As the biomass of jellyfish (medusae of the Scyphozoa and Hydrozoa) has risen in numerous locations worldwide, awareness of their potential to exert a controlling influence on marine ecosystems and hinder the recruitment of fish stocks has increased. Medusae are capable of intensive, size–selective, predation on zooplankton, which may alter the composition of the plankton community. Jellyfish are often found in dense layers, up to hundreds of metres thick, which can extend horizontally for hundreds of kilometres. Such aggregations may benefit specialist feeders, such as turtles, that rely upon jellyfish for food and those fish that are able to find refuge under the jellyfish umbrellas. Nonetheless, the predominance of jellyfish in pelagic ecosystems is not generally viewed as desirable; in fact, this situation has been portrayed as the result of pollution and overexploitation of otherwise productive seas. However, jellyfish are sensitive indicators of environmental change, and their populations appear to respond to climatic fluctuations, so jellyfish warrant study for their intrinsic ecosystem role particularly given present concerns over climate change. With growing acceptance that fishery management should take an holistic ‘ecosystem approach’, knowledge of the interactions between jellyfish, fisheries and climate may be vital in progression towards the goal of ecosystem–based sustainable management of fisheries. Unfortunately, due to their gelatinous nature, medusae are difficult to sample using conventional netting techniques and data on changes in distribution and abundance are consequently sparse. Recent studies have demonstrated that medusae can be detected acoustically and that this technique could provide a rapid and cost–effective estimate of their biomass and distribution. This thesis reports my endeavour to demonstrate the ecosystem role of medusae and to develop acoustic techniques to monitor their biomass. Through regession analyses, I link the abundance of medusae (Aurelia aurita, Cyanea lamarckii, and Cyanea capillata) in regions of the North Sea to climatic fluctuations, as quantified by the North Atlantic Oscillation Index, and show that medusae may be important indicators of regional ecosystem change. The mechanisms linking climatic fluctuations to ecosystem changes are explored via a correlative modelling approach using General Additive Models; I show that the mechanisms are location dependent and explainable in terms of direct, rapidly responding (intra–annual) influences (surface warming, river run–off, and wind–driven mixing and advection) and longer–term (interannual) oceanographic responses (changes in circulation currents i.e. the northward extent of the gulf stream and relative strength of inflow into the North Sea of the North Atlantic current, Continental Shelf Jet and Arctic waters). I present correlative evidence for a detrimental impact by Aurelia aurita on herring 0–group recruitment, once the influence of interannual change in herring spewing stock biomass on recruitment is factored out through modelling with a Ricker stock–recruitment relationship. Similarly, a commensal relationship between whiting and Cyanea spp. medusae is shown to improve North Sea whiting survival to the 1–group. In progress towards the automated acoustic identification of species, I have developed an in situ discrimination tool that can distinguish between echoes from: Aequorea aequorea; Chrysaora hysoscella; clupeid fish (sardine, anchovy and round herring); and horse mackerel/Cape hake. The technique relies upon characteristic differences in echo strength between frequencies, which are determined for each jellyfish species and finfish group using combined multifrequency acoustic and pelagic trawl samples. This method has facilitated the world’s first acoustic–based estimate of jellyfish biomass in the Namibian Benguela Sea. The 12.2 million tonnes of biomass of medusae (Aequorea aequorea and Chrysaora hysoscella) in the Namibian Benguela Sea was found to be greater than the combined biomass, 3.6 million tonnes, of commercially important fish (horse mackerel, Cape hake, sardines, anchovy, and round herring) in the same area. These results suggest that medusae may have an important role in the Benguela ecosystem that has previously been overlooked and that their biomass should be monitored.

This document describes the design and deployment of a first generation water vapor density sensing unit, the HumiSense. This device is based on an open, air-filled capacitor which is part of a resonant circuit. The frequency of the resonant circuit mixed with a fixed frequency oscillator is the basis of the method to generate a signal that is associated to the change in water vapor density within the open capacitor with time. The physical testing results were inconclusive given that there were many unresolved artifacts in the data. Several suggestions for improving the device for future device generations were provided. / Graduation date: 2013

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Dye sensitized solar cells (DSSCs) have typically been produced using organic liquids such as acetonitrile as the electrolyte solvent. In real world situations water can permeate into the cell through sealing materials and is also likely to be introduced during the fabrication process. This is a problem as the introduction of water into cells optimized to use an organic solvent tends to be detrimental to cell performance. In this work DSSCs which are optimized to use water as the main electrolyte solvent are produced and characterized. Optimization of aqueous DSSCs resulted in cells with efficiencies up to 3.5% being produced. In terms of characterization, it is generally seen in this work that aqueous DSSCs produce a lower photocurrent but similar photovoltage compared to DSSCs made using acetonitrile and reasons for this are examined in detail. The decreased ability of the aqueous electrolyte to wet the nanoporous TiO2 compared to an acetonitrile electrolyte is found to be a key difficulty and several possible solutions to this problem are examined. By measuring the photocurrent output of aqueous cells as a function of xy position it can be seen that there is some dye dissolution near to the electrolyte filling holes. This is thought to be linked to pH and the effect of 4-tert-butylpyridine and may also decrease the photocurrent. It is found that there is little difference between the two types of cells in terms of the conduction band position and the reaction of electrons in the semiconductor with triiodide in the electrolyte, explaining the similarity in photovoltage. By altering the pH of the electrolyte in an aqueous cell it is found to be possible to change the TiO2 conduction band position in the DSSC. This has a significant effect on the open circuit voltage and short circuit current of the cell, though the pH range available is limited by the fact that dye desorbs at high pH values.

Manufacturing process development is under constant pressure to achieve a good yield for stable processes. The development of new technologies, especially in the field of photomask and semiconductor development, is at its phys- ical limits. In this area, data, e.g. sensor data, has to be collected and analyzed for each process in order to ensure process quality. With increasing complexity of manufactur- ing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume exceeds the possibility of a manual data analysis. At this point, data mining techniques become interesting. The application of current techniques is complex because most of the data is captured with sensor measurement tools. Therefore, every measured value contains a specific error. In this paper we propose an error-aware extension of the density-based al- gorithm DBSCAN. Furthermore, we present some quality measures which could be utilized for further interpretation of the determined clustering results. With this new cluster algorithm, we can ensure that masks are classified into the correct cluster with respect to the measurement errors, thus ensuring a more likely correlation between the masks.