Back to basics: Informing the public of co-morbid physical health problems in those with mental illness

Abstract OBJECTIVE: Those with mental illness are at increased risk of physical health problems. The current study aimed to examine the information available online to the Australian public about the increased risk and consequences of physical illness in those with mental health problems and the services available to address these co-morbidities. METHODS: A structured online search was conducted with the search engine Google Australia (www.google.com.au) using generic search terms 'mental health information Australia', 'mental illness information Australia', 'depression', 'anxiety', and 'psychosis'. The direct content of websites was examined for information on the physical co-morbidities of mental illness. All external links on high-profile websites [the first five websites retrieved under each search term (n = 25)] were examined for information pertaining to physical health. RESULTS: Only 4.2% of websites informing the public about mental health contained direct content information about the increased risk of physical co-morbidities. The Australian Government's Department of Health and Ageing site did not contain any information. Of the high-profile websites, 62% had external links to resources about physical health and 55% had recommendations or resources for physical health. Most recommendations were generic. CONCLUSIONS: Relative to the seriousness of this problem, there is a paucity of information available to the public about the increased physical health risks associated with mental illness. Improved public awareness is the starting point of addressing this health inequity.

Worrying and anxiety are some of the biggest mental health problems facing children today

People often assume children have no worries or nothing to be stressed about. However, children, like adults, do worry about a range of things. There may be times during periods of stress or change when children worry more intensely about things than usual.

Multi-criteria optimization of group replacement schedules for distributed water pipeline assets

This thesis presents a multi-criteria optimisation study of group replacement schedules for water pipelines, which is a capital-intensive and service critical decision. A new mathematical model was developed, which minimises total replacement costs while maintaining a satisfactory level of services. The research outcomes are expected to enrich the body of knowledge of multi-criteria decision optimisation, where group scheduling is required. The model has the potential to optimise replacement planning for other types of linear asset networks resulting in bottom-line benefits for end users and communities. The results of a real case study show that the new model can effectively reduced the total costs and service interruptions.

Optimal camera planning under versatile user constraints in multi-camera image processing systems

The selection of optimal camera configurations (camera locations, orientations, etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we propose a statistical framework of the problem as well as propose a trans-dimensional simulated annealing algorithm to effectively deal with it. We compare our approach with a state-of-the-art method based on binary integer programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than two alternative heuristics designed to deal with the scalability issue of BIP. Last, we show the versatility of our approach using a number of specific scenarios.

The Child Behavior Checklist and Youth Self-Report in adolescents with epilepsy : testing measurement invariance of the Attention and Thought Problems subscales

The objective of this study was to test for the measurement invariance of the Attention and Thought Problems subscales of the Child Behavior Checklist (CBCL) and Youth Self-Report (YSR) in a population-based sample of adolescents with and without epilepsy. Data were obtained from the 14-year follow-up of the Mater University Study of Pregnancy in which 33 adolescents with epilepsy and 1068 healthy controls were included for analysis. Confirmatory factor analysis was used to test for measurement invariance between adolescents with and without epilepsy. Structural equation modeling was used to test for group differences in attention and thought problems as measured with the CBCL and YSR. Measurement invariance was demonstrated for the original CBCL Attention Problems and YSR Thought Problems. After the removal of ambiguous items (“confused” and “daydreams”),measurement invariance was established for the YSR Attention Problems. The original and reduced CBCL Thought Problems were noninvariant. Adolescents with epilepsy had significantly more symptoms of behavioral problems on the CBCL Attention Problems, β = 0.51, p = 0.002, compared with healthy controls. In contrast, no significant differences were found for the YSR Attention and Thought Problems, β = −0.11, p = 0.417 and β = −0.20, p = 0.116, respectively. In this population-based sample of adolescents with epilepsy, the CBCL Attention Problems and YSR Thought Problems appear to be valid measures of behavioral problems, whereas the YSR Attention Problems was valid only after the removal of ambiguous items. Replication of these findings in clinical samples of adolescents with epilepsy that overcome the limitations of the current study is warranted.

Screening for risk of later mental health problems : a longitudinal study

Purpose: This is one of the first studies to report that the Achenbach internalising scales were much more effective at identifying those with current comorbid depression and anxiety, rather than individual mood disorder. Introduction: The Achenbach behaviour checklists (YSR,YASR) are widely used, low cost screening tools used to assess problem behaviour. Several studies report good association between the checklists and psychiatric diagnoses; although with varying degrees of agreement. Most are cross-sectional studies involving adolescents referred to mental health services; few are in large community-based studies. This study examined the usefulness of the Achenbach internalising scales in the primary screening (both predictive and concurrent)for depression and anxiety. Methods: The sample was 2400 young adults from an Australian population-based prospective birth cohort study. The association between the empirical anxiety and depression scales were individually assessed against DSM-IV depression and anxiety diagnoses. Odds ratios and diagnostic efficiency tests report the findings. Results: Adolescents with internalising symptoms were twice (OR 2.3, 95%CI 1.7 to 3.1) as likely to be diagnosed with later DSM-IV depression. YASR internalising scale predicted DSM-IV mood disorders (depression OR = 6.9, 95% CI 5.0–9.5; anxiety OR = 5.1, 95% CI 3.8–6.7) in the previous 12 months. The internalising scales were much more effective at identifying those with comorbid depression and anxiety. Conclusions: Adolescence and early adulthood are key risk periods for the onset of anxiety and depression. This study found that young people with internalising behaviour problems were more likely to have comorbid depression and anxiety DSM-IV disorder.

'Explain in plain English' questions revisited : data structures problems

Recent studies have linked the ability of novice (CS1) programmers to read and explain code with their ability to write code. This study extends earlier work by asking CS2 students to explain object-oriented data structures problems that involve recursion. Results show a strong correlation between ability to explain code at an abstract level and performance on code writing and code reading test problems for these object-oriented data structures problems. The authors postulate that there is a common set of skills concerned with reasoning about programs that explains the correlation between writing code and explaining code. The authors suggest that an overly exclusive emphasis on code writing may be detrimental to learning to program. Non-code writing learning activities (e.g., reading and explaining code) are likely to improve student ability to reason about code and, by extension, improve student ability to write code. A judicious mix of code-writing and code-reading activities is recommended.

A modified particle swarm optimization for correlated phenomena

The wide applicability of correlation analysis inspired the development of this paper. In this paper, a new correlated modified particle swarm optimization (COM-PSO) is developed. The Correlation Adjustment algorithm is proposed to recover the correlation between the considered variables of all particles at each of iterations. It is shown that the best solution, the mean and standard deviation of the solutions over the multiple runs as well as the convergence speed were improved when the correlation between the variables was increased. However, for some rotated benchmark function, the contrary results are obtained. Moreover, the best solution, the mean and standard deviation of the solutions are improved when the number of correlated variables of the benchmark functions is increased. The results of simulations and convergence performance are compared with the original PSO. The improvement of results, the convergence speed, and the ability to simulate the correlated phenomena by the proposed COM-PSO are discussed by the experimental results.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.

Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm

This paper presents an efficient algorithm for multi-objective distribution feeder reconfiguration based on Modified Honey Bee Mating Optimization (MHBMO) approach. The main objective of the Distribution feeder reconfiguration (DFR) is to minimize the real power loss, deviation of the nodes’ voltage. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. So the metahuristic algorithm has been applied to this problem. This paper describes the full algorithm to Objective functions paid, The results of simulations on a 32 bus distribution system is given and shown high accuracy and optimize the proposed algorithm in power loss minimization.

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

Mechanism based emulation of dynamic simulation models : concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.

Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

The potential of XML technology as an answer to the data interchange problems of the construction industry

The complex supply chain relations of the construction industry, coupled with the substantial amount of information to be shared on a regular basis between the parties involved, make the traditional paper-based data interchange methods inefficient, error prone and expensive. The successful information technology (IT) applications that enable seamless data interchange, such as the Electronic Data Interchange (EDI) systems, have generally failed to be successfully implemented in the construction industry. An alternative emerging technology, Extensible Markup Language (XML), and its applicability to streamline business processes and to improve data interchange methods within the construction industry are analysed, as is the EDI technology to identify the strategic advantages that XML technology provides to overcome the barriers to implementation. In addition, the successful implementation of XML-based automated data interchange platforms for a large organization, and the proposed benefits thereof, are presented as a case study.