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Wastewater is an abundant renewable source of nutrients such as nitrogen and phosphorus. However, current recovery methods require large amounts of energy and chemicals. Consequently, these processes pose economic and environmental burdens and is therefore often not implemented. Microbial fuel cells (MFC) are one of the bio-electrochemical wastewater treatment technologies that has potential for nutrient recovery as it can reduce chemicals cost while at the same time treat the wastewater and produce electricity. However, there is limited knowledge of the optimum operation mode of MFCs and the effect of the substrate on nutrient recovery. This study investigates the efficiency of struvite formation for nutrient recovery in a MFC using various wastewater substrates, such as, urine, centrate, and greywater (phase I) and different operating modes (phase II). In phase II of the experimental run the system was operated by recirculating the treated anolyte to the cathode compartment. The performance of the 700ml-volume dual-chamber cells was assessed based on electricity output, organics removal in terms of soluble chemical oxygen demand (sCOD), and struvite recovery. The highest sCOD removal efficiency of over 95% was achieved with the centrate-fed cell. The sCOD removal efficiency increased by 24% from the single operation (phase I) to the recirculation stage (phase II). The highest maximum power density generated from the urine, centrate, and greywater fed MFCs for phase I were found to be 1.7, 1.5, and 0.95 mW/cm², and 1.18, 0.82, and 0.73 mW/cm² for phase II, respectively. The urine-fed 700ml cell produced the highest struvite precipitate quantity (1.5g), followed by centrate (1.3g) and greywater (0.3g) in phase I, while the urine sample yielded 1.7g in phase II. The highest phosphate recovery efficiencies of over 55% (phase I) and 20% (phase II) were found for the cell treating urine, while efficiencies of other substrates were 36% (for centrate) and 48% (for greywater. Morphological and chemical analysis of the precipitate formed confirmed that the nutrients were recovered primarily as struvite. The results of this study verify the potential for implementing MFCs for nutrient recovery and anolyte recirculation.

A very prominent area in the field of Mechatronics is robot navigation and path planning. This area deals with the problem of autonomously calculating the least cost path in a provided environment, whether it is static or dynamic, and navigating the robot platform though this environment. Over the last decade, since the year 2001, there have been major breakthroughs in this field after LaValle introduced his revolutionary algorithm, the Rapidly-Exploring Random Tree (RRT) approach. Later, in the year 2011, Karaman introduced his novel modification to RRT which he called the Rapidly- Exploring Random Tree Star (RRT*). The main advantage of RRT* is its effectiveness and robustness in finding the path to the target and its probabilistic completeness property which guarantees the best theoretical path if given enough run time. However, RRT* still suffers from long processing times to provide paths with satisfactory quality in terms of cost and smoothness. Having said that, in this thesis we propose an improved version of the RRT* algorithm which will address the issue of long processing times and sub-par path quality. This new method is called Rapidly-Exploring Random Tree Star Normal (RRT*N). The presented method can handle static and dynamic obstacles in 2D and 3D environments. This improved method uses a Gaussian probability distribution to generate new nodes which have a higher probability of being generated along the vector pointing from the starting point to the goal point, which results in a tree centered on the line joining the robot to the target. It is shown that this method can be three times faster in finding the path to the target in static scenarios, and upto 20 times faster in dynamic environments compared to RRT*. Furthermore its rate of achieving satisfactory paths is consistently more than 95% while maintaining similar path quality. For instance in 250 trials of the presented static scenario, RRT*N had an average processing time and average path length of 38.46 seconds and 240.99 units, respectively. Meanwhile, RRT* resulted in 121.58 seconds and 259.11 units. This work is based on an extensive literature review to validate this work’s novelty and the simulation and experimental results presented show the robustness of the proposed RRT*N method.

By following a bottom-up approach based on principles of induction, the current study aims to provide the most typical lexical units of the provisional element of recitals in business contracts. In doing so, a novel methodological approach to corpus homogeneity is proposed to satisfy quality standards of the corpora serving the purpose of this study. Cosine values were utilized for homogeneity operationalization since they operate on the principle of similarity. Further, in producing the frequency wordlists, the study focuses on two methodological parameters, namely N-gram sequence length and frequency, which are proposed by Biber and Conard (2005). By means of a textual analysis tool named Gramulator (McCarthy et al., 2012), the N-grams common to two specialized corpora of several contract types were extracted. A preliminary frequency baseline (< 50%) was employed in the selection process. More notably, the statistical examination of the data suggests the presence of a parent-child relationship between both corpora. The findings of the current study are of interest to English for legal purposes (ELP) practitioners who are precisely teaching new law students as well as researchers that wish to investigate the discourse functions forming the conventions of business contracts drafting.