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.

The view from here : educational program creates momentum for positive change

Kate Nayton, Elaine Fielding and Elizabeth Beattie describe how they developed a successful program to educate hospital staff about dementia care. The program may soon be trialled in other acute care facilities.

Waves for change : the role of the South Tarawa-based women's interests program in the decolonisation process of the Gilbert Islands

This thesis has created a space for women in the history of the decolonisation of the Gilbert Islands. It traces the historical development of the national women's interests program in the Republic of Kiribati (formerly of the Gilbert and Ellice Islands Colony (GEIC)) as it was implemented through a network of women's clubs during the 1960s and 1970s. This thesis has provided the first history and interpretation of the Indigenous women's interests movement as it impacted the Gilbert Islands. It offers a narrative of the movement in terms of three overlapping waves of women leaders, based on an analysis of fieldwork, archival research and interviews conducted on South Tarawa, Kiribati.

The CLIMATE schools combined study : a cluster randomised controlled trial of a universal Internet-based prevention program for youth substance misuse, depression and anxiety

Background Anxiety, depressive and substance use disorders account for three quarters of the disability attributed to mental disorders and frequently co-occur. While programs for the prevention and reduction of symptoms associated with (i) substance use and (ii) mental health disorders exist, research is yet to determine if a combined approach is more effective. This paper describes the study protocol of a cluster randomised controlled trial to evaluate the effectiveness of the CLIMATE Schools Combined intervention, a universal approach to preventing substance use and mental health problems among adolescents. Methods/design Participants will consist of approximately 8400 students aged 13 to 14-years-old from 84 secondary schools in New South Wales, Western Australia and Queensland, Australia. The schools will be cluster randomised to one of four groups; (i) CLIMATE Schools Combined intervention; (ii) CLIMATE Schools - Substance Use; (iii) CLIMATE Schools - Mental Health, or (iv) Control (Health and Physical Education as usual). The primary outcomes of the trial will be the uptake and harmful use of alcohol and other drugs, mental health symptomatology and anxiety, depression and substance use knowledge. Secondary outcomes include substance use related harms, self-efficacy to resist peer pressure, general disability, and truancy. The link between personality and substance use will also be examined. Discussion Compared to students who receive the universal CLIMATE Schools - Substance Use, or CLIMATE Schools - Mental Health or the Control condition (who received usual Health and Physical Education), we expect students who receive the CLIMATE Schools Combined intervention to show greater delays to the initiation of substance use, reductions in substance use and mental health symptoms, and increased substance use and mental health knowledge

Improving dental and medical students’ psychological health using a self-development coaching program : a pilot study

Introduction: Dental and medical students worldwide, including in Saudi Arabia, have been reported to have a high incidence of poor psychological health, such as depression, stress, anxiety, and lowlife satisfaction. Self-development coaching programs have become an increasingly popular way to improve individuals’ lives. However, few studies have evaluated the psychological effects of such programs among dental and medical students. Moreover, no studies have been conducted on self-development coaching programs in Saudi Arabia. Aims: The aim of this study was to assess the feasibility of a larger study via a pilot study and to acquire preliminary findings about the effectiveness of a self-development coaching program on psychological health among dental and medical students in Saudi Arabia. Methods: A pre-post interventional study design was used to test a self-development coaching program (How to be an Ultra-Super Student) with a sample of medical students (n=17) at Umm Al-Qura University at Saudi Arabia. The outcome measures were students’ psychological distress (depression, anxiety, and stress), life satisfaction, self-efficacy, the coach, and coaching program characteristics. Results: The study showed that there was a significant improvement in depression (p=0.04), self-efficacy (p=0.02), and satisfaction with life (p=0.04), which supported the feasibility of a large study in the future. Conclusions: The study’s findings encourage the implementation of a randomized, controlled trial study with a larger sample to further test the effectiveness of using self-development coaching programs with medical and dental students in Saudi Arabia to improve their psychological health.

Satisfaction survey for a medication management program : satisfaction guaranteed?

BACKGROUND Providing clinical pharmacy services to patients in their homes after discharge from hospital has been reported to reduce health care costs and improve outcomes. The Medication Management Program of the Fraser Health Authority involves pharmacists making home visits to provide clinical pharmacy services to elderly patients who have recently been discharged from hospital and others considered to be at high risk for adverse drug events. Although clinical and economic outcomes of this program have been evaluated, humanistic outcomes such as satisfaction have not been assessed. Moreover, very little evaluation of patient satisfaction with home pharmacy services has been reported in the literature. OBJECTIVE To evaluate patient satisfaction with the Medication Management Program. METHODS A telephone survey instrument, consisting of 7 Likert-scale items and 2 open-ended questions, was developed and administered to patients who received a home pharmacist visit between September 1 and November 23, 2011. In addition to the survey responses, demographic and clinical data for both respondents and nonrespondents were collected. RESULTS Of the 175 patients invited to participate in the survey, 103 (58.9%) agreed to participate. The majority of respondents agreed or strongly agreed with all of the survey items, indicating satisfaction with the program. For example, 97 (94%) agreed or strongly agreed that they would recommend the pharmacist home visit program continue to be available, and all 103 (100%) agreed or strongly agreed that they were satisfied with the pharmacist home visit. Respondents provided some suggestions for program improvement. CONCLUSIONS The survey findings demonstrate that patients were satisfied with the home clinical pharmacy services offered through the Fraser Health Medication Management Program.

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.

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

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.

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.