University of Greenwich

About: University of Greenwich is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 3749 authors who have published 9958 publications receiving 234340 citations.

Risk perception, experience, and objective risk: A cross-national study with European emergency survivors

Abstract: Understanding public risk perceptions and their underlying processes is important in order to learn more about the way people interpret and respond to hazardous emergency events. Direct experience with an involuntary hazard has been found to heighten the perceived risk of experiencing the same hazard and its consequences in the future, but it remains unclear if cross-over effects are possible (i.e., experience with one hazard influencing perceived risk for other hazards also). Furthermore, the impact of objective risk and country of residence on perceived risk is not well understood. As part of the BeSeCu (Behavior, Security, and Culture) Project, a sample of 1,045 survivors of emergencies from seven European countries (i.e., Germany, the Czech Republic, Poland, Sweden, Spain, Turkey, and Italy) was drawn. Results revealed heightened perceived risk for emergency events (i.e., domestic and public fires, earthquakes, floods, and terrorist attacks) when the event had been experienced previously plus some evidence of cross-over effects, although these effects were not so strong. The largest country differences in perceived risk were observed for earthquakes, but this effect was significantly reduced by taking into account the objective earthquake risk. For fires, floods, terrorist attacks, and traffic accidents, only small country differences in perceived risk were found. Further studies including a larger number of countries are welcomed.

Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets

Abstract: The recent growth in neural network hydrological modelling has focused on the provision of river flow estimates of one kind or another. Little or no scientific research has been undertaken to assess the potential benefits for modelling sediment transfer. Some initial pathfinder experiments were therefore conducted to assess the competence of a backpropagation network to produce a combined model of sediment transfer occurring under different types of agriculture and land management conservation regimes. The results of this investigation demonstrate that a neural network solution is able to exceed the limitations of traditional multiple linear regression. The potential to create multiple solutions at different levels of generalisation and robust solutions that can be transferred to unknown catchment types is illustrated.

QFD‐based modular logistics service design

Abstract: Purpose – This paper aims to develop a framework of QFD (quality function deployment)‐based logistics service design to integrate the HOQ (house of quality) technique and modular logic to help in designing logistics services with high quality and a large service variety.Design/methodology/approach – Based on a literature review, a conceptual research framework is built integrating the QFD method and modular logic together. A case study is used to illustrate a real application in logistics service design of the third‐party logistics (3PL) provider.Findings – The results show that QFD and modularity used simultaneously as design principles can ensure service design quality at three layers (service, process, activity) in the modular logistics service platform.Research limitations/implications – This paper provides multi‐disciplinary insights for both industry and academics on how QFD/HOQ and modular logic can be integrated to systematically translate customer requirements into logistics service designs.Pract...