Interspecific competition between the potato cyst nematode species Globodera pallida and G. rostochiensis

The two potato cyst nematode species, Globodera pallida and G. rostochiensis, are among the most important pests of potato. PCN are difficult to manage, while the two species respond differently to the main control methods. An increase in the incidence of G. pallida had been reported and is generally attributed to greater effectiveness of control measures against G. rostochiensis. The status of PCN in Ireland was studied using PCR. The results demonstrated qPCR to be an efficient means of high-throughput PCN sampling, being able to accurately identify both species in mixed-species populations. Species discrimination using qPCR revealed an increase in the incidence of G. pallida in Ireland in the absence of G. pallida-selective control measures. The population dynamics of G. pallida and G. rostochiensis in Ireland were studied in mixed- and single-species competition assays in vivo. G. pallida proved to be the more successful species, with greater multiplication in mixed- than single-species populations, with G. rostochiensis showing the opposite. This effect was similarly observed in staggered inoculation trials and population proportion trials. It was hypothesised that the greater G. pallida competitiveness could be attributed to its later hatch. G. pallida exhibited a later peak in hatching activity and more prolonged hatch, relative to G. rostochiensis. G. rostochiensis hatch was significantly reduced in mixedspecies hatching assays. G. pallida hatch was significantly higher when hatch was induced in potato root leachates containing G. rostochiensis-specific compounds, indicating that G. pallida hatch is stimulated upon perception of G. rostochiensis–derived compounds. Rhizotron studies revealed that root damage, caused by feeding of the early-hatching G. rostochiensis, resulted in increased lateral root proliferation and significantly increased G. pallida multiplication. Split-root trials indicated a significant G. pallida-induced ISR effect. G. rostochiensis multiplication was significantly reduced in split-root rhizotrons when G. pallida colonised roots before or after G. rostochiensis infection.

Symptom burden in individuals with inflammatory bowel disease: a mixed methods study

To investigate the symptom burden experiences of individuals with inflammatory bowel disease (IBD). An explanatory sequential mixed methods study was conducted. A cross-sectional, correlational survey was first undertaken. Symptom burden was measured using a modified disease specific version of the Memorial Symptom Assessment Scale, which was administered to a consecutive sample of individuals with IBD (n = 247) at an IBD Outpatients department in one urban teaching hospital in Ireland. Disease activity was determined using clinical disease activity indices, which were completed by the consulting physician. A sequential qualitative, descriptive study was then conducted aimed at explaining noteworthy quantitative findings. A criterion-related purposeful sample of seven participants from the quantitative study was recruited. Semi-structured face to face interviews were conducted using an interview guide and data were analysed using content analysis. Findings revealed that participants experienced a median of 10 symptoms during the last week, however as many as 16 symptoms were experienced during active disease. The most burdensome symptoms were lack of energy, bowel urgency, diarrhoea, feeling bloated, flatulence and worry. Total symptom burden was found to be low with a mean score of 0.56 identified out of a possible range from 0 to 4. Participants with active disease (M = 0.81, SD = 0.48; n = 68) had almost double mean total symptom burden scores than participants with inactive disease (M = 0.46, SD = 0.43; n = 166) (p < 0.001). Mean total psychological symptom burden was found to be significantly greater than mean total physical symptom burden (rho = 0.73, n = 247, p < 0.001). Self-reported disease control, gender, number of flare ups in the last two years, and smoking status was found to be significant predictors of total symptom burden, with self-reported disease control identified as the strongest predictor. Qualitative data revealed tiredness, pain, bowel symptoms, worry and fear as being burdensome. Furthermore, symptom burden experiences were described in terms of its impact on restricting aspects of daily activities, which accumulated into restrictions on general life events. Psychological symptom burden was revealed as more problematic than physical symptom burden due to its constant nature, with physical and psychological symptoms described to occur in a cyclical manner. Participants revealed that disease control was evaluated not only in terms of symptoms, but also in terms of their abilities to control the impact of symptoms on their lives. This study highlights the considerable number of symptoms and the most burdensome symptoms experienced by individuals with IBD, both during active and inactive disease. This study has important implications on symptom assessment in terms of the need to encompass both physical and psychological symptoms. In addition, greater attention needs to be placed on psychological aspects of IBD care.

Assessing the impulse control of violent offenders using computer-based cognitive performance tasks

The topic of this thesis is impulsivity. The meaning and measurement of impulse control is explored, with a particular focus on forensic settings. Impulsivity is central to many areas of psychology; it is one of the most common diagnostic criteria of mental disorders and is fundamental to the understanding of forensic personalities. Despite this widespread importance there is little agreement as to the definition or structure of impulsivity, and its measurement is fraught with difficulty owing to a reliance on self-report methods. This research aims to address this problem by investigating the viability of using simple computerised cognitive performance tasks as complementary components of a multi-method assessment strategy for impulse control. Ultimately, the usefulness of this measurement strategy for a forensic sample is assessed. Impulsivity is found to be a multifaceted construct comprised of a constellation of distinct sub-dimensions. Computerised cognitive performance tasks are valid and reliable measures that can assess impulsivity at a neuronal level. Self-report and performance task methods assess distinct components of impulse control and, for the optimal assessment of impulse control, a multi-method battery of self-report and performance task measures is advocated. Such a battery is shown to have demonstrated utility in a forensic sample, and recommendations for forensic assessment in the Irish context are discussed.

Application of signal detection methods to fisheries management

The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Improved kinematic sensing for motion control applications

New compensation methods are presented that can greatly reduce the slit errors (i.e. transition location errors) and interval errors induced due to non-idealities in optical incremental encoders (square-wave). An M/T-type, constant sample-time digital tachometer (CSDT) is selected for measuring the velocity of the sensor drives. Using this data, three encoder compensation techniques (two pseudoinverse based methods and an iterative method) are presented that improve velocity measurement accuracy. The methods do not require precise knowledge of shaft velocity. During the initial learning stage of the compensation algorithm (possibly performed in-situ), slit errors/interval errors are calculated through pseudoinversebased solutions of simple approximate linear equations, which can provide fast solutions, or an iterative method that requires very little memory storage. Subsequent operation of the motion system utilizes adjusted slit positions for more accurate velocity calculation. In the theoretical analysis of the compensation of encoder errors, encoder error sources such as random electrical noise and error in estimated reference velocity are considered. Initially, the proposed learning compensation techniques are validated by implementing the algorithms in MATLAB software, showing a 95% to 99% improvement in velocity measurement. However, it is also observed that the efficiency of the algorithm decreases with the higher presence of non-repetitive random noise and/or with the errors in reference velocity calculations. The performance improvement in velocity measurement is also demonstrated experimentally using motor-drive systems, each of which includes a field-programmable gate array (FPGA) for CSDT counting/timing purposes, and a digital-signal-processor (DSP). Results from open-loop velocity measurement and closed-loop servocontrol applications, on three optical incremental square-wave encoders and two motor drives, are compiled. While implementing these algorithms experimentally on different drives (with and without a flywheel) and on encoders of different resolutions, slit error reductions of 60% to 86% are obtained (typically approximately 80%).