Prediction model of the buildup of volatile organic compounds on urban roads

A model to predict the buildup of mainly traffic-generated volatile organic compounds or VOCs (toluene, ethylbenzene, ortho-xylene, meta-xylene, and para-xylene) on urban road surfaces is presented. The model required three traffic parameters, namely average daily traffic (ADT), volume to capacity ratio (V/C), and surface texture depth (STD), and two chemical parameters, namely total suspended solid (TSS) and total organic carbon (TOC), as predictor variables. Principal component analysis and two phase factor analysis were performed to characterize the model calibration parameters. Traffic congestion was found to be the underlying cause of traffic-related VOC buildup on urban roads. The model calibration was optimized using orthogonal experimental design. Partial least squares regression was used for model prediction. It was found that a better optimized orthogonal design could be achieved by including the latent factors of the data matrix into the design. The model performed fairly accurately for three different land uses as well as five different particle size fractions. The relative prediction errors were 10–40% for the different size fractions and 28–40% for the different land uses while the coefficients of variation of the predicted intersite VOC concentrations were in the range of 25–45% for the different size fractions. Considering the sizes of the data matrices, these coefficients of variation were within the acceptable interlaboratory range for analytes at ppb concentration levels.

Latent degradation indicators estimation and prediction : a Monte Carlo approach

Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

The potential of critical race theory in decolonizing university curricula

This paper critiques our experiences as non-Indigenous Australian educators of working with numerous embedding Indigenous perspectives curricular projects at an Australian university. Reporting on these project outcomes alone, while useful in identifying limitations, does not illustrate ways in which future embedding and decolonising projects can persist and evolve. Deeper analysis is required of the ways in which Indigenous knowledge and perspectives are perceived, and what ‘embedding’ IK in university curricula truly means to various educational stakeholders. To achieve a deeper analysis and propose ways to invigorate the continuing decolonisation of Australian university curricula, this paper critically interrogates the methodology and conceptualisation of Indigenous knowledge in embedding Indigenous perspectives (EIP) in the university curriculum using tenets of critical race theory. Accordingly, we conduct this analysis from the standpoint that EIP should not subscribe to the luxury of independence of scholarship from politics and activism. The learning objective is to create a space to legitimise politics in the intellectual / academic realm (Dei, 2008, p. 10). We conclude by arguing that critical race theory’s emancipatory, future and action-oriented goals for curricula (Dei, 2008) would enhance effective and sustainable embedding initiatives, and ultimately, preventing such initiatives from returning to the status quo (McLaughlin & Whatman, 2008).

Decolonising curricula in an Australian university : re-imagining curriculum through critical race theory

The recognition of Indigenous knowledge in western academic institutions challenges colonial discourses which have informed and shaped knowledge about Indigenous peoples, cultures and histories. Deeper analysis is required of the ways in which Indigenous knowledge and perspectives are perceived, and the processes through which university curricula can accommodate Indigenous knowledge in teaching and learning. To achieve this deeper analysis, and to invigorate the continuing decolonisation of Australian university curricula, this paper critically interrogates the methodology and conceptualisation of Indigenous knowledge in embedding Indigenous perspectives (EIP) projects in the university curriculum by drawing from tenets of critical race theory and the cultural interface (Nakata, 2007). Accordingly, we conduct this analysis from the standpoint that Indigenous knowledge in university curricula should not subscribe to the luxury of independence of scholarship from politics and activism. The learning objective is to create a space to legitimise politics in the intellectual / academic realm (Dei, 2008, p. 10). We conclude by arguing that critical race theory’s emancipatory, future and action-oriented goals for curricula (Dei, 2008) would enhance effective and sustainable embedding initiatives, and ultimately, preventing such initiatives from returning to the status quo (McLaughlin & Whatman, 2008).

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.