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.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.

The familiar and the strange: the limits of universal design in the European context

Keynote Presentation paper

Capillary electrophoresis with boron doped diamond electrode for amperometric detection of low molecular weight biological substrate

The research work in this thesis included the sensitive and selective separation of biological substance by capillary electrophoresis with a boron doped diamond electrode for amperometric detection. Chapter 1 introduced the capillary electrophoresis and electrochemical detection. It included the different modes of capillary electrophoresis, polyelectrolyte multilayers coating for open tubular capillary electrochromatography, different modes of electrochemical detection and carbon based electrodes. Chapter 2 showed the synthesized and electropolymerized N-acetyltyramine with a negatively charged sulfobutylether-β-cyclodextrin on a boron doped diamond (BDD) electrode followed by the electropolymerzation of pyrrole to form a stable and permselective film for selective dopamine detection. For comparison, a glassy carbon (GC) electrode with a combined electropolymerized permselective film of polytyramine and polypyrrole-1-propionic acid was used for selective detection of dopamine. The detection limit of dopamine was improved from 100 nM at a GC electrode to 5 nM at a BDD electrode. Chapter 3 showed field-amplified sample stacking using a fused silica capillary coated with gold nanoparticles embedded in poly(diallyldimethylammonium) chloride, which has been investigated for the electrophoretic separation of indoxyl sulphate, homovanillic acid and vanillylmandelic acid. The detection limit of the three analytes obtained by using a boron doped diamond electrode was around 75 nM, which was significantly below their normal physiological levels in biological fluids. This combined separation and detection scheme was applied to the direct analysis of these analytes and other interfereing chemicals including uric and ascorbic acids in urine samples without off-line sample treatment or preconcentration. Chapter 4 showed the selective detection of Pseudomonas Quinolone Signal, PQS for quorum sensing from its precursor HHQ, using a simply boron doped diamond electrode. Furthermore, by combining poly(diallyldimethylammonium) chloride modified fused silica capillary with a BDD electrode for amperometric detection, PQS was separated from HHQ and other analogues. The detection limit of PQS was as low as 65 nM. Different P. aeruginosa mutant strains were studied. Chapter 5 showed the separation of aminothiols by layer-by-layer coating of silica capillary with a boron doped diamond electrode. The capillary was layer-by-layer coated with the polycation poly(diallyldimethylammonium) chloride and negatively charged silica nanoparticles. All the aminothiols was separated and detected using a BDD electrode in an acidic electrolyte. It was a novel scheme for the separation and detection of glutathione reduced and oxidized forms, which is important for estimated overstressed level in the human system.