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The use of personality tests throughout Canadian society is based on the assumption that their results are valid. However, research has shown that individuals can, and do, fake their responses on personality inventories. Individuals may fake good, emphasizing their positive characteristics, or fake bad, emphasizing negative characteristics, in order to obtain a desired outcome. Recent research has provided support for a congruence model of faking, which states that schema-consistent responses are provided more quickly than schema-inconsistent responses. Faking successfully, without being detected by validity indices, requires balancing favourable and unfavourable responses, regardless of the faking schema a participant adopts. This demand results in cognitive fatigue over time, producing increasingly unbalanced response patterns. Two studies were conducted to evaluate the efficacy of the congruence and cognitive overload models of faking in detecting instructed faking, and to examine whether these models or the newly developed Faking Response Strategy Scales provide added value in detecting faking relative to currently established gold-standard measures. Results showed that all of the self-report scales examined—whether traditional or new—were valid detectors of faking, which supports their ongoing use. However, results highlighted the weakness of the Impression Management subscale of the Balanced Inventory of Desirable Responding, the current gold-standard in the field, in providing added value relative to other scales. Response latency data supported the congruence model of faking, but results for the cognitive overload model were mixed: Study 1 data supported the cognitive overload model, but time constraints introduced in Study 2 seem to have caused random responding, rather than increasing cognitive overload as was intended. Results supported a multidimensional model of faking, and show that adding measures of response latency and response pattern can enhance the ability of traditional measures to detect faking. These findings have important theoretical and practical implications for methods of detecting faking and for the understanding of cognitive processes underlying faking. / Thesis (Master, Psychology) -- Queen's University, 2013-09-28 11:00:11.347

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Automated fault diagnosis in induction machines is a difficult task and normally requires background information of electrical machines. Here a different methodology to the condition monitoring problem is devised. The approach is based entirely on Digital Signal Processing (DSP) and Statistical Pattern Recognition (PR). Description of machine conditions is extracted from empirical data. The main tasks that must be carried out by a PR-based condition monitoring system are: condition identification, knowledge reinforcement and knowledge creation for previously unseen conditions. The DSP operations are employed to quickly isolate sensor faults and to remove noise using data acquired from a single channel. DSP transformations may seem promising in making the monitoring system portable. Most importantly, they can compensate for operational changes in the machine. These changes affect the supply line currents and the primary signal quantities to be measured, i.e. the current and the axial leakage flux. The data which is input to the statistical monitoring system may be transformed, in the form of features, or remain unaltered. The system exploits the statistical properties of the feature vectors. The particular features, namely the LAR coefficients, convey short-term, high-resolution spectral information. For a long record, the feature vector sequence may provide information about changes in the record spectral characteristics, with time. Many induction machine processes are stationary and they can be properly be dealt with by a simple statistical classifier, e.g. a Gaussian model. For nonstationary processes, the system may employ a more comprehensive tool, namely the Hidden Markov Model. which may track the changing behaviour of the process in question. Initially a limited number of machine conditions are available to the process engineer. By identifying their boundaries, new faulty conditions could be signalled for and adopted into the database.

(1) Human trypsin was purified from human pancreatic tissue and used as an immunogen in the production of monoclonal antibodies. It was also used as a standard preparation of human trypsin in the screening of monoclonal antibodies and in the development of an immunoradiometric assay (IRMA). (2) Eight monoclonal antibodies were raised to human trypsin, two of which were subsequently employed in the development of an IRMA to measure immunoreactive trypsin (IRT) in blood and blood spots. (3) An antiserum to human trypsin was raised in sheep and employed in a sandwich ELISA. (4) Monoclonal antibodies were tested by a number of different screening systems including ELISA, immunoblotting and immunoadsorption assays with 125I-labelled human trypsin. (5) Six selected monoclonal antibodies were tested for use (a) as the solid phase antibody and (b) as the radioactive tracer in an IRMA, and against each other for compatability in an IRMA. (6) One combination of antibodies was chosen and experiments performed to determine (a) lack of competition of binding to antigen between the two and (b) positive binding of the combination to serum IRT. (7) An IRMA to measure IRT in blood and blood spots was developed using two monoclonal antibodies, 56/C5/33 (IgG2a) and 125I-labelled 55/A4/31 (IgM). (8) The detection limit of the assay with purified cationic human trypsin as the analyte was determined to be 0.8 ng trypsin. (9) The IRMA was performed with reconstituted blood containing standards of human trypsin (either blood or dried blood spots on filter paper). (10) There is potential for this assay to be further developed and employed as a routine screening assay to detect elevated concentrations of IRT in dried blood spots from newborn infants with cystic fibrosis.