Three-dimensional earth-contact heat flows: A comparison of simulated and measured data for a buried structure

If renewable energy sources are to be successfully utilised within the built environment then accurate simulation tools have an important role to play. This paper addresses a very specific part of the overall simulation process: that of earth-contact heat transfer which can be the cause of significant modelling errors. Using a three-dimensional numerical model, simulation results are compared with experimental data for a test room. It has been shown that for this case, neglecting climatic conditions (e.g. snow and rain) causes discrepancies between measured and calculated temperatures. A sensitivity study has also shown that thermophysical properties (like the soil thermal conductivity) have to be accurately known.     

Complex Systems Models for Strategic Decision Making

A number of simulation techniques may be applied to strategy and decision-making. In using such techniques it is important to understand the role of models, in the context of the wider decision-making process which is largely a communal and political process; the choice of a particular approach or technique depends not only on the type of decision being considered, but also on the stage of the decision-making process (which in turn affects the type of problem under consideration).Models which take into account the dynamics of a system can give insight into possible outcomes and indications of unintended consequences. Such models may not necessarily reproduce all aspects of a complex adaptive system; there is a continuum of modelling techniques, ranging from static equilibrium models through to agent-based approaches which can incorporate evolution and learning. The trade-offs between alternative approaches is discussed.     

Agent-Based Modelling — Intelligent Customer Relationship Management

The intelligent customer relationship management (iCRM) tool — built using agent-based modelling techniques — aims to illustrate how CRM investments can influence a customer population, giving a clearer view of potential return on investment (ROI). Unlike conventional approaches, this model considers the communication of customer experiences between members of a social network, incorporating the powerful influence of word of mouth on the adoption of products and services. The tool is an advance on traditional techniques that rely on macroscopic behaviours and aggregated customer data, while neglecting important network and spatial effects.     

Risk Mitigation of Immunogenicity: A Key to Personalized Retinal Gene Therapy

Gene therapy (GT) for ocular disorders has advanced the most among adeno-associated virus (AAV)-mediated therapies, with one product already approved in the market. The bank of retinal gene mutations carefully compiled over 30 years, the small retinal surface that does not require high clinical vector stocks, and the relatively immune-privileged environment of the eye explain such success. However, adverse effects due to AAV-delivery, though rare in the retina have led to the interruption of clinical trials. Risk mitigation, as the key to safe and efficient GT, has become the focus of ‘bedside-back-to-bench’ studies. Herein, we overview the inflammatory adverse events described in retinal GT trials and analyze which components of the retinal immunological environment might be the most involved in these immune responses, with a focus on the innate immune system composed of microglial surveillance. We consider the factors that can influence inflammation in the retina after GT such as viral sensors in the retinal tissue and CpG content in promoters or transgene sequences. Finally, we consider options to reduce the immunological risk, including dose, modified capsids or exclusion criteria for clinical trials. A better understanding and mitigation of immune risk factors inducing host immunity in AAV-mediated retinal GT is the key to achieving safe and efficient GT.     

Agent-Based Modelling — Treating Customers As Individuals Not Numbers

Developments in computer hardware and software over the last ten years have made modelling a cheap and powerful support tool in the business world. Routinely, simple models are used to understand how changes in the cost base of a product will affect profits and returns on investments. Used in this way models allow decision makers to investigate the sensitivities between variables to make more informed decisions.