The People-Game-Play model for understanding videogames’ impact on wellbeing

Given the increasing popularity of videogames, understanding when, how and for whom they have a positive or negative impact on wellbeing is critical. We propose a model for exploring these questions based on existing literature and our own research. The People-Game-Play model identifies player characteristics, game features and the experience of play as key determinants of the impact of videogame play on wellbeing. We propose research exploring the relationships within and between each of these key factors is needed and identify some examples of future research in this space.

A fuzzy TOPSIS based multi criteria performance measurement model for lean supply chain

Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.

Packed red blood cells suppress myeloid dendritic cell and monocyte inflammatory responses in a whole blood model of transfusion

Purpose/Objective: The basis for poor outcomes in some patients post transfusion remains largely unknown. Despite leukodepletion, there is still evidence of immunomodulatory effects of transfusion that require further study. In addition, there is evidence that the age of blood components transfused significantly affects patient outcomes. Myeloid dendritic cell (DC) and monocyte immune function were studied utilising an in vitro whole blood model of transfusion. Materials and methods: Freshly collected (‘recipient’) whole blood was cultured with ABO compatible leukodepleted PRBC at 25% blood replacement-volume (6hrs). PRBC were assayed at [Day (D) 2, 14, 28and 42 (date-of expiry)]. In parallel, LPS or Zymosan (Zy) were added to mimic infection. Recipients were maintained for the duration of the time course (2 recipients, 4 PRBC units, n = 8).Recipient DC and monocyte intracellular cytokines and chemokines (IL-6, IL-10, IL-12,TNF-a, IL-1a, IL-8, IP-10, MIP-1a, MIP-1b, MCP-1) were measured using flow cytometry. Changes in immune response were calculated by comparison to a parallel no transfusion control (Wilcoxin matched pairs). Influence of storage age was calculated using ANOVA. Results: Significant suppression of DC and monocyte inflammatory responses were evident. DC and monocyte production of IL-1a was reduced following exposure to PRBC regardless of storage age (P < 0.05 at all time points). Storage independent PRBC mediated suppression of DC and monocyte IL-1a was also evident in cultures costimulated with Zy. In cultures co-stimulated with either LPS or Zy, significant suppression of DC and monocyte TNF-a and IL-6 was also evident. PRBC storage attenuated monocyte TNF-a production when co-cultured with LPS (P < 0.01 ANOVA). DC and monocyte production of MIP-1a was significantly reduced following exposure to PRBC (DC: P < 0.05 at D2, 28, 42; Monocyte P < 0.05 all time points). In cultures co-stimulated with LPS and zymosan, a similar suppression of MIP-1a production was also evident, and production of both DC and monocyte MIP-1b and IP-10 were also significantly reduced. Conclusions: The complexity of the transfusion context was reflected in the whole blood approach utilised. Significant suppression of these key DC and monocyte immune responses may contribute to patient outcomes, such as increased risk of infection and longer hospital stay, following blood transfusion.

Bridging the biology of the ovine TRALI model – human inflammatory cell responses to TRALI inducing supernatants

Background Transfusion-related acute lung injury (TRALI) is a serious and potentially fatal consequence of transfusion. A two-event TRALI model demonstrated date-of-expiry - day (D) 5 platelet (PLT) and D42 packed red blood cell (PRBC) supernatants (SN) induced TRALI in LPS-treated sheep. We have adapted a whole blood transfusion culture model as an investigative bridge between the ovine TRALI model human responses to transfusion. Methods A whole blood transfusion model was adapted to replicate the ovine model - specifically +/- 0.23μg/mL LPS as the first event and 10% SN volume (transfusion) as the second event. Four pooled SN from blood products, previously used in the TRALI ovine model, were investigated: D1-PLT, D5-PLT, D1-PRBC, and D42-PRBC. Fresh human whole blood (recipient) was mixed with combinations of LPS and BP-SN stimuli and incubated in vitro for 6 hrs. Addition of golgi plug enabled measurement of monocyte cytokine production (IL-6, IL-8, IL-10, IL-12, TNF-α, IL-1α, CXCL-5, IP-10, MIP-1α, MCP-1) using multi-colour flow cytometry. Responses for 6 recipients were assessed. Results In the presence of LPS, D42-PRBC-SN significantly increased monocyte IL-6 (P=0.031), IL-8 (P=0.016) and IL-1α (P=0.008) production compared to D1-PRBC-SN. This response to D42-PRBC-SN was LPS-dependent, and was not evident in non-LPSstimulated controls. This response was also specific to D42-PRBC-SN, as similar changes were not evident for the D5-PLT-SN, compared to the D1-PLT-SN, regardless of the presence of LPS. D5-PLT-SN significantly increased IL-12 production (P=0.024) compared to D1-PLT-SN. This response was again LPS-dependent. Conclusions These data demonstrate a novel two-event mechanism of monocyte inflammatory response that was dependent upon both the presence of date-of-expiry blood product SN and LPS. Further, these results demonstrate different cytokines responses induced by date-of-expiry PLT-SN and PRBC-SN. These data are consistent with the evidence from the ovine TRALI model, and enhancing its relevance to transfusion related changes in humans.

Storyboard augmentation of process model grammars for stakeholder communication

Process models are often used to visualize and communicate workflows to involved stakeholders. Unfortunately, process modeling notations can be complex and need specific knowledge to be understood. Storyboards, as a visual language to illustrate workflows as sequences of images, provide natural visualization features that allow for better communication, to provide insight to people from non-process modelling expert domains. This paper proposes a visualization approach using a 3D virtual world environment to visualize storyboards for business process models. A prototype was built to present its applicability via generating output with examples of five major process model patterns and two non-trivial use cases. Illustrative results for the approach show the promise of using a 3D virtual world to visualize complex process models in an unambiguous and intuitive manner.

Bayesian indirect inference using a parametric auxiliary model

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and computationally easier to deal with. Such an approach has been well explored in the classical literature but has received substantially less attention in the Bayesian paradigm. The purpose of this paper is to compare and contrast a collection of what we call parametric Bayesian indirect inference (pBII) methods. One class of pBII methods uses approximate Bayesian computation (referred to here as ABC II) where the summary statistic is formed on the basis of the auxiliary model, using ideas from II. Another approach proposed in the literature, referred to here as parametric Bayesian indirect likelihood (pBIL), we show to be a fundamentally different approach to ABC II. We devise new theoretical results for pBIL to give extra insights into its behaviour and also its differences with ABC II. Furthermore, we examine in more detail the assumptions required to use each pBII method. The results, insights and comparisons developed in this paper are illustrated on simple examples and two other substantive applications. The first of the substantive examples involves performing inference for complex quantile distributions based on simulated data while the second is for estimating the parameters of a trivariate stochastic process describing the evolution of macroparasites within a host based on real data. We create a novel framework called Bayesian indirect likelihood (BIL) which encompasses pBII as well as general ABC methods so that the connections between the methods can be established.