Bond graph models of DC-DC converters operating in both CCM and DCM

In this paper, Bond Graphs are employed to develop a novel mathematical model of conventional switched-mode DC-DC converters valid for both continuous and discontinuous conduction modes. A unique causality bond graph model of hybrid models is suggested with the operation of the switch and the diode to be represented by a Modulated Transformer with a binary input and a resistor with fixed conductance causality. The operation of the diode is controlled using an if-then function within the model. The extracted hybrid model is implemented on a Boost and Buck converter with their operations to change from CCM to DCM and to return to CCM. The vector fields of the models show validity in a wide operation area and comparison with the simulation of the converters using PSPICE reveals high accuracy of the proposed model, with the Normalised Root Means Square Error and the Maximum Absolute Error remaining adequately low. The model is also experimentally tested on a Buck topology.

The determinants of a cross market arbitrage opportunity: theory and evidence for the European bond market

This paper examines the determinants of cross-platform arbitrage profits. We develop a structural model that enables us to decompose the likelihood of an arbitrage opportunity into three distinct factors: the fixed cost to trade the opportunity, the level at which one of the platforms delays a price update and the impact of the order flow on the quoted prices (inventory and asymmetric information effects). We then investigate the predictions from the theoretical model for the European Bond market with the estimation of a probit model. Our main finding is that the results found in the empirical part corroborate strongly the predictions from the structural model. The event of a cross market arbitrage opportunity has a certain degree of predictability where an optimal ex ante scenario is represented by a low level of spreads on both platforms, a time of the day close to the end of trading hours and a high volume of trade.

Investigating the impact of defects on key stakeholders in the UK new housing sector

The UK house building sector is facing dual pressures to expand supply, along with delivering against tougher Building Regulations’ requirements, predominantly in the areas of sustainability. A review of current literature has highlighted that the pressures the UK house building industry is currently under may be having a negative impact on build quality, causing an increase in defects. A review and synthesis of the current defect literature with respect to new-build housing and the wider construction sector has found that the prevailing emphasis is limited to the classification, causes, pathology and statistical analysis of defects. There is thus a need to better understand the overall impact of individual defects on key stakeholders within the new-build housing defect detection and remediation process. As part of ongoing research to develop and verify a defect impact assessment rating system, this paper seeks to contribute to our understanding of the impact of individual defects from a key stakeholder perspective by undertaking the literature review and synthesis phase. The literature review identifies the three distinct, but interrelated, dominant impact factors: cost, disruption, and health and safety. By pulling the strands of defect literature together the theoretical lens and key stakeholder sampling strategy is formed as the basis for the subsequent impact weighting development phase.

Detecting defects in UK new-build housing: a learning perspective

House builders play a key role in controlling the quality of new homes in the UK. The UK house building sector is, however, currently facing pressures to expand supply as well as conform to tougher low carbon planning and Building Regulation requirements; primarily in the areas of sustainability. There is growing evidence that the pressure the UK house building industry is currently under may be eroding build quality and causing an increase in defects. It is found that the prevailing defect literature is limited to the causes, pathology and statistical analysis of defects (and failures). The literature does not extend to examine how house builders individually and collectively, in practice, collect and learn from defects experience in order to reduce the prevalence of defects in future homes. The theoretical lens for the research is organisational learning. This paper contributes to our understanding of organisational learning in construction through a synthesis of current literature. Further, a suitable organisational learning model is adopted. The paper concludes by reporting the research design of an ongoing collaborative action research project with the National House Building Council (NHBC), focused on developing a better understanding of house builders’ localised defects analysis procedures and learning processes.

Reducing defects through organizational learning within a Housing Association environment

Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

Detecting defects in the UK new-build housing sector: a learning perspective

Rapid growth in the production of new homes in the UK is putting build quality under pressure as evidenced by an increase in the number of defects. Housing associations (HAs) contribute approximately 20% of the UK’s new housing supply. HAs are currently experiencing central government funding cuts and rental revenue reductions. As part of HAs’ quest to ramp up supply despite tight budget conditions, they are reviewing how they learn from defects. Learning from defects is argued as a means of reducing the persistent defect problem within the UK housebuilding industry, yet how HAs learn from defects is under-researched. The aim of this research is to better understand how HAs, in practice, learn from past defects to reduce the prevalence of defects in future new homes. The theoretical lens for this research is organizational learning. The results drawn from 12 HA case studies indicate that effective organizational learning has the potential to reduce defects within the housing sector. The results further identify that HAs are restricting their learning to focus primarily on reducing defects through product and system adaptations. Focusing on product and system adaptations alone suppresses HAs’ abilities to reduce defects in the future.