A cognitive processing framework for learning analytics

Incorporating a learner’s level of cognitive processing into Learning Analytics presents opportunities for obtaining rich data on the learning process. We propose a framework called COPA that provides a basis for mapping levels of cognitive operation into a learning analytics system. We utilise Bloom’s taxonomy, a theoretically respected conceptualisation of cognitive processing, and apply it in a flexible structure that can be implemented incrementally and with varying degree of complexity within an educational organisation. We outline how the framework is applied, and its key benefits and limitations. Finally, we apply COPA to a University undergraduate unit, and demonstrate its utility in identifying key missing elements in the structure of the course.

Firmicutes dominate the bacterial taxa within sugar-cane processing plants

Sugar cane processing sites are characterised by high sugar/hemicellulose levels, available moisture and warm conditions, and are relatively unexplored unique microbial environments. The PhyloChip microarray was used to investigate bacterial diversity and community composition in three Australian sugar cane processing plants. These ecosystems were highly complex and dominated by four main Phyla, Firmicutes (the most dominant), followed by Proteobacteria, Bacteroidetes, and Chloroflexi. Significant variation (p , 0.05) in community structure occurred between samples collected from ‘floor dump sediment’, ‘cooling tower water’, and ‘bagasse leachate’. Many bacterial Classes contributed to these differences, however most were of low numerical abundance. Separation in community composition was also linked to Classes of Firmicutes, particularly Bacillales, Lactobacillales and Clostridiales, whose dominance is likely to be linked to their physiology as ‘lactic acid bacteria’, capable of fermenting the sugars present. This process may help displace other bacterial taxa, providing a competitive advantage for Firmicutes bacteria.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Processing biases towards gain-framed messages may lead reward sensitive individuals to adopt safer driving behaviours

Using Gray and McNaughton’s revised RST, this study investigated the extent to which the Behavioural Approach System (BAS) and the Fight-Flight-Freeze System (FFFS) influence the processing of gain-framed and loss-framed road safety messages and subsequent message acceptance. It was predicted that stronger BAS sensitivity and FFFS sensitivity would be associated with greater processing and acceptance of the gain-framed messages and loss-framed messages, respectively. Young drivers (N = 80, aged 17–25 years) viewed one of four road safety messages and completed a lexical decision task to assess message processing. Both self-report (e.g., Corr-Cooper RST-PQ) and behavioural measures (i.e., CARROT and Q-Task) were used to assess BAS and FFFS traits. Message acceptance was measured via self-report ratings of message effectiveness, behavioural intentions, attitudes and subsequent driving behaviour. The results are discussed in the context of the effect that differences in reward and punishment sensitivities may have on message processing and message acceptance.

Outdoor flight testing of a pole inspection UAV incorporating high-speed vision

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.

Wind speed perception and risk

Background How accurately do people perceive extreme wind speeds and how does that perception affect the perceived risk? Prior research on human–wind interaction has focused on comfort levels in urban settings or knock-down thresholds. No systematic experimental research has attempted to assess people's ability to estimate extreme wind speeds and perceptions of their associated risks. Method We exposed 76 people to 10, 20, 30, 40, 50, and 60 mph (4.5, 8.9, 13.4, 17.9, 22.3, and 26.8 m/s) winds in randomized orders and asked them to estimate wind speed and the corresponding risk they felt. Results Multilevel modeling showed that people were accurate at lower wind speeds but overestimated wind speeds at higher levels. Wind speed perceptions mediated the direct relationship between actual wind speeds and perceptions of risk (i.e., the greater the perceived wind speed, the greater the perceived risk). The number of tropical cyclones people had experienced moderated the strength of the actual–perceived wind speed relationship; consequently, mediation was stronger for people who had experienced fewer storms. Conclusion These findings provide a clearer understanding of wind and risk perception, which can aid development of public policy solutions toward communicating the severity and risks associated with natural disasters.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Time- vs. frequency-domain identification of parametric radiation force models for marine structures at zero speed

The dynamics describing the motion response of a marine structure in waves can be represented within a linear framework by the Cummins Equation. This equation contains a convolution term that represents the component of the radiation forces associated with fluid memory effects. Several methods have been proposed in the literature for the identification of parametric models to approximate and replace this convolution term. This replacement can facilitate the model implementation in simulators and the analysis of motion control designs. Some of the reported identification methods consider the problem in the time domain while other methods consider the problem in the frequency domain. This paper compares the application of these identification methods. The comparison is based not only on the quality of the estimated models, but also on the ease of implementation, ease of use, and the flexibility of the identification method to incorporate prior information related to the model being identified. To illustrate the main points arising from the comparison, a particular example based on the coupled vertical motion of a modern containership vessel is presented.

Robust speed tracking control of synchronous motors using immersion and invariance

This paper presents a novel control strategy for velocity tracking of Permanent Magnet Synchronous Machines (PMSM). The model of the machine is considered within the port-Hamiltonian framework and a control is designed using concepts of immersion and invariance (I&I) recently developed in the literature. The proposed controller ensures internal stability and output regulation, and it forces integral action on non-passive outputs.

Developmentally and transgene regulated nuclear processing of primary transcripts of chalcone synthase A in petunia

The introduction of chalcone synthase A transgenes into petunia plants can result in degradation of chalcone synthase A RNAs and loss of chalcone synthase, a process called cosuppression or post-transcriptional gene silencing. Here we show that the RNA degradation is associated with changes in premRNA processing, i.e. loss of tissue specificity in transcript cleavage patterns, accumulation of unspliced molecules, and use of template-specific secondary poly(A) sites. These changes can also be observed at a lower level in leaves but not flowers of nontransgenic petunias. Based on this, a model is presented of how transgenes may disturb the carefully evolved, developmentally controlled post-transcriptional regulation of chalcone synthase gene expression by influencing the survival rate of the endogenous and their own mRNA.

Modeling and analysis of a novel variable-speed cage induction generator

This paper introduces a novel cage induction generator and presents a mathematical model, through which its behavior can be accurately predicted. The proposed generator system employs a three-phase cage induction machine and generates single-phase and constant-frequency electricity at varying rotor speeds without an intermediate inverter stage. The technique uses any one of the three stator phases of the machine as the excitation winding and the remaining two phases, which are connected in series, as the power winding. The two-series-connected-and-one-isolated (TSCAOI) phase winding configuration magnetically decouples the two sets of windings, enabling independent control. Electricity is generated through the power winding at both sub- and super-synchronous speeds with appropriate excitation to the isolated single winding at any frequency of generation. A dynamic mathematical model, which accurately predicts the behavior of the proposed generator, is also presented and implemented in MATLAB/Simulink. Experimental results of a 2-kW prototype generator under various operating conditions are presented, together with theoretical results, to demonstrate the viability of the TSCAOI power generation. The proposed generator is simple and capable of both storage and retrieval of energy through its excitation winding and is expected to be suitable for applications, such as small wind turbines and microhydro systems.

Calcium influx inhibits MT1-MMP processing and blocks MMP-2 activation

We have previously reported that concanavalin A (ConA)-induced MMP-2 activation involves both transcriptional and non-transcriptional mechanisms. Here we examined the effects of calcium influx on MT1-MMP expression and MMP-2 activation in MDA-MB-231 cells. The calcium ionophore ionomycin caused a dose-dependent inhibition of ConA-induced MMP-2 activation, but had no effect on MT1-MMP mRNA levels. However, Western analysis revealed an accumulation of pro-MT1-MMP (63 kDa), indicating that ionomycin blocked the conversion of pro-MT1-MMP protein to the active 60 kDa form. This suggests that increased calcium levels inhibit the processing of MT1-MMP. This finding may help to elucidate the mechanism(s) which regulates MT1-MMP activation.

An application of an extended theory of planned behaviour to understand drivers' compliance with the school zones speed limit in Australia and Malaysia

This research investigated individual and situational factors that influence driver compliance with the school zones speed limit in Australia and Malaysia. Despite all the countermeasures implemented in these two countries, driver compliance with the speed limit in school zones is still poor. The findings of the research provided support for the application of the Theory of Planned Behaviour to understand and therefore influence drivers' intention to comply with the school zones speed limit in both Australia and Malaysia. The research also revealed that mindfulness, a construct rarely used in road safety before, directly influenced Australian drivers' intention to comply, while habit influenced intention to comply in Malaysia. The research raised important theoretical and cross-cultural issues for future research and highlighted the need to increase the visibility of school zones with the use of more noticeable traffic control devices.

The impacts of variable speed limit on speed variation and headway distribution

This thesis investigates the impacts of variable speed limit on motorway speed variation and headway distribution. Initiative techniques of traffic flow categorisation study contribute in analysing the effects of variable speed limit on various traffic states. The project focuses on the speed harmonisation impacts within and across lanes as well as the uniformity of headway spread in the application of variable speed limit.