Internet-Based Interventions for Psychological Problems

Presentation about internet based interventions for depression, substance and alcohol abuse.

An investigation of foot and ankle problems experienced by nurses

Relatively little information has been reported about foot and ankle problems experienced by nurses, despite anecdotal evidence which suggests they are common ailments. The purpose of this study was to improve knowledge about the prevalence of foot and ankle musculoskeletal disorders (MSDs) and to explore relationships between these MSDs and proposed risk factors. A review of the literature relating to work-related MSDs, MSDs in nursing, foot and lower-limb MSDs, screening for work-related MSDs, foot discomfort, footwear and the prevalence of foot problems in the community was undertaken. Based on the review, theoretical risk factors were proposed that pertained to the individual characteristics of the nurses, their work activity or their work environment. Three studies were then undertaken. A cross-sectional survey of 304 nurses, working in a large tertiary paediatric hospital, established the prevalence of foot and ankle MSDs. The survey collected information about self-reported risk factors of interest. The second study involved the clinical examination of a subgroup of 40 nurses, to examine changes in body discomfort, foot discomfort and postural sway over the course of a single work shift. Objective measurements of additional risk factors, such as individual foot posture (arch index) and the hardness of shoe midsoles, were performed. A final study was used to confirm the test-retest reliability of important aspects of the survey and key clinical measurements. Foot and ankle problems were the most common MSDs experienced by nurses in the preceding seven days (42.7% of nurses). They were the second most common MSDs to cause disability in the last 12 months (17.4% of nurses), and the third most common MSDs experienced by nurses in the last 12 months (54% of nurses). Substantial foot discomfort (Visual Analogue Scale (VAS) score of 50mm or more) was experienced by 48.5% of nurses at sometime in the last 12 months. Individual risk factors, such as obesity and the number of self-reported foot conditions (e.g., callouses, curled toes, flat feet) were strongly associated with the likelihood of experiencing foot problems in the last seven days or during the last 12 months. These risk factors showed consistent associations with disabling foot conditions and substantial foot discomfort. Some of these associations were dependent upon work-related risk factors, such as the location within the hospital and the average hours worked per week. Working in the intensive care unit was associated with higher odds of experiencing foot problems within the last seven days, foot problems in the last 12 months and foot problems that impaired activity in the last 12 months. Changes in foot discomfort experienced within a day, showed large individual variability. Fifteen of the forty nurses experienced moderate/substantial foot discomfort at the end of their shift (VAS 25+mm). Analysis of the association between risk factors and moderate/substantial foot discomfort revealed that foot discomfort was less likely for nurses who were older, had greater BMI or had lower foot arches, as indicated by higher arch index scores. The nurses’ postural sway decreased over the course of the work shift, suggesting improved body balance by the end of the day. These findings were unexpected. Further clinical studies examining individual nurses on several work shifts are needed to confirm these results, particularly due to the small sample size and the single measurement occasion. There are more than 280,000 nurses registered to practice in Australia. The nursing workforce is ageing and the prevalence of foot problems will increase. If the prevalence estimates from this study are extrapolated to the profession generally, more than 70,000 hospital nurses have experienced substantial foot discomfort and 25-30,000 hospital nurses have been limited in their activity due to foot problems during the last 12 months. Nurses with underlying foot conditions were more likely to report having foot problems at work. Strategies to prevent or manage foot conditions exist and they should be disseminated to nurses. Obesity is a significant risk factor for foot and ankle MSDs and these nurses may need particular assistance to manage foot problems. The risk of foot problems for particular groups of nurses, e.g. obese nurses, may vary depending upon the location within the hospital. Further research is needed to confirm the findings of this study. Similar studies should be conducted in other occupational groups that require workers to stand for prolonged periods.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Modelling and solving train scheduling problems under capacity constraints

Many large coal mining operations in Australia rely heavily on the rail network to transport coal from mines to coal terminals at ports for shipment. Over the last few years, due to the fast growing demand, the coal rail network is becoming one of the worst industrial bottlenecks in Australia. As a result, this provides great incentives for pursuing better optimisation and control strategies for the operation of the whole rail transportation system under network and terminal capacity constraints. This PhD research aims to achieve a significant efficiency improvement in a coal rail network on the basis of the development of standard modelling approaches and generic solution techniques. Generally, the train scheduling problem can be modelled as a Blocking Parallel- Machine Job-Shop Scheduling (BPMJSS) problem. In a BPMJSS model for train scheduling, trains and sections respectively are synonymous with jobs and machines and an operation is regarded as the movement/traversal of a train across a section. To begin, an improved shifting bottleneck procedure algorithm combined with metaheuristics has been developed to efficiently solve the Parallel-Machine Job- Shop Scheduling (PMJSS) problems without the blocking conditions. Due to the lack of buffer space, the real-life train scheduling should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold a train until the next section on the routing becomes available. As a consequence, the problem has been considered as BPMJSS with the blocking conditions. To develop efficient solution techniques for BPMJSS, extensive studies on the nonclassical scheduling problems regarding the various buffer conditions (i.e. blocking, no-wait, limited-buffer, unlimited-buffer and combined-buffer) have been done. In this procedure, an alternative graph as an extension of the classical disjunctive graph is developed and specially designed for the non-classical scheduling problems such as the blocking flow-shop scheduling (BFSS), no-wait flow-shop scheduling (NWFSS), and blocking job-shop scheduling (BJSS) problems. By exploring the blocking characteristics based on the alternative graph, a new algorithm called the topological-sequence algorithm is developed for solving the non-classical scheduling problems. To indicate the preeminence of the proposed algorithm, we compare it with two known algorithms (i.e. Recursive Procedure and Directed Graph) in the literature. Moreover, we define a new type of non-classical scheduling problem, called combined-buffer flow-shop scheduling (CBFSS), which covers four extreme cases: the classical FSS (FSS) with infinite buffer, the blocking FSS (BFSS) with no buffer, the no-wait FSS (NWFSS) and the limited-buffer FSS (LBFSS). After exploring the structural properties of CBFSS, we propose an innovative constructive algorithm named the LK algorithm to construct the feasible CBFSS schedule. Detailed numerical illustrations for the various cases are presented and analysed. By adjusting only the attributes in the data input, the proposed LK algorithm is generic and enables the construction of the feasible schedules for many types of non-classical scheduling problems with different buffer constraints. Inspired by the shifting bottleneck procedure algorithm for PMJSS and characteristic analysis based on the alternative graph for non-classical scheduling problems, a new constructive algorithm called the Feasibility Satisfaction Procedure (FSP) is proposed to obtain the feasible BPMJSS solution. A real-world train scheduling case is used for illustrating and comparing the PMJSS and BPMJSS models. Some real-life applications including considering the train length, upgrading the track sections, accelerating a tardy train and changing the bottleneck sections are discussed. Furthermore, the BPMJSS model is generalised to be a No-Wait Blocking Parallel- Machine Job-Shop Scheduling (NWBPMJSS) problem for scheduling the trains with priorities, in which prioritised trains such as express passenger trains are considered simultaneously with non-prioritised trains such as freight trains. In this case, no-wait conditions, which are more restrictive constraints than blocking constraints, arise when considering the prioritised trains that should traverse continuously without any interruption or any unplanned pauses because of the high cost of waiting during travel. In comparison, non-prioritised trains are allowed to enter the next section immediately if possible or to remain in a section until the next section on the routing becomes available. Based on the FSP algorithm, a more generic algorithm called the SE algorithm is developed to solve a class of train scheduling problems in terms of different conditions in train scheduling environments. To construct the feasible train schedule, the proposed SE algorithm consists of many individual modules including the feasibility-satisfaction procedure, time-determination procedure, tune-up procedure and conflict-resolve procedure algorithms. To find a good train schedule, a two-stage hybrid heuristic algorithm called the SE-BIH algorithm is developed by combining the constructive heuristic (i.e. the SE algorithm) and the local-search heuristic (i.e. the Best-Insertion- Heuristic algorithm). To optimise the train schedule, a three-stage algorithm called the SE-BIH-TS algorithm is developed by combining the tabu search (TS) metaheuristic with the SE-BIH algorithm. Finally, a case study is performed for a complex real-world coal rail network under network and terminal capacity constraints. The computational results validate that the proposed methodology would be very promising because it can be applied as a fundamental tool for modelling and solving many real-world scheduling problems.

Identifying child sexual abuse within school contexts : policy problems and solutions, and a landmark case

As a strategy to identify child sexual abuse, most Australian States and Territories have enacted legislation requiring teachers to report suspected cases. Some Australian State and non-State educational authorities have also created policy-based obligations to report suspected child sexual abuse. Significantly, these can be wider than non-existent or limited legislative duties, and therefore are a crucial element of the effort to identify sexual abuse. Yet, no research has explored the existence and nature of these policy-based duties. The first purpose of this paper is to report the results of a three-State study into policy-based reporting duties in State and non-State schools in Australia. In an extraordinary coincidence, while conducting the study, a case of failure to comply with reporting policy occurred with tragic consequences. This led to a rare example in Australia (and one of only a few worldwide) of a professional being prosecuted for failure to comply with a legislative duty. It also led to disciplinary proceedings against school staff. The second purpose of this paper is to describe this case and connect it with findings from our policy analysis.