Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

No sex difference in body fat in response to supervised and measured exercise

It is often reported that females lose less body weight than males do in response to exercise. These differences are suggested to be a result of females exhibiting a stronger defense of body fat and a greater compensatory appetite response to exercise than males do. Purpose This study aimed to compare the effect of a 12-wk supervised exercise program on body weight, body composition, appetite, and energy intake in males and females. Methods A total of 107 overweight and obese adults (males = 35, premenopausal females = 72, BMI = 31.4 ± 4.2 kg·m−2, age = 40.9 ± 9.2 yr) completed a supervised 12-wk exercise program expending approximately 10.5 MJ·wk−1 at 70% HRmax. Body composition, energy intake, appetite ratings, RMR, and cardiovascular fitness were measured at weeks 0 and 12. Results The 12-wk exercise program led to significant reductions in body mass (males [M] = −3.03 ± 3.4 kg and females [F] = −2.28 ± 3.1 kg), fat mass (M = −3.14 ± 3.7 kg and F = −3.01 ± 3.0 kg), and percent body fat (M = −2.45% ± 3.3% and F = −2.45% ± 2.2%; all P < 0.0001), but there were no sex-based differences (P > 0.05). There were no significant changes in daily energy intake in males or females after the exercise intervention compared with baseline (M = 199.2 ± 2418.1 kJ and F = −131.6 ± 1912.0 kJ, P > 0.05). Fasting hunger levels significantly increased after the intervention compared with baseline values (M = 11.0 ± 21.1 min and F = 14.0 ± 22.9 mm, P < 0.0001), but there were no differences between males and females (P > 0.05). The exercise also improved satiety responses to an individualized fixed-energy breakfast (P < 0.0001). This was comparable in males and females. Conclusions Males and premenopausal females did not differ in their response to a 12-wk exercise intervention and achieved similar reductions in body fat. When exercise interventions are supervised and energy expenditure is controlled, there are no sex-based differences in the measured compensatory response to exercise.

The adaptive metabolic response to exercise-induced weight loss influences both energy expenditure and energy intake

BACKGROUND/OBJECTIVEs A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS Thirty overweight and obese women (body mass index (BMI)=30.6±3.6 kg/m2) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention (MApost), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI=31.0±3.9 kg/m2). RESULTS Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (−102.9±77.5 kcal per day). MApost was associated with the change in leptin (r=0.47; P=0.04), and the change in resting fat (r=0.52; P=0.01) and carbohydrate oxidation (r=−0.44; P=0.02). Furthermore, MApost was also associated with the change in EI following EX (r=−0.44; P=0.01). CONCLUSIONS Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Facial expression recognition experiments with data from television broadcasts and the world wide web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.

Total heat transfer measurements on a flight investigation of reentry environment model

Stagnation-point total heat transfer was measured on a 1:27.7 model of the Flight Investigation of Reentry Environment II flight vehicle. Experiments were performed in the X1 expansion tube at an equivalent flight velocity and static enthalpy of 11 km/s and 12.7 MJ/kg, respectively. Conditions were chosen to replicate the flight condition at a total flight time of 1639.5 s, where radiation contributed an estimated 17-36% of the total heat transfer. This contribution is theorized to reduce to <2% in the scaled experiments, and the heating environment on the test model was expected to be dominated by convection. A correlation between reported flight heating rates and expected experimental heating, referred to as the reduced flight value, was developed to predict the level of heating expected on the test model. At the given flow conditions, the reduced flight value was calculated to be 150 MW/m2. Average stagnation-point total heat transfer was measured to be 140 ± 7% W/m2, showing good agreement with the predicted value. Experimentally measured heat transfer was found to have good agreement of between 5 and 15% with a number of convective heating correlations, confirming that convection dominates the tunnel heating environment, and that useful experimental measurements could be made in weakly coupled radiating flow

A randomized controlled trial of a correspondence-based intervention for carers of relatives with psychosis

Background Family members play a crucial role in supporting the recovery of loved ones with psychosis. The journey of recovery is not only traversed by the person experiencing the mental illness but also by their family. Interventions to support these families have traditionally either focused on psychoeducation or addressed problematic interactions or expressed emotion. Family programmes have far less frequently emphasized supporting family members' adjustment to the challenges posed by their relative's disorder or their recovery from associated distress. The study compared a control condition that received only a psychoeducational booklet (Information) and a condition also receiving a correspondence-based interactive recovery-oriented intervention (Connections). The Connections group was expected to show greater improvements in recovery knowledge, well-being, experiences of caregiving, hopefulness and distress. Method A randomized controlled trial was conducted to evaluate the effectiveness of two correspondence-based family interventions delivered to 81 carers of relatives with psychosis. Results Intent-to-treat analyses showed no differential outcomes between conditions, but an analysis of participants who substantially completed their allocated treatment showed that carers receiving Connections had significantly more improvements in well-being, positive experiences of caregiving and distress. Conclusions Correspondence interventions that support carer's recovery may result in more positive mental health for those who complete key elements of the programme compared with information alone. However, many carers do not complete a correspondence programme and this may limit its impact.

Modelling for the electricity distribution network

Global awareness for cleaner and renewable energy is transforming the electricity sector at many levels. New technologies are being increasingly integrated into the electricity grid at high, medium and low voltage levels, new taxes on carbon emissions are being introduced and individuals can now produce electricity, mainly through rooftop photovoltaic (PV) systems. While leading to improvements, these changes also introduce challenges, and a question that often rises is ‘how can we manage this constantly evolving grid?’ The Queensland Government and Ergon Energy, one of the two Queensland distribution companies, have partnered with some Australian and German universities on a project to answer this question in a holistic manner. The project investigates the impact the integration of renewables and other new technologies has on the physical structure of the grid, and how this evolving system can be managed in a sustainable and economical manner. To aid understanding of what the future might bring, a software platform has been developed that integrates two modelling techniques: agent-based modelling (ABM) to capture the characteristics of the different system units accurately and dynamically, and particle swarm optimization (PSO) to find the most economical mix of network extension and integration of distributed generation over long periods of time. Using data from Ergon Energy, two types of networks (3 phase, and Single Wired Earth Return or SWER) have been modelled; three-phase networks are usually used in dense networks such as urban areas, while SWER networks are widely used in rural Queensland. Simulations can be performed on these networks to identify the required upgrades, following a three-step process: a) what is already in place and how it performs under current and future loads, b) what can be done to manage it and plan the future grid and c) how these upgrades/new installations will perform over time. The number of small-scale distributed generators, e.g. PV and battery, is now sufficient (and expected to increase) to impact the operation of the grid, which in turn needs to be considered by the distribution network manager when planning for upgrades and/or installations to stay within regulatory limits. Different scenarios can be simulated, with different levels of distributed generation, in-place as well as expected, so that a large number of options can be assessed (Step a). Once the location, sizing and timing of assets upgrade and/or installation are found using optimisation techniques (Step b), it is possible to assess the adequacy of their daily performance using agent-based modelling (Step c). One distinguishing feature of this software is that it is possible to analyse a whole area at once, while still having a tailored solution for each of the sub-areas. To illustrate this, using the impact of battery and PV can have on the two types of networks mentioned above, three design conditions can be identified (amongst others): · Urban conditions o Feeders that have a low take-up of solar generators, may benefit from adding solar panels o Feeders that need voltage support at specific times, may be assisted by installing batteries · Rural conditions - SWER network o Feeders that need voltage support as well as peak lopping may benefit from both battery and solar panel installations. This small example demonstrates that no single solution can be applied across all three areas, and there is a need to be selective in which one is applied to each branch of the network. This is currently the function of the engineer who can define various scenarios against a configuration, test them and iterate towards an appropriate solution. Future work will focus on increasing the level of automation in identifying areas where particular solutions are applicable.

Estimating benefits of energy storage for aggregate storage applications in electricity distribution networks in Queensland

Electric Energy Storage (EES) is considered as one of the promising options for reducing the need for costly upgrades in distribution networks in Queensland (QLD). However, It is expected, the full potential for storage for distribution upgrade deferral cannot be fully realized due to high cost of EES. On the other hand, EES used for distribution deferral application can support a variety of complementary storage applications such as energy price arbitrage, time of use (TOU) energy cost reduction, wholesale electricity market ancillary services, and transmission upgrade deferral. Aggregation of benefits of these complementary storage applications would have the potential for increasing the amount of EES that may be financially attractive to defer distribution network augmentation in QLD. In this context, this paper analyzes distribution upgrade deferral, energy price arbitrage, TOU energy cost reduction, and integrated solar PV-storage benefits of EES devices in QLD.

PTSD and traumatic stress : from gene to community and bench to bedside

Individuals and communities are exposed to traumatic events, those that are accidents or naturally occurring and those that are intentional or human made. Although resilience is the expected response, for some, posttraumatic stress disorder may be the outcome. Brain models of PTSD require understanding the phenomenology of the disorder and the brain “break down” that occurs. Among several models, importantly, is the perspective that PTSD is a “forgetting” disorder. Other elements in the onset and triggers of PTSD can identify further models to examine at the bench. New studies of the 5-HT2A receptor, the glucocorticoid receptor, p11, mitochondrial genes and cannabinoids are bringing new perspectives to understanding brain function in PTSD. Effective treatments indicate areas for bench research on the mechanisms of the disorder.

Maternal report of young children's eating styles. Validation of the Children's Eating Behaviour Questionnaire in three ethnically diverse Australian samples

The aim of this study was to validate the Children’s Eating Behaviour Questionnaire (CEBQ) in three ethnically and culturally diverse samples of mothers in Australia. Confirmatory factor analysis utilising structural equation modelling examined whether the established 8-factor model of the CEBQ was supported in our three populations: (i) a community sample of first-time mothers allocated to the control group of the NOURISH trial (mean child age = 24 months [SD = 1]; N = 244); (ii) a sample of immigrant Indian mothers of children aged 1–5 years (mean age = 34 months [SD = 14]; N = 203), and (iii) a sample of immigrant Chinese mothers of children aged 1–4 years (mean age = 36 months [SD = 14]; N = 216). The original 8-factor model provided an acceptable fit to the data in the NOURISH sample with minor post hoc re-specifications (two error covariances on Satiety Responsiveness and an item-factor covariance to account for a cross-loading of an item (Fussiness) on Satiety Responsiveness). The re-specified model showed reasonable fit in both the Indian and Chinese samples. Cronbach’s α estimates ranged from .73 to .91 in the Australian sample and .61–.88 in the immigrant samples. This study supports the appropriateness of the CEBQ in the multicultural Australian context.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Performance of buried tunnels subjected to surface blast incorporating fluid structure interaction

This paper uses finite element techniques to investigate the performance of buried tunnels subjected to surface blasts incorporating fully coupled Fluid Structure Interaction and appropriate material models which simulate strain rate effects. Modelling techniques are first validated against existing experimental results and then used to treat the blast induced shock wave propagation and tunnel response in dry and saturated sands. Results show that the tunnel buried in saturated sand responds earlier than that in dry sand. Tunnel deformations decrease with distance from explosive in both sands, as expected. In the vicinity of the explosive, the tunnel buried in saturated sand suffered permanent deformation in both axial and circumferential directions, whereas the tunnel buried in dry sand recovered from most of the axial deformation. Overall, response of the tunnel in saturated sand is more severe for a given blast event and shows the detrimental effect of pore water on the blast response of buried tunnels. The validated modelling techniques developed in this paper can be used to investigate the blast response of tunnels buried in dry and saturated sands.

Food security in Australia in an era of neoliberalism, productivism and climate change

For over 150 years Australia has exported bulk, undifferentiated, commodities such as wool, wheat, meat and sugar to the UK and more recently to Japan, Korea, and the Middle East. It is estimated that, each year, Australia's farming system feeds a domestic population of some 22 million people, while exporting enough food to feed another 40 million. With the Australian population expected to double in the next 40 years, and with the anticipated growth in the world's population to reach a level of some 9 billion (from its present level of 7 billion) in the same period, there are strong incentives for an expansion of food production in Australia. Neoliberal settings are encouraging this expansion at the same time as they are facilitating importation of foods, higher levels of foreign direct investment and the commoditisation of resources (such as water). Yet, expansion in food production – and in an era of climate change – will continue to compromise the environment. After discussing Australia's neoliberal framework and its relation to farming, this paper outlines how Australia is attempting to address the issue of food security. It argues that productivist farming approaches that are favoured by both industry and government are proving incapable of bringing about long-term production outcomes that will guarantee national food security.

Experiences of resettled refugees during the 2011 Queensland floods

With projected climatic changes it is expected that refugees and other forced migrants will increasingly spend protracted amounts of time in transit countries or will resettle in locations that experience ecological vulnerability. A submission to the Queensland Floods Commission Inquiry 2011 by MDA reported that the floods displaced about 70 refugee client families and that 30 families had ongoing complex needs at the time of the submission. The findings reported in this chapter are derived from a follow-up of a cohort of men from refugee backgrounds who participated in the 2008–10 SettleMEN project. The chapter provides an insight into the experiences of refugee migrants who experience environmental disaster in a site of settlement