Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

An interactive method for engaging the public health workforce with evidence

Introduction Systematic review authors are increasingly directing their attention to not only ensuring the robust processes and methods of their syntheses, but also to facilitating the use of their reviews by public health decision-makers and practitioners. This latter activity is known by several terms including knowledge translation, for which one definition is a ‘dynamic and iterative process that includes synthesis, exchange and ethically sound application of knowledge’.1 Unfortunately—and despite good intentions—the successful translation of knowledge has at times been inhibited by the failure of reviews to meet the needs of decision-makers, and the limitations of the traditional avenues by which reviews are disseminated.2 Encouraging the utilization of reviews by the public health workforce is a complex challenge. An unsupportive culture within the workforce, a lack of experience in assessing evidence, the use of traditional academic language in communication and the lack of actionable messages can all act as barriers to successful knowledge translation.3 Improving communication through developing strategies that include summaries, podcasts, webinars and translational tools which target key decision-makers such as HealthEvidence.org should be considered by authors as promising actions to support the uptake of reviews into practice.4,5 Earlier work has also suggested that to better meet the research evidence needs of public health professionals, authors should aim to produce syntheses that are actionable, relevant and timely.2 Further, review authors must interact more with those who will, or could use their reviews; particularly when determining the scope and questions to which a review will be directed.2 Unfortunately, individual engagement, ideal for examining complex issues and addressing particular concerns, is often difficult, particularly when attempting to reach large groups where for efficiency purposes, the strategy tends to be didactic, ‘lecturing’ and therefore less likely to change attitudes or encourage higher order thinking.6 …

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Dynamic Bayesian Networks (DBNs) provide a versatile platform for predicting and analysing the behaviour of complex systems. As such, they are well suited to the prediction of complex ecosystem population trajectories under anthropogenic disturbances such as the dredging of marine seagrass ecosystems. However, DBNs assume a homogeneous Markov chain whereas a key characteristics of complex ecosystems is the presence of feedback loops, path dependencies and regime changes whereby the behaviour of the system can vary based on past states. This paper develops a method based on the small world structure of complex systems networks to modularise a non-homogeneous DBN and enable the computation of posterior marginal probabilities given evidence in forwards inference. It also provides an approach for an approximate solution for backwards inference as convergence is not guaranteed for a path dependent system. When applied to the seagrass dredging problem, the incorporation of path dependency can implement conditional absorption and allows release from the zero state in line with environmental and ecological observations. As dredging has a marked global impact on seagrass and other marine ecosystems of high environmental and economic value, using such a complex systems model to develop practical ways to meet the needs of conservation and industry through enhancing resistance and/or recovery is of paramount importance.

Intergenerational collaborative drawing: A research method for researching with/about young children

This paper focuses on the methodological effectiveness of intergenerational collaborative drawing (ICD). A group of eight researchers trialled this particular approach to drawing, most of them for the first time. Each researcher drew with young children, peers and tertiary students, with drawings created over a period of six months. The eight researchers came together in a 'community of scholars' approach to this project because of two shared interests: (i) issues of social justice, access and equity; and (ii) arts-based education research methods. The researchers were curious how ICD might methodologically support their respective research processes. As knowledge and theory about young children becomes more complex, researchers need responsive methodological tools to ask new questions and conduct rigorous, ethical research. This partial account describes how drawing together might perform methodologically. The data reported here draws from the detailed field notes, drawings and reflections of the researchers. Conclusions arise from the analysis of these reflections, with the authors suggesting ways in which ICD might benefit research with young children.

Complex permittivity measurements of RF plasma polymerized polyterpenol organic thin films employing split post dielectric resonator

In the fields of organic electronics and biotechnology, applications for organic polymer thin films fabricated using low-temperature non-equilibrium plasma techniques are gaining significant attention because of the physical and chemical stability of thin films and the low cost of production. Polymer thin films were fabricated from non-synthetic terpinen-4-ol using radiofrequency polymerization (13.56 MHz) on low loss dielectric substrates and their permittivity properties were ascertained to determine potential applications for these organic films. Real and imaginary parts of permittivity as a function of frequency were measured using the variable angle spectroscopic ellipsometer. The real part of permittivity (k) was found to be between 2.34 and 2.65 in the wavelength region of 400–1100 nm, indicating a potential low-k material. These permittivity values were confirmed at microwave frequencies. Dielectric properties of polyterpenol films were measured by means of split post dielectric resonators (SPDRs) operating at frequencies of 10 GHz and 20 GHz. Permittivity increased for samples deposited at higher RF energy – from 2.65 (25 W) to 2.83 (75 W) measured by a 20-GHz SPDR and from 2.32 (25 W) to 2.53 (100 W) obtained using a 10-GHz SPDR. The error in permittivity measurement was predominantly attributed to the uncertainty in film thickness measurement.

Quick abnormal-bid-detection method for construction contract auctions

Non-competitive bids have recently become a major concern in both Public and Private sector construction contract auctions. Consequently, several models have been developed to help identify bidders potentially involved in collusive practices. However, most of these models require complex calculations and extensive information that is difficult to obtain. The aim of this paper is to utilize recent developments for detecting abnormal bids in capped auctions (auctions with an upper bid limit set by the auctioner) and extend them to the more conventional uncapped auctions (where no such limits are set). To accomplish this, a new method is developed for estimating the values of bid distribution supports by using the solution to what has become known as the German tank problem. The model is then demonstrated and tested on a sample of real construction bid data and shown to detect cover bids with high accuracy. This work contributes to an improved understanding of abnormal bid behavior as an aid to detecting and monitoring potential collusive bid practices.