Applying a social justice framework to ensure good practice in monitoring student learning engagement

A current Australian Learning and Teaching Council (ALTC) funded action research project aims to provide a set of practical resources founded on a social justice framework, to guide good practice for monitoring student learning engagement (MSLE) in higher education. The project involves ten Australasian institutions, eight of which are engaged in various MSLE type projects. A draft framework, consisting of six social justice principles which emerged from the literature has been examined with reference to the eight institutional approaches for MSLE in conjunction with the personnel working on these initiatives during the first action research cycle. The cycle will examine the strategic and operational implications of the framework in each of the participating institutions. Cycle 2 will also build capacity to embed the principles within the institutional MSLE program and will identify and collect examples and resources that exemplify the principles in practice. The final cycle will seek to pilot the framework to guide new MSLE initiatives. In its entirety, the project will deliver significant resources to the sector in the form of a social justice framework for MSLE, guidelines and sector exemplars for MSLE. As well as increasing the awareness amongst staff around the criticality of transition to university (thereby preventing attrition) and the significance of the learning and teaching agenda in enhancing student engagement, the project will build leadership capacity within the participating institutions and provide a knowledge base and institutional capacity for the Australasian HE sector to deploy the deliverables that will safeguard student learning engagement At this early stage of the project the workshop session provides an opportunity to discuss and examine the draft set of social justice principles and to discuss their potential value for the participants’ institutional contexts. Specifically, the workshop will explore critical questions associated with the principles.

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor-intensive, time consuming and expensive. Remote sensing technologies represent an attractive and cost-effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection, and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments, we call this approach PTAGS. Improved object recognition is achieved by fusing information from multi-sensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches.

Adherence, adaptation and acceptance of elderly chronic heart failure patients to receiving healthcare via telephone-monitoring

Background: Although the potential to reduce hospitalisation and mortality in chronic heart failure (CHF) is well reported, the feasibility of receiving healthcare by structured telephone support or telemonitoring is not. Aims: To determine; adherence, adaptation and acceptability to a national nurse-coordinated telephone-monitoring CHF management strategy. The Chronic Heart Failure Assistance by Telephone Study (CHAT). Methods: Triangulation of descriptive statistics, feedback surveys and qualitative analysis of clinical notes. Cohort comprised of standard care plus intervention (SC + I) participants who completed the first year of the study. Results: 30 GPs (70% rural) randomised to SC + I recruited 79 eligible participants, of whom 60 (76%) completed the full 12 month follow-up period. During this time 3619 calls were made into the CHAT system (mean 45.81 SD ± 79.26, range 0-369), Overall there was an adherence to the study protocol of 65.8% (95% CI 0.54-0.75; p = 0.001) however, of the 60 participants who completed the 12 month follow-up period the adherence was significantly higher at 92.3% (95% CI 0.82-0.97, p ≤ 0.001). Only 3% of this elderly group (mean age 74.7 ±9.3 years) were unable to learn or competently use the technology. Participants rated CHAT with a total acceptability rate of 76.45%. Conclusion: This study shows that elderly CHF patients can adapt quickly, find telephone-monitoring an acceptable part of their healthcare routine, and are able to maintain good adherence for a least 12 months. © 2007.

Monitoring identity of past, current and future : a performance management system perspective

This paper seeks to explore how organisations can effectively use performance management systems (PMS) to monitor collective identities. The monitoring of relationships between identity and an influential PMS—the balanced scorecard (BSC)—are explored. Drawing from identity and management accounting literature, this paper argues that identity products, patternings and processes are commonly positioned, monitored and interpreted through the multiple perspectives and levels of the BSC. Specifically, human, technical and organisational capital under the Learning and Growth perspective of the BSC can incorporate various identity measures that sustain the relative, distinctive and fluid nature of identities. The value of this research is to strengthen the theoretical grounds which position identity as an important dimension of organisational capital in PMS.

Acoustic component detection for automatic species recognition in environmental monitoring

Automatic species recognition plays an important role in assisting ecologists to monitor the environment. One critical issue in this research area is that software developers need prior knowledge of specific targets people are interested in to build templates for these targets. This paper proposes a novel approach for automatic species recognition based on generic knowledge about acoustic events to detect species. Acoustic component detection is the most critical and fundamental part of this proposed approach. This paper gives clear definitions of acoustic components and presents three clustering algorithms for detecting four acoustic components in sound recordings; whistles, clicks, slurs, and blocks. The experiment result demonstrates that these acoustic component recognisers have achieved high precision and recall rate.

Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

The value of smart metering household electricity consumption

Smart metering presents opportunities for business model creation. However the viability of many potential business models in a smart metering scenario may be dictated by privacy regulation and data sharing arrangements. An understanding by businesses of customers’ preferences for the visualisation of their electricity consumption and the degree to which they are willing to share it, is valuable. We present results from two interviews exploring data visualisation and willingness to share personal electricity consumption information. Participants displayed a high willingness to share and a preference for access to additional information when visualising their electricity consumption.

Acoustic emission for diesel engine monitoring: A review and preliminary

Vibration analysis has been a prime tool in condition monitoring of rotating machines, however, its application to internal combustion engines remains a challenge because engine vibration signatures are highly non-stationary that are not suitable for popular spectrum-based analysis. Signal-to-noise ratio is a main concern in engine signature analysis due to severe background noise being generated by consecutive mechanical events, such as combustion, valve opening and closing, especially in multi-cylinder engines. Acoustic Emission (AE) has been found to give excellent signal-to-noise ratio allowing discrimination of fine detail of normal or abnormal events during a given cycle. AE has been used to detect faults, such as exhaust valve leakage, fuel injection behaviour, and aspects of the combustion process. This paper presents a review of AE application to diesel engine monitoring and preliminary investigation of AE signature measured on an 18-cylinder diesel engine. AE is compared with vibration acceleration for varying operating conditions: load and speed. Frequency characteristics of AE from those events are analysed in time-frequency domain via short time Fourier trasform. The result shows a great potential of AE analysis for detection of various defects in diesel engines.

A study on the use of acoustic emission technique as a structural health monitoring tool

As civil infrastructures such as bridges age, there is a concern for safety and a need for cost-effective and reliable monitoring tool. Different diagnostic techniques are available nowadays for structural health monitoring (SHM) of bridges. Acoustic emission is one such technique with potential of predicting failure. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). AEtechnique involves recording the stress waves bymeans of sensors and subsequent analysis of the recorded signals,which then convey information about the nature of the source. AE can be used as a local SHM technique to monitor specific regions with visible presence of cracks or crack prone areas such as welded regions and joints with bolted connection or as a global technique to monitor the whole structure. Strength of AE technique lies in its ability to detect active crack activity, thus helping in prioritising maintenance work by helping focus on active cracks rather than dormant cracks. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. One is the generation of large amount of data during the testing; hence an effective data analysis and management is necessary, especially for long term monitoring uses. Complications also arise as a number of spurious sources can giveAEsignals, therefore, different source discrimination strategies are necessary to identify genuine signals from spurious ones. Another major challenge is the quantification of damage level by appropriate analysis of data. Intensity analysis using severity and historic indices as well as b-value analysis are some important methods and will be discussed and applied for analysis of laboratory experimental data in this paper.

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Functional and advanced functional and causal relationship diagrams for remote condition monitoring

Analysing the condition of an asset is a big challenge as there can be many aspects which can contribute to the overall functional reliability of the asset that have to be considered. In this paper we propose a two-step functional and causal relationship diagram (FCRD) to address this problem. In the first step, the FCRD is designed to facilitate the analysis of the condition of an asset by evaluating the interdependence (functional and causal) relationships between different components of the asset with the help of a relationship diagram. This is followed by the advanced FCRD (AFCRD) which refines the information from the FCRD into a comprehensive and manageable format. This new two-step methodology for asset condition monitoring is tested and validated for the case of a water treatment plant. © IMechE 2012.

Damage quantification techniques in acoustic emission monitoring

Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing.

Using fiber Bragg grating in geothermal monitoring

Earthquake precursor monitoring is the foundation of earthquake prediction and geothermal monitoring is one of the basic methods of earthquake precursor monitoring. High temperature well contains more information and therefore its monitoring is more important. However, electric sensors are hard to meet the monitoring requirements of high sensitivity and long lifetime. For a better observation of the earthquake precursor, a high sensitive fiber Bragg grating (FBG) temperature sensor is designed to monitoring a well at 87.5±1◦C. The performance of the FBG sensor demonstrates that it’s quite possible that applying FBG to high-sensitivity temperature-monitoring fields, such as geothermal monitoring. As far as we known, it is the first time that trying a high sensitive FBG temperature sensor in a practical application, let alone in the field of geothermal monitoring.

Extending persistent monitoring by combining ocean models and Markov decision processes

Ocean processes are complex and have high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain a synoptic view of ocean processes and resolve their spatiotemporal variability. One way to perform these persistent observations is to utilise an autonomous vehicle that can remain on deployment for long time periods. However, such vehicles are generally underactuated and slow moving. A challenge for persistent monitoring with these vehicles is dealing with currents while executing a prescribed path or mission. Here we present a path planning method for persistent monitoring that exploits ocean currents to increase navigational accuracy and reduce energy consumption.