The effects of varying forms and degrees of government intervention upon the effective competitiveness of UK small businesses

This doctoral thesis responds to the need for greater understanding of small businesses and their inherent unique problem-types. Integral to the investigation is the theme that for governments to effectively influence small business, a sound understanding of the factors they are seeking to influence is essential. Moreover, the study, in its recognition of the many shortcomings in management research and, in particular that the research methods and approaches adopted often fail to give adequate understanding of issues under study, attempts to develop an innovative and creative research approach. The aim thus being to produce, not only advances in small business management knowledge from the standpoints of government policy makers and `lq recipient small business, but also insights into future potential research method for the continued development of that knowledge. The origins of the methodology lay in the non-acceptance of traditional philosophical positions in epistemology and ontology, with a philosophical standpoint of internal realism underpinning the research. Internal realism presents the basis for the potential co-existence of qualitative and quantitative research strategy and underlines the crucial contributory role of research method in provision of ultimate factual status of the assertions of research findings. The concept of epistemological bootstrapping is thus used to develop a `lq partial research framework to foothold case study research, thereby avoiding limitations of objectivism and brute inductivism. The major insights and issues highlighted by the `lq bootstrap, guide the researcher around the participant case studies. A novel attempt at contextualist (linked multi-level and processual) analysis was attempted in the major in-depth case study, with two further cases playing a support role and contributing to a balanced emphasis of empirical research within the context of time constraints inherent within part-time research.

Cardiac dysfunction and cell damage across clinical stages of severity in growth-restricted fetuses

Objective - The purpose of this study was to assess cardiac function and cell damage in intrauterine growth-restricted (IUGR) fetuses across clinical Doppler stages of deterioration. Study Design - One hundred twenty appropriate-for-gestational-age and 81 IUGR fetuses were classified in stages 1/2/3 according umbilical artery present/absent/reversed end-diastolic blood flow, respectively. Cardiac function was assessed by modified-myocardial performance index, early-to-late diastolic filling ratios, cardiac output, and cord blood B-type natriuretic peptide; myocardial cell damage was assessed by heart fatty acid–binding protein, troponin-I, and high-sensitivity C-reactive protein. Results - Modified-myocardial performance index, blood B-type natriuretic peptide, and early-to-late diastolic filling ratios were increased in a stage-dependent manner in IUGR fetuses, compared with appropriate-for-gestational-age fetuses. Heart fatty acid–binding protein levels were higher in IUGR fetuses at stage 3, compared with control fetuses. Cardiac output, troponin-I, and high-sensitivity C-reactive protein did not increase in IUGR fetuses at any stage. Conclusion - IUGR fetuses showed signs of cardiac dysfunction from early stages. Cardiac dysfunction deteriorates further with the progression of fetal compromise, together with the appearance of biochemical signs of cell damage.

A world full of surprises:Bayesian theory of surprise to quantify degrees of uncertainty

In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a "surprising" event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks. Copyright © 2014 ACM.

Ultrahigh step-up dc-dc converter for distributed generation by three Degrees of Freedom (3DoF) approach

This paper proposes a novel dc-dc converter topology to achieve an ultrahigh step-up ratio while maintaining a high conversion efficiency. It adopts a three degree of freedom approach in the circuit design. It also demonstrates the flexibility of the proposed converter to combine with the features of modularity, electrical isolation, soft-switching, low voltage stress on switching devices, and is thus considered to be an improved topology over traditional dc-dc converters. New control strategies including the two-section output voltage control and cell idle control are also developed to improve the converter performance. With the cell idle control, the secondary winding inductance of the idle module is bypassed to decrease its power loss. A 400-W dc-dc converter is prototyped and tested to verify the proposed techniques, in addition to a simulation study. The step-up conversion ratio can reach 1:14 with a peak efficiency of 94% and the proposed techniques can be applied to a wide range of high voltage and high power distributed generation and dc power transmission.

Impact of severity and type of depression on quality of life in cases identified in the community

BACKGROUND: The impact of different levels of depression severity on quality of life (QoL) is not well studied, particularly regarding ICD-10 criteria. The ICD classification of depressive episodes in three levels of severity is also controversial and the less severe category, mild, has been considered as unnecessary and not clearly distinguishable from non-clinical states. The present work aimed to test the relationship between depression severity according to ICD-10 criteria and several dimensions of functioning as assessed by Medical Outcome Study (MOS) 36-item Short Form general health survey (SF-36) at the population level. METHOD: A sample of 551 participants from the second phase of the Outcome of Depression International Network (ODIN) study (228 controls without depression and 313 persons fulfilling ICD criteria for depressive episode) was selected for a further assessment of several variables, including QoL related to physical and mental health as measured with the SF-36. RESULTS: Statistically significant differences between controls and the depression group were found in both physical and mental markers of health, regardless of the level of depression severity; however, there were very few differences in QoL between levels of depression as defined by ICD-10. Regardless of the presence of depression, disability, widowed status, being a woman and older age were associated with worse QoL in a structural equation analysis with covariates. Likewise, there were no differences according to the type of depression (single-episode versus recurrent). CONCLUSIONS: These results cast doubt on the adequacy of the current ICD classification of depression in three levels of severity.