified 39 articles containing qualitative empirical support for the AOP, but limited quantitative evidence of the KERs. A second search was conducted to address the need for quantitative evidence as well as the lack of evidence for the KER between and increase in reactive oxygen species (ROS) and oxidative DNA damage. The second search retrieved 12 articles that could be used to define a quantitative relationship between cellular ROS and oxidative DNA damage. To begin to address gaps in quantitative understanding, I then conducted experiments in the laboratory to measure oxidative DNA damage, DNA strand breaks, chromosomal aberrations, and mutations in TK6 cells after exposure to a range of concentrations of 4-Nitroquinoline 1-oxide (4NQO: a prototype ROS producing agent). An increase in both oxidative DNA damage and DNA strand breaks was observed after 2, 4, and 6 h exposures with the high throughput comet assay (CometChip). An increase in the incidence of micronuclei was observed after a 24 h exposure to a low concentration of 4NQO, as measured with the flow cytometry micronucleus assay, while high cytotoxicity was found at higher concentrations. Lastly an increase in mutation frequency was observed with Duplex Sequencing, an error-corrected sequencing technology. Additionally, an increase in the proportion of C>A transversions was observed, consistent with the expected mutations following oxidative DNA lesions. Overall, my work contributes to the quantitative understanding of AOP #296 and this project serves as a key example of AOP-informed study design, highlighting notable challenges in characterizing quantitative relationships.

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Knowledge of the impacts of management and scale are important for improved understanding and prediction of turf chemical runoff in urban environments. This study addressed the effects of mowing height, warm-season turf species and plot size on runoff of water, bromide, dimethylamine salts of the herbicides 2, 4-D, MCPP and dicamba, flutolanil fungicide, and chlorpyrifos insecticide from a Brooksville silty clay soil. The runoff plots were sloped at 3 % and arranged as split-plot in a randomized complete block design. The pesticides were applied as a tank mix: 2, 4-D at 1.12 kg ai/ha, MCPP at 1.80 kg ai/ha, dicamba at 0.50 kg ai/ha, flutolanil at 2.24 kg ai/ha and chlorpyrifos at 2.24 kg ai/ha. Bromide was applied separately at 15 kg ai/ha. The pesticides and bromide were applied 24 h and 0.5 h respectively, prior to each rainfall simulation event. Rainfall simulated at 38 mm/h was applied to treated plots for 1.5 h to generate runoff which was collected at 5 minute intervals. Pesticide runoff concentrations were determined by reverse-phase HPLC using UV-Vis detection. The limit of quantification for each compound was approximately 5 µg/L. Bromide was analyzed for by an ion selective electrode following EPA method 9211 with the limit of detection at 200µg/L. Plot size, mowing height and/or grass species significantly affected different runoff aspects of the pesticides investigated at p< 0.05. Averaged across treatments, percentages of applied pesticide lost in runoff were 43.3 ± 12.7 for 2, 4-D, 29.5 ± 8.3 for MCPP, 24.6 ± 8.3 for dicamba, 6.8 ± 1.0 for flutolanil and 0.22 ± 0.04 for chlorpyrifos. Similarly, average peak pesticide concentrations were 3.7 ± 0.9 mg/L for 2, 4-D, 4.2 ± 1.1 mg/L for MCPP, 1.2 ± 0.3 mg/L for dicamba, 0.8 ± 0.3 mg/L for flutolanil and 0.04 ± 0.02 mg/L for chlorpyrifos. Results obtained for water and bromide runoff suggest that the treatment effects observed for the pesticides were due to differences in retention mechanism rather than turf hydrology. Linear relationships were obtained between plot area and chemical mass and total runoff indicating that runoff from bermudagrass turf is ‘scalable’.

Southern bottomland hardwood forests lack effective growth and yield predictive models primarily due to the complexity of the ecosystems. These models are important tools for relative comparison of management schemes and making sound management decisions to obtain optimal future yields. Starting in 1982, 150 red oak-sweetgum bottomland hardwood growth and yield plots were established in northern and central parts of Mississippi. These plots were remeasured in 1988, 1992, 1993, 2005, 2006, and 2007 along with the addition of new plots. A diameter distribution model was developed from stand level component equations constructed in a previous study (Iles 2008; Schultz et al. 2010). The equations created performed well when testing the predicted survival and diameter growth against the observed data. The resulting growth and yield system will be a basis for better decision making in the comparison of management alternatives as well as increased conservation and efficient utilization of wood products.

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