A framework for analysing system intelligence of the building control system: a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

A study of environmental and management drivers of non-CO2 greenhouse gas emissions in Australian agro-ecosystems

Australian climate, soils and agricultural management practices are significantly different from those of the northern hemisphere nations. Consequently, experimental data on greenhouse gas production from European and North American agricultural soils and its interpretation are unlikely to be directly applicable to Australian systems. A programme of studies of non-CO2 greenhouse gas emissions from agriculture has been established that is designed to reduce uncertainty of non-CO2 greenhouse gas emissions in the Australian National Greenhouse Gas Inventory and provide outputs that will enable better on-farm management practices for reducing non-CO2 greenhouse gas emissions, particularly nitrous oxide. The systems being examined and their locations are irrigated pasture (Kyabram Victoria), irrigated cotton (Narrabri, NSW), irrigated maize (Griffith, NSW), rain-fed wheat (Rutherglen, Victoria) and rain-fed wheat (Cunderdin, WA). The field studies include treatments with and without fertilizer addition, stubble burning versus stubble retention, conventional cultivation versus direct drilling and crop rotation to determine emission factors and treatment possibilities for best management options. The data to date suggest that nitrous oxide emissions from nitrogen fertilizer, applied to irrigated dairy pastures and rain-fed winter wheat, appear much lower than the average of northern hemisphere grain and pasture studies. More variable emissions have been found in studies of irrigated cotton/vetch/wheat rotation and substantially higher emissions from irrigated maize.

The development of a conceptual framework for a district 4-Year Pavement Management Plan, implementation of network-level project screening process

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between preservation needs and available funding. Funding levels are not adequate to meet the preservation needs of the roadway network; therefore projects listed in the 4-Year Pavement Management Plan must be ranked to determine which projects should be funded now and which can be postponed until a later year. Currently, each district uses locally developed methods to prioritize projects. These ranking methods have relied on less formal qualitative assessments based on engineers’ subjective judgment. It is important for TxDOT to have a 4-Year Pavement Management Plan that uses a transparent, rational project ranking process. The objective of this study is to develop a conceptual framework that describes the development of the 4-Year Pavement Management Plan. It can be largely divided into three Steps; 1) Network-Level project screening process, 2) Project-Level project ranking process, and 3) Economic Analysis. A rational pavement management procedure and a project ranking method accepted by districts and the TxDOT administration will maximize efficiency in budget allocations and will potentially help improve pavement condition. As a part of the implementation of the 4-Year Pavement Management Plan, the Network-Level Project Screening (NLPS) tool including the candidate project identification algorithm and the preliminary project ranking matrix was developed. The NLPS has been used by the Austin District Pavement Engineer (DPE) to evaluate PMIS (Pavement Management Information System) data and to prepare a preliminary list of candidate projects for further evaluation.

Employer's management of part-time student labour

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Performance and scalability assessment for non-certificate-based public key management in VANETS

Current research in secure messaging for Vehicular Ad hoc Networks (VANETs) appears to focus on employing a digital certificate-based Public Key Cryptosystem (PKC) to support security. The security overhead of such a scheme, however, creates a transmission delay and introduces a time-consuming verification process to VANET communications. This paper proposes a non-certificate-based public key management for VANETs. A comprehensive evaluation of performance and scalability of the proposed public key management regime is presented, which is compared to a certificate-based PKC by employing a number of quantified analyses and simulations. Not only does this paper demonstrate that the proposal can maintain security, but it also asserts that it can improve overall performance and scalability at a lower cost, compared to the certificate-based PKC. It is believed that the proposed scheme will add a new dimension to the key management and verification services for VANETs.

Research process management

Though the value of a process-centred view for the understanding and (re-)design of corporations has been widely accepted, our understanding of the research process in Information Systems (IS) remains superficial. A process-centred view on IS research considers the conduct of a research project as a sequence of activities involving resources, data and research artifacts. As such, it helps to reflect on more effective ways to conduct IS research, to consolidate and compare diverse practices and to complement the focus on research methodologies with research project practices. This paper takes a first step towards the discipline of ‘Research Process Management’ by exploring the features of research processes and by presenting a preliminary approach for research process design that can facilitate modelling IS research. The case study method and the design science research method are used as examples to demonstrate the potential of such reference research process models.

Diffusion determinants for passive building technologies

This summary is based on an international review of leading peer reviewed journals, in both technical and management fields. It draws on highly cited articles published between 2000 and 2009 to investigate the research question, "What are the diffusion determinants for passive building technologies in Australia?". Using a conceptual framework drawn from the innovation systems literature, this paper synthesises and interprets the literature to map the current state of passive building technologies in Australia and to analyse the drivers for, and obstacles to, their optimal diffusion. The paper concludes that the government has a key role to play through its influence over the specification of building codes.

Regulating coal seam gas in Queensland : lessons in an adaptive environmental management approach?

The current regulatory approach to coal seam gas projects in Queensland is based on the philosophy of adaptive environmental management. This method of “learning by doing” is implemented in Queensland primarily through the imposition of layered monitoring and reporting duties on the coal seam gas operator alongside obligations to compensate and “make good” harm caused. The purpose of this article is to provide a critical review of the Queensland regulatory approach to the approval and minimisation of adverse impacts from coal seam gas activities. Following an overview of the hallmarks of an effective adaptive management approach, this article begins by addressing the mosaic of approval processes and impact assessment regimes that may apply to coal seam gas projects. This includes recent Strategic Cropping Land reforms. This article then turns to consider the preconditions for land access in Queensland and the emerging issues for landholders relating to the negotiation of access and compensation agreements. This article then undertakes a critical review of the environmental duties imposed on coal seam gas operators relating to hydraulic fracturing, well head leaks, groundwater management and the disposal and beneficial use of produced water. Finally, conclusions are drawn regarding the overall effectiveness of the Queensland framework and the lessons that may be drawn from Queensland’s adaptive environmental management approach.

A Bayesian‐Markov process for reliability prediction

Accurate reliability prediction for large-scale, long lived engineering is a crucial foundation for effective asset risk management and optimal maintenance decision making. However, a lack of failure data for assets that fail infrequently, and changing operational conditions over long periods of time, make accurate reliability prediction for such assets very challenging. To address this issue, we present a Bayesian-Marko best approach to reliability prediction using prior knowledge and condition monitoring data. In this approach, the Bayesian theory is used to incorporate prior information about failure probabilities and current information about asset health to make statistical inferences, while Markov chains are used to update and predict the health of assets based on condition monitoring data. The prior information can be supplied by domain experts, extracted from previous comparable cases or derived from basic engineering principles. Our approach differs from existing hybrid Bayesian models which are normally used to update the parameter estimation of a given distribution such as the Weibull-Bayesian distribution or the transition probabilities of a Markov chain. Instead, our new approach can be used to update predictions of failure probabilities when failure data are sparse or nonexistent, as is often the case for large-scale long-lived engineering assets.