A plasmonic 'ac Wheatstone bridge' circuit for high-sensitivity phase measurement and single-molecule detection

In this paper, a plasmonic “ac Wheatstone bridge” circuit is proposed and theoretically modeled for the first time. The bridge circuit consists of three metallic nanoparticles, shaped as rectangular prisms, with two nanoparticles acting as parallel arms of a resonant circuit and the third bridging nanoparticle acting as an optical antenna providing an output signal. Polarized light excites localized surface plasmon resonances in the two arms of the circuit, which generate an optical signal dependent on the phase-sensitive excitations of surface plasmons in the antenna. The circuit is analyzed using a plasmonic coupling theory and numerical simulations. The analyses show that the plasmonic circuit is sensitive to phase shifts between the arms of the bridge and has the potential to detect the presence of single molecules.

Proactive detection of high collision concentration locations on highways

In previous research (Chung et al., 2009), the potential of the continuous risk profile (CRP) to proactively detect the systematic deterioration of freeway safety levels was presented. In this paper, this potential is investigated further, and an algorithm is proposed for proactively detecting sites where the collision rate is not sufficiently high to be classified as a high collision concentration location but where a systematic deterioration of safety level is observed. The approach proposed compares the weighted CRP across different years and uses the cumulative sum (CUSUM) algorithm to detect the sites where changes in collision rate are observed. The CRPs of the detected sites are then compared for reproducibility. When high reproducibility is observed, a growth factor is used for sequential hypothesis testing to determine if the collision profiles are increasing over time. Findings from applying the proposed method using empirical data are documented in the paper together with a detailed description of the method.

Factors influencing health promotion activities in midlife and older Australian women with a chronic disease

Background: Chronic diseases including type 2 diabetes are a leading cause of morbidity and mortality in midlife and older Australian women. There are a number of modifiable risk factors for type 2 diabetes and other chronic diseases including smoking, nutrition, physical activity and overweight and obesity. Little research has been conducted in the Australian context to explore the perceived barriers to health promotion activities in midlife and older Australian women with a chronic disease. Aims: The primary aim of this study was to explore women’s perceived barriers to health promotion activities to reduce modifiable risk factors, and the relationship of perceived barriers to smoking behaviour, fruit and vegetable intake, physical activity and body mass index. A secondary aim of this study was to investigate nurses’ perceptions of the barriers to action for women with a chronic disease, and to compare those perceptions with those of the women. Methods: The study was divided into two phases where Phase 1 was a cross sectional survey of women, aged over 45 years with type 2 diabetes who were attending Diabetes clinics in the Primary and Community Health Service of the Metro North Health Service District of Queensland Health (N = 22). The women were a subsample of women participating in a multi-model lifestyle intervention, the ‘Reducing Chronic Disease among Adult Australian Women’ project. Phase 2 of the study was a cross sectional online survey of nurses working in Primary and Community Health Service in the Metro North Health Service District of Queensland Health (N = 46). Pender’s health promotion model was used as the theoretical framework for this study. Results: Women in this study had an average total barriers score of 32.18 (SD = 9.52) which was similar to average scores reported in the literature for women with a range of physical disabilities and illnesses. The leading five barriers for this group of women were: concern about safety; too tired; not interested; lack of information about what to do; with lack of time and feeling I can’t do things correctly the equal fifth ranked barriers. In this study there was no statistically significant difference in average total barriers scores between women in the intervention group and those is the usual care group of the parent study. There was also no significant relationship between the women’s socio-demographic variables and lifestyle risk factors and their level of perceived barriers. Nurses in the study had an average total barriers score of 44.48 (SD = 6.24) which was higher than all other average scores reported in the literature. The leading five barriers that nurses perceived were an issue for women with a chronic disease were: lack of time and interferes with other responsibilities the leading barriers; embarrassment about appearance; lack of money; too tired and lack of support from family and friends. There was no significant relationship between the nurses’ sociodemographic and nursing variables and the level of perceived barriers. When comparing the results of women and nurses in the study there was a statistically significant difference in the median total barriers score between the groups (p < 0.001), where the nurses perceived the barriers to be higher (Md = 43) than the women (Md = 33). There was also a significant difference in the responses to the individual barriers items in fifteen of the eighteen items (p < 0.002). Conclusion: Although this study is limited by a small sample size, it contributes to understanding the perception of midlife and older women with a chronic disease and also the perception of nurses, about the barriers to healthy lifestyle activities that women face. The study provides some evidence that the perceptions of women and nurses may differ and argues that these differences may have significant implications for clinical practice. The study recommends a greater emphasis on assessing and managing perceived barriers to health promotion activities in health education and policy development and proposes a conceptual model for understanding perceived barriers to action.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

CAT Detect (Computer Activity Timeline Detection) : a tool for detecting inconsistency in computer activity timelines

The construction of timelines of computer activity is a part of many digital investigations. These timelines of events are composed of traces of historical activity drawn from system logs and potentially from evidence of events found in the computer file system. A potential problem with the use of such information is that some of it may be inconsistent and contradictory thus compromising its value. This work introduces a software tool (CAT Detect) for the detection of inconsistency within timelines of computer activity. We examine the impact of deliberate tampering through experiments conducted with our prototype software tool. Based on the results of these experiments, we discuss techniques which can be employed to deal with such temporal inconsistencies.

Prevalence of malnutrition in Parkinson's disease : a systematic review

Parkinson's disease (PD) patients may be at higher risk of malnutrition because of the symptoms associated with the disease and the side effects of the medication used to manage it. A decline in nutritional status is associated with many adverse outcomes related to health and quality of life. It is not clear, however, to what extent this population is currently affected by malnutrition. The objective of this review was to systematically assess the methodology and outcomes of studies reporting the prevalence of malnutrition in PD patients. Studies that attempted to classify participants with PD into nutritional risk and/or malnutrition categories using body mass index, weight change, anthropometric measures, and nutritional screening and assessment scores were included. The prevalence of malnutrition ranged from 0% to 24% in PD patients, while 3–60% of PD patients were reported to be at risk of malnutrition. There was a large degree of variation among studies in the methods chosen, the definition of malnutrition using those methods, and the detail in which the methodological protocols were reported. The true extent of malnutrition in the PD population has yet to be accurately quantified. It is important, however, to screen for malnutrition at the time of PD diagnosis.

Exploring health behaviour determinants of ageing Australians with chronic diseases

Background: Chronic disease presents overwhelming challenges to elderly patients, their families, health care providers and the health care system. The aim of this study was to explore a theoretical model for effective management of chronic diseases, especially type 2 diabetes mellitus and/or cardiovascular disease. The assumed theoretical model considered the connections between physical function, mental health, social support and health behaviours. The study effort was to improve the quality of life for people with chronic diseases, especially type 2 diabetes and/or cardiovascular disease and to reduce health costs. Methods: A cross-sectional post questionnaire survey was conducted in early 2009 from a randomised sample of Australians aged 50 to 80 years. A total of 732 subjects were eligible for analysis. Firstly, factors influencing respondents‘ quality of life were investigated through bivariate and multivariate regression analysis. Secondly, the Theory of Planned Behaviour (TPB) model for regular physical activity, healthy eating and medication adherence behaviours was tested for all relevant respondents using regression analysis. Thirdly, TPB variable differences between respondents who have diabetes and/or cardiovascular disease and those without these diseases were compared. Finally, the TPB model for three behaviours including regular physical activity, healthy eating and medication adherence were tested in respondents with diabetes and/or cardiovascular diseases using Structure Equation Modelling (SEM). Results: This was the first study combining the three behaviours using a TPB model, while testing the influence of extra variables on the TPB model in one study. The results of this study provided evidence that the ageing process was a cumulative effect of biological change, socio-economic environment and lifelong behaviours. Health behaviours, especially physical activity and healthy eating were important modifiable factors influencing respondents‘ quality of life. Since over 80% of the respondents had at least one chronic disease, it was important to consider supporting older people‘s chronic disease self-management skills such as healthy diet, regular physical activity and medication adherence to improve their quality of life. Direct measurement of the TPB model was helpful in understanding respondents‘ intention and behaviour toward physical activity, healthy eating and medication adherence. In respondents with diabetes and/or cardiovascular disease, the TPB model predicted different proportions of intention toward three different health behaviours with 39% intending to engage in physical activity, 49% intending to engage in healthy eating and 47% intending to comply with medication adherence. Perceived behavioural control, which was proven to be the same as self-efficacy in measurement in this study, played an important role in predicting intention towards the three health behaviours. Also social norms played a slightly more important role than attitude for physical activity and medication adherence, while attitude and social norms had similar effects on healthy eating in respondents with diabetes and/or cardiovascular disease. Both perceived behavioural control and intention directly predicted recent actual behaviours. Physical activity was more a volitional control behaviour than healthy eating and medication adherence. Step by step goal setting and motivation was more important for physical activity, while accessibility, resources and other social environmental factors were necessary for improving healthy eating and medication adherence. The extra variables of age, waist circumference, health related quality of life and depression indirectly influenced intention towards the three behaviours mainly mediated through attitude and perceived behavioural control. Depression was a serious health problem that reduced the three health behaviours‘ motivation, mediated through decreased self-efficacy and negative attitude. This research provided evidence that self-efficacy is similar to perceived behavioural control in the TPB model and intention is a proximal goal toward a particular behaviour. Combining four sources of information in the self-efficacy model with the TPB model would improve chronic disease patients‘ self management behaviour and reach an improved long-term treatment outcome. Conclusion: Health intervention programs that target chronic disease management should focus on patients‘ self-efficacy. A holistic approach which is patient-centred and involves a multidisciplinary collaboration strategy would be effective. Supporting the socio-economic environment and the mental/ emotional environment for older people needs to be considered within an integrated health care system.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

Demonstration of cellulosic ethanol production from sugarcane bagasse in Australia : the Mackay Renewable Biocommodities Pilot Plant

The ready availability of sugarcane bagasse at an existing industrial facility and the potential availability of extra fibre through trash collection make sugarcane fibre the best candidate for early stage commercialisation of cellulosic ethanol technologies. The commercialisation of cellulosic ethanol technologies in the sugar industry requires both development of novel technologies and the assessment of these technologies at a pre-commercial scale. In 2007, the Queensland University of Technology (QUT) received funding from the Australian and Queensland Governments to construct a pilot research and development facility for the production of bioethanol and other renewable biocommodities from biomass including sugarcane bagasse. This facility has been built on the site of the Racecourse Sugar Mill in Mackay, Queensland and is known as the Mackay Renewable Biocommodities Pilot Plant (MRBPP). This research facility is capable of processing cellulosic biomass by a variety of pretreatment technologies and includes equipment for enzymatic saccharification, fermentation and distillation to produce ethanol. Lignin and fermentation co-products can also be produced in the pilot facility.

Bioimpedance in the assessment of unilateral lymphedema of a limb: the optimal frequency

Background: Bioimpedance techniques provide a reliable method of assessing unilateral lymphedema in a clinical setting. Bioimpedance devices are traditionally used to assess body composition at a current frequency of 50 kHz. However, these devices are not transferable to the assessment of lymphedema, as the sensitivity of measuring the impedance of extracellular fluid is frequency dependent. It has previously been shown that the best frequency to detect extracellular fluid is 0 kHz (or DC). However, measurement at this frequency is not possible in practice due to the high skin impedance at DC, and an estimate is usually determined from low frequency measurements. This study investigated the efficacy of various low frequency ranges for the detection of lymphedema. Methods and Results: Limb impedance was measured at 256 frequencies between 3 kHz and 1000 kHz for a sample control population, arm lymphedema population, and leg lymphedema population. Limb impedance was measured using the ImpediMed SFB7 and ImpediMed L-Dex® U400 with equipotential electrode placement on the wrists and ankles. The contralateral limb impedance ratio for arms and legs was used to calculate a lymphedema index (L-Dex) at each measurement frequency. The standard deviation of the limb impedance ratio in a healthy control population has been shown to increase with frequency for both the arm and leg. Box and whisker plots of the spread of the control and lymphedema populations show that there exists good differentiation between the arm and leg L-Dex measured for lymphedema subjects and the arm and leg L-Dex measured for control subjects up to a frequency of about 30 kHz. Conclusions: It can be concluded that impedance measurements above a frequency of 30 kHz decrease sensitivity to extracellular fluid and are not reliable for early detection of lymphedema.