The broad-scale distribution and abundance of Scyphomedusae in Irish waters

Scyphomedusae are receiving increasing recognition as key components of marine ecosystems. However, information on their distribution and abundance beyond coastal waters is generally lacking. Organising access to such data is critical to effectively transpose findings from laboratory, mesocosm and small scale studies to the scale of ecological processes. These data are also required to identify the risks of detrimental impacts of jellyfish blooms on human activities. In Ireland, such risks raise concerns among the public, but foremost amongst the professionals of the aquaculture and fishing sectors. The present work looked at the opportunity to get access to new information on the distribution of jellyfish around Ireland mostly by using existing infrastructures and programmes. The analysis of bycatch data collected during the Irish groundfish surveys provided new insights into the distribution of Pelagia noctiluca over an area >160 000 km2, a scale never reached before in a region of the Northeast Atlantic (140 sampling stations). Similarly, 4 years of data collected during the Irish Sea juvenile gadoid fish survey provided the first spatially, explicit, information on the abundance of Aurelia aurita and Cyanea spp. (Cyanea capillata and Cyanea lamarckii) throughout the Irish Sea (> 200 sampling events). In addition, the use of ships of opportunity allowed repeated samplings (N = 37) of an >100 km long transect between Dublin (Ireland) and Holyhead (Wales, UK), therefore providing two years of seasonal monitoring of the occurrence of scyphomedusae in that region. Finally, in order to inform the movements of C. capillata in an area where many negative interactions with bathers occur, the horizontal and vertical movements of 5 individual C. capillata were investigated through acoustic tracking.

A novel and miniaturized 433/868MHz multi-band wireless sensor platform for body sensor network applications

Body Sensor Network (BSN) technology is seeing a rapid emergence in application areas such as health, fitness and sports monitoring. Current BSN wireless sensors typically operate on a single frequency band (e.g. utilizing the IEEE 802.15.4 standard that operates at 2.45GHz) employing a single radio transceiver for wireless communications. This allows a simple wireless architecture to be realized with low cost and power consumption. However, network congestion/failure can create potential issues in terms of reliability of data transfer, quality-of-service (QOS) and data throughput for the sensor. These issues can be especially critical in healthcare monitoring applications where data availability and integrity is crucial. The addition of more than one radio has the potential to address some of the above issues. For example, multi-radio implementations can allow access to more than one network, providing increased coverage and data processing as well as improved interoperability between networks. A small number of multi-radio wireless sensor solutions exist at present but require the use of more than one radio transceiver devices to achieve multi-band operation. This paper presents the design of a novel prototype multi-radio hardware platform that uses a single radio transceiver. The proposed design allows multi-band operation in the 433/868MHz ISM bands and this, together with its low complexity and small form factor, make it suitable for a wide range of BSN applications.

Biomethane production from food waste and organic residues

Anaerobic digestion (AD) of biodegradable waste is an environmentally and economically sustainable solution which incorporates waste treatment and energy recovery. The organic fraction of municipal solid waste (OFMSW), which comprises mostly of food waste, is highly degradable under anaerobic conditions. Biogas produced from OFMSW, when upgraded to biomethane, is recognised as one of the most sustainable renewable biofuels and can also be one of the cheapest sources of biomethane if a gate fee is associated with the substrate. OFMSW is a complex and heterogeneous material which may have widely different characteristics depending on the source of origin and collection system used. The research presented in this thesis investigates the potential energy resource from a wide range of organic waste streams through field and laboratory research on real world samples. OFMSW samples collected from a range of sources generated methane yields ranging from 75 to 160 m3 per tonne. Higher methane yields are associated with source segregated food waste from commercial catering premises as opposed to domestic sources. The inclusion of garden waste reduces the specific methane yield from household organic waste. In continuous AD trials it was found that a conventional continuously stirred tank reactor (CSTR) gave the highest specific methane yields at a moderate organic loading rate of 2 kg volatile solids (VS) m-3 digester day-1 and a hydraulic retention time of 30 days. The average specific methane yield obtained at this loading rate in continuous digestion was 560 ± 29 L CH4 kg-1 VS which exceeded the biomethane potential test result by 5%. The low carbon to nitrogen ratio (C: N <14:1) associated with canteen food waste lead to increasing concentrations of volatile fatty acids in line with high concentrations of ammonia nitrogen at higher organic loading rates. At an organic loading rate of 4 kg VS m-3day-1 the specific methane yield dropped considerably (381 L CH4 kg-1 VS), the pH rose to 8.1 and free ammonia (NH3 ) concentrations reached toxicity levels towards the end of the trial (ca. 950 mg L-1). A novel two phase AD reactor configuration consisting of a series of sequentially fed leach bed reactors connected to an upflow anaerobic sludge blanket (UASB) demonstrated a high rate of organic matter decay but resulted in lower specific methane yields (384 L CH4 kg-1 VS) than the conventional CSTR system.

High-efficiency photovoltaics through mechanically stacked integration of solar cells based on the InP lattice constant

Solar Energy is a clean and abundant energy source that can help reduce reliance on fossil fuels around which questions still persist about their contribution to climate and long-term availability. Monolithic triple-junction solar cells are currently the state of the art photovoltaic devices with champion cell efficiencies exceeding 40%, but their ultimate efficiency is restricted by the current-matching constraint of series-connected cells. The objective of this thesis was to investigate the use of solar cells with lattice constants equal to InP in order to reduce the constraint of current matching in multi-junction solar cells. This was addressed by two approaches: Firstly, the formation of mechanically stacked solar cells (MSSC) was investigated through the addition of separate connections to individual cells that make up a multi-junction device. An electrical and optical modelling approach identified separately connected InGaAs bottom cells stacked under dual-junction GaAs based top cells as a route to high efficiency. An InGaAs solar cell was fabricated on an InP substrate with a measured 1-Sun conversion efficiency of 9.3%. A comparative study of adhesives found benzocyclobutene to be the most suitable for bonding component cells in a mechanically stacked configuration owing to its higher thermal conductivity and refractive index when compared to other candidate adhesives. A flip-chip process was developed to bond single-junction GaAs and InGaAs cells with a measured 4-terminal MSSC efficiency of 25.2% under 1-Sun conditions. Additionally, a novel InAlAs solar cell was identified, which can be used to provide an alternative to the well established GaAs solar cell. As wide bandgap InAlAs solar cells have not been extensively investigated for use in photovoltaics, single-junction cells were fabricated and their properties relevant to PV operation analysed. Minority carrier diffusion lengths in the micrometre range were extracted, confirming InAlAs as a suitable material for use in III-V solar cells, and a 1-Sun conversion efficiency of 6.6% measured for cells with 800 nm thick absorber layers. Given the cost and small diameter of commercially available InP wafers, InGaAs and InAlAs solar cells were fabricated on alternative substrates, namely GaAs. As a first demonstration the lattice constant of a GaAs substrate was graded to InP using an InxGa1-xAs metamorphic buffer layer onto which cells were grown. This was the first demonstration of an InAlAs solar cell on an alternative substrate and an initial step towards fabricating these cells on Si. The results presented offer a route to developing multi-junction solar cell devices based on the InP lattice parameter, thus extending the range of available bandgaps for high efficiency cells.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

Molecular mechanisms underpinning IFN-gamma and TNF-alpha synergy in intestinal epithelial cells

Cytokine-driven signalling shapes immune homeostasis and guides inflammatory responses mainly through induction of specific gene expression programmes both within and outside the immune cell compartment. These transcriptional outputs are often amplified via cytokine synergy, which sets a stimulatory threshold that safeguards from exacerbated inflammation and immunopathology. In this study, we investigated the molecular mechanisms underpinning synergy between two pivotal Th1 cytokines, IFN-γ and TNF-α, in human intestinal epithelial cells. These two proinflammatory mediators induce a unique state of signalling and transcriptional synergy implicated in processes such as antiviral and antitumour immunity, intestinal barrier and pancreatic β-cell dysfunction. Since its discovery more than 30 years ago, this biological phenomenon remains, however, only partially defined. Here, using a functional genomics approach including RNAi perturbation screens and small-molecule inhibitors, we identified two new regulators of IFN-γ/TNF-α-induced chemokine and antiviral gene and protein expression, a Bcl-2 protein BCL-G and a histone demethylase UTX. We also discovered that IFN-γ/TNF-α synergise to trigger a coordinated shutdown of major receptor tyrosine kinases expression in colon cancer cells. Together, these findings extend our current understanding of how IFN-γ/TNF-α synergy elicits qualitatively and quantitatively distinct outputs in the intestinal epithelium. Given the well-documented role of this synergistic state in immunopathology of various disorders, our results may help to inform the identification of high quality and biologically relevant druggable targets for diseases characterised by an IFN-γ/TNF-α high immune signature