Faecal incontinence in childhood : a multidisciplinary approach including biofeedback

One hundred and seven children with faecal incontinence were evaluated and managed over a 3 year period by a multidisciplinary team. After initial clinical assessment, evaluation of defaecatory mechanisms (using a balloon model) and assessment of personal-social development and self-concept were undertaken. Management was based on initial bowel evacuation, short-term laxatives, and habit training involving systematic use of positive reinforcement; 69 children received biofeedback conditioning. Idiopathic megacolon with constipation and soiling was the most common finding (98 cases). Other diagnoses included previously undiagnosed neurogenic bowel (three cases), post-surgical anal anomalies (four cases), and psychogenic encopresis (two cases). Idiopathic megacolon was characterized by decreased rectal sensation, increased threshold for external sphincter relaxation and an inability to evacuate. Faecal incontinence was associated with an undesirably low social self-concept (70% of the 40 evaluated), but was not related to a delay in development (mean general developmental quotient = 105 ± 8, for the 35 tested). Family psychopathology warranting referral for family therapy was found in 14 children (13%). The management programme yielded a short-term (3 months) cure rate of 68% and a long-term (12 months) cure rate of 90%, with 10% having continued soiling which varied from occasional to several incidents/week. No significant improvement in self-concept was observed overall, although marked improvements were observed in some children. We conclude that disordered defaecatory dynamics are a major determinant of faecal incontinence in children. Undesirably low social self-concepts but normal developmental ability accompany this condition. Management is facilitated by a multidisciplinary approach, acknowledging the role of both behavioural and physiological components of the problem. This approach is effective in eradicating soiling in the majority of cases, comparing favourably with other published data.

Childhood experiences of cancer : an interpretative phenomenological analysis approach

Pediatric oncology has emerged as one of the great medical success stories of the last 4 decades. The cure rate of childhood cancer has increased from approximately 25% in the 1960’s to more than 75% in more recent years. However, very little is known about how children actually experience the diagnosis and treatment of their illness. A total of 9 families in which a child was diagnosed with cancer were interviewed twice over a 12-month period. Using the qualitative methodology of interpretative phenomenological analysis (IPA), children’s experiences of being patients with a diagnosis of cancer were explicated. The results revealed 5 significant themes: the experience of illness, the upside of being sick, refocusing on what is important, acquiring a new perspective, and the experience of returning to wellbeing. Changes over time were noted because children’s experiences’ were often pertinent to the stage of treatment the child had reached. These results revealed rich and intimate information about a sensitive issue with implications for understanding child development and medical and psychosocial treatment.

Refund of imputation credits

The purpose of this paper is to explain the features of the new provisions for the refund of imputation credits, which are contained in the New Business Tax System (Miscellaneous) Act (No1) 2000.1 The provisions have been introduced to ensure that:  certain eligible resident taxpayers are taxed on their dividend income at their personal marginal rate of tax; and  certain eligible resident nonprofit organisations can apply their tax exemption on their dividend income. The provisions are contained in Division 67 of the Income Tax Assessment Act 1997 for refunds to resident individuals and superannuation entities and Division 7AA of Part IIIA of the Income Tax Assessment Act 1936 for refunds to endorsed income tax exempt charities and certain deductible gift recipients.

The effects of metabolic work rate and ambient environment on physiological tolerance times while wearing explosive and chemical personal protective equipment

This study evaluated the physiological tolerance times when wearing explosive and chemical (>35kg) personal protective equipment (PPE) in simulated environmental extremes across a range of differing work intensities. Twelve healthy males undertook nine trials which involved walking on a treadmill at 2.5, 4 and 5.5 km.h-1 in the following environmental conditions, 21, 30 and 37 °C wet bulb globe temperature (WBGT). Participants exercised for 60 min or until volitional fatigue, core temperature reached 39 °C, or heart rate exceeded 90% of maximum. Tolerance time, core temperature, skin temperature, mean body temperature, heart rate and body mass loss were measured. Exercise time was reduced in the higher WBGT environments (WBGT37rate exceeding 90% of their maximum. A total of eight trials (7.4%) lasted the full duration. Only nine (8.3%) trials were terminated due to volitional fatigue and six (5.6%) due to core temperatures in excess of 39 °C. These results demonstrate that physiological tolerance times are influenced by the external environment and workload, and that cardiovascular strain is the limiting factor to work tolerance when wearing this heavy multi layered PPE.

Relative entropy rate based multiple hidden Markov Model Approximation

This paper proposes a novel relative entropy rate (RER) based approach for multiple HMM (MHMM) approximation of a class of discrete-time uncertain processes. Under different uncertainty assumptions, the model design problem is posed either as a min-max optimisation problem or stochastic minimisation problem on the RER between joint laws describing the state and output processes (rather than the more usual RER between output processes). A suitable filter is proposed for which performance results are established which bound conditional mean estimation performance and show that estimation performance improves as the RER is reduced. These filter consistency and convergence bounds are the first results characterising multiple HMM approximation performance and suggest that joint RER concepts provide a useful model selection criteria. The proposed model design process and MHMM filter are demonstrated on an important image processing dim-target detection problem.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.

Bitrate modeling of scalable videos using quantization parameter, frame rate and spatial resolution

The quality and bitrate modeling is essential to effectively adapt the bitrate and quality of videos when delivered to multiplatform devices over resource constraint heterogeneous networks. The recent model proposed by Wang et al. estimates the bitrate and quality of videos in terms of the frame rate and quantization parameter. However, to build an effective video adaptation framework, it is crucial to incorporate the spatial resolution in the analytical model for bitrate and perceptual quality adaptation. Hence, this paper proposes an analytical model to estimate the bitrate of videos in terms of quantization parameter, frame rate, and spatial resolution. The model can fit the measured data accurately which is evident from the high Pearson correlation. The proposed model is based on the observation that the relative reduction in bitrate due to decreasing spatial resolution is independent of the quantization parameter and frame rate. This modeling can be used for rate-constrained bit-stream adaptation scheme which selects the scalability parameters to optimize the perceptual quality for a given bandwidth constraint.

A conditional retransmission enabled transport protocol for real-time networked control systems

Real-time networked control systems (NCSs) over data networks are being increasingly implemented on a massive scale in industrial applications. Along with this trend, wireless network technologies have been promoted for modern wireless NCSs (WNCSs). However, popular wireless network standards such as IEEE 802.11/15/16 are not designed for real-time communications. Key issues in real-time applications include limited transmission reliability and poor transmission delay performance. Considering the unique features of real-time control systems, this paper develops a conditional retransmission enabled transport protocol (CRETP) to improve the delay performance of the transmission control protocol (TCP) and also the reliability performance of the user datagram protocol (UDP) and its variants. Key features of the CRETP include a connectionless mechanism with acknowledgement (ACK), conditional retransmission and detection of ineffective data packets on the receiver side.

Adaptive bilateral filtering using saliency map for deblocking low bit rate videos

This paper proposes a novel approach to video deblocking which performs perceptually adaptive bilateral filtering by considering color, intensity, and motion features in a holistic manner. The method is based on bilateral filter which is an effective smoothing filter that preserves edges. The bilateral filter parameters are adaptive and avoid over-blurring of texture regions and at the same time eliminate blocking artefacts in the smooth region and areas of slow motion content. This is achieved by using a saliency map to control the strength of the filter for each individual point in the image based on its perceptual importance. The experimental results demonstrate that the proposed algorithm is effective in deblocking highly compressed video sequences and to avoid over-blurring of edges and textures in salient regions of image.

No cure for curiosity: Linking physical and digital urban layers

Although mobile phones are often used in public urban places to interact with one’s geographically dispersed social circle, they can also facilitate interactions with people in the same public urban space. The PlaceTagz study investigates how physical artefacts in public urban places can be utilised and combined with mobile phone technologies to facilitate interactions. Printed on stickers, PlaceTagz are QR codes linking to a digital message board enabling collocated users to interact with each other over time resulting in a place-based digital memory. This exploratory project set out to investigate if and how PlaceTagz are used by urban dwellers in a real world deployment. We present findings from analysing content received through PlaceTagz and interview data from application users. QR codes, which do not contain any contextual information, piqued the curiosity of users wondering about the embedded link’s destination and provoked comments in regards to people, place and technology.

Negligible heat strain in armored vehicle officers wearing personal body armor

Objectives This study evaluated the heat strain experienced by armored vehicle officers (AVOs) wearing personal body armor (PBA) in a sub-tropical climate. Methods Twelve male AVOs, aged 35-58 years, undertook an eight hour shift while wearing PBA. Heart rate and core temperature were monitored continuously. Urine specific gravity (USG) was measured before and after, and with any urination during the shift. Results Heart rate indicated an intermittent and low-intensity nature of the work. USG revealed six AVOs were dehydrated from pre through post shift, and two others became dehydrated. Core temperature averaged 37.4 ± 0.3°C, with maximum's of 37.7 ± 0.2°C. Conclusions Despite increased age, body mass, and poor hydration practices, and Wet-Bulb Globe Temperatures in excess of 30°C; the intermittent nature and low intensity of the work prevented excessive heat strain from developing.

An efficient DSE using conditional multivariate complex Gaussian distribution

This paper presents an efficient noniterative method for distribution state estimation using conditional multivariate complex Gaussian distribution (CMCGD). In the proposed method, the mean and standard deviation (SD) of the state variables is obtained in one step considering load uncertainties, measurement errors, and load correlations. In this method, first the bus voltages, branch currents, and injection currents are represented by MCGD using direct load flow and a linear transformation. Then, the mean and SD of bus voltages, or other states, are calculated using CMCGD and estimation of variance method. The mean and SD of pseudo measurements, as well as spatial correlations between pseudo measurements, are modeled based on the historical data for different levels of load duration curve. The proposed method can handle load uncertainties without using time-consuming approaches such as Monte Carlo. Simulation results of two case studies, six-bus, and a realistic 747-bus distribution network show the effectiveness of the proposed method in terms of speed, accuracy, and quality against the conventional approach.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Raman microscopy of formamide-intercalated kaolinites treated by controlled-rate thermal analysis technology

Raman spectroscopy of formamide-intercalated kaolinites treated using controlled-rate thermal analysis technology (CRTA), allowing the separation of adsorbed formamide from intercalated formamide in formamide-intercalated kaolinites, is reported. The Raman spectra of the CRTA-treated formamide-intercalated kaolinites are significantly different from those of the intercalated kaolinites, which display a combination of both intercalated and adsorbed formamide. An intense band is observed at 3629 cm-1, attributed to the inner surface hydroxyls hydrogen bonded to the formamide. Broad bands are observed at 3600 and 3639 cm-1, assigned to the inner surface hydroxyls, which are hydrogen bonded to the adsorbed water molecules. The hydroxyl-stretching band of the inner hydroxyl is observed at 3621 cm-1 in the Raman spectra of the CRTA-treated formamide-intercalated kaolinites. The results of thermal analysis show that the amount of intercalated formamide between the kaolinite layers is independent of the presence of water. Significant differences are observed in the CO stretching region between the adsorbed and intercalated formamide.