The role of secondary values in the selection process in organising

This study develops a model (i.e., secondary values selection process - 2VS) to describe how values shared by individuals (i.e., secondary values) contribute to the creation of meaning and interpretation in organisations. Elements of the model are identified through exploration of two bodies of literature (a) cultural approaches to organisational studies, and (b) theories of evolution. Incorporated within the model are observable elements that support analysis and evaluation of the 2VS. Outcomes of the study are (a) development of a more complete understanding of the Selection Process in organising and (b) creation of a mechanism for cultural analysis of organisational settings.

Measuring costs of sustainability issues in highway infrastructure : perception of stakeholders in Australia

With increasing pressure to deliver environmentally friendly and socially responsible highway infrastructure projects, stakeholders are also putting significant focus on the early identification of financial viability and outcomes for these projects. Infrastructure development typically requires major capital input, which may cause serious financial constraints for investors. The push for sustainability has added new dimensions to the evaluation of highway projects, particularly on the cost front. Comprehensive analysis of the cost implications of implementing place sustainable measures in highway infrastructure throughout its lifespan is highly desirable and will become an essential part of the highway development process and a primary concern for decision makers. This paper discusses an ongoing research which seeks to identify cost elements and issues related to sustainable measures for highway infrastructure projects. Through life-cycle costing analysis (LCCA), financial implications of pursuing sustainability, which are highly concerned by the construction stakeholders, have been assessed to aid the decision making when contemplating the design, development and operation of highway infrastructure. An extensive literature review and evaluation of project reports from previous Australian highway projects was first conducted to reveal all potential cost elements. This provided the foundation for a questionnaire survey, which helped identify those specific issues and related costs that project stakeholders consider to be most critical in the Australian industry context. Through the survey, three key stakeholders in highway infrastructure development, namely consultants, contractors and government agencies, provided their views on the specific selection and priority ranking of the various categories. Findings of the survey are being integrated into proven LCCA models for further enhancement. A new LCCA model will be developed to assist the stakeholders to evaluate costs and investment decisions and reach optimum balance between financial viability and sustainability deliverables.

Statistical modeling to determine resonant frequency information present in combustion chamber pressure signals

A statistical modeling method to accurately determine combustion chamber resonance is proposed and demonstrated. This method utilises Markov-chain Monte Carlo (MCMC) through the use of the Metropolis-Hastings (MH) algorithm to yield a probability density function for the combustion chamber frequency and find the best estimate of the resonant frequency, along with uncertainty. The accurate determination of combustion chamber resonance is then used to investigate various engine phenomena, with appropriate uncertainty, for a range of engine cycles. It is shown that, when operating on various ethanol/diesel fuel combinations, a 20% substitution yields the least amount of inter-cycle variability, in relation to combustion chamber resonance.

Construction, application and validation of selection evaluation model (SEM) for intelligent HVAC control system

Design teams are confronted with the quandary of choosing apposite building control systems to suit the needs of particular intelligent building projects, due to the availability of innumerable ‘intelligent’ building products and a dearth of inclusive evaluation tools. This paper is organised to develop a model for facilitating the selection evaluation for intelligent HVAC control systems for commercial intelligent buildings. To achieve these objectives, systematic research activities have been conducted to first develop, test and refine the general conceptual model using consecutive surveys; then, to convert the developed conceptual framework into a practical model; and, finally, to evaluate the effectiveness of the model by means of expert validation. The results of the surveys are that ‘total energy use’ is perceived as the top selection criterion, followed by the‘system reliability and stability’, ‘operating and maintenance costs’, and ‘control of indoor humidity and temperature’. This research not only presents a systematic and structured approach to evaluate candidate intelligent HVAC control system against the critical selection criteria (CSC), but it also suggests a benchmark for the selection of one control system candidate against another.

Pressure filtration of Australian bagasse pulp

A one-dimensional pressure filtration model that can be used to predict the behaviour of bagasse pulp has been developed and verified in this study.The dynamic filtration model uses steady state compressibility parameters determined experimentally by uniaxial loading. The compressibility parameters M and N for depithed bagasse pulp were determined to be in the ranges 3000–8000kPa and 2.5–3.0 units, respectively. The model also incorporates experimentally determined steady state permeability data from separate experiments to predict the pulp concentration and fibre pressure throughout a pulp mat during dynamic filtration. Under steady state conditions, a variable Kozeny factor required different values for the permeability parameters when compared to a constant Kozeny factor. The specific surface area was 25–30% lower and the swelling factor was 20–25% higher when a variable Kozeny factor was used. Excellent agreement between experimental data and the dynamic filtration model was achieved when a variable Kozeny factor was used.

Selection-adaption-retention dynamics and variety in organisational routines

The notion of routines as mechanisms for achieving stability and change in organisations is well established in the organisational theory literature (Becker, 2004). However the relationship between the dynamics of selection, adaptation and retention and the increase or decrease in the varieties of routines which are the result of these processes, is not as well established theoretically or empirically. This paper investigates the processes associated with the evolution of an inter-organisational routine over time. The paper contributes to theory by advancing a conceptual clarification between the dynamics of organisational routines which produce variation, and the varieties of routines which are generated as a result of such processes; and an explanation for the relationship between selection, adaptation and retention dynamics and the creation of variety. The research is supported by analysis of empirical data pertaining to the procurement of engineering assets in a large asset intensive organisation.

Inhibitors to optimal project portfolio selection

The selection of projects and programs of work is a key function of both public and private sector organisations. Ideally, projects and programs that are selected to be undertaken are consistent with strategic objectives for the organisation; will provide value for money and return on investment; will be adequately resourced and prioritised; will not compete with general operations for resources and not restrict the ability of operations to provide income to the organisation; will match the capacity and capability of the organisation to deliver; and will produce outputs that are willingly accepted by end users and customers. Unfortunately,this is not always the case. Possible inhibitors to optimal project portfolio selection include: processes that are inconsistent with the needs of the organisation; reluctance to use an approach that may not produce predetermined preferences; loss of control and perceived decision making power; reliance on quantitative methods rather than qualitative methods for justification; ineffective project and program sponsorship; unclear project governance, processes and linkage to business strategies; ignorance, taboos and perceived effectiveness; inadequate education and training about the processes and their importance.

EduBuilding: material selection from life cycle costing sensitivity

Life Cycle Cost Analysis provides a form of synopsis of the initial and consequential costs of building related decisions. These cost figures may be implemented to justify higher investments, for example, in the quality or flexibility of building solutions through a long term cost reduction. The emerging discipline of asset mnagement is a promising approach to this problem, because it can do things that techniques such as balanced scorecards and total quantity cannot. Decisions must be made about operating and maintaining infrastructure assets. An injudicious sensitivity of life cycle costing is that the longer something lasts, the less it costs over time. A life cycle cost analysis will be used as an economic evaluation tool and collaborate with various numbers of analyses. LCCA quantifies incurring costs commonly overlooked (by property and asset managers and designs) as replacement and maintenance costs. The purpose of this research is to examine the Life Cycle Cost Analysis on building floor materials. By implementing the life cycle cost analysis, the true cost of each material will be computed projecting 60 years as the building service life and 5.4% as the inflation rate percentage to classify and appreciate the different among the materials. The analysis results showed the high impact in selecting the floor materials according to the potential of service life cycle cost next.

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.