The effects of the indoor environment of residential care homes on dementia suffers in Hong Kong : a critical incident technique approach

Dementia is an irreversible and incurable syndrome that leads to progressive impairment of cognitive functions and behavioural and psychological symptoms such as agitation, depression and psychosis. Appropriate environmental conditions can help delay its onset and progression, and indoor environmental (IE) factors have a major impact. However, there is no firm understanding of the full range of relevant IE factors and their impact levels. This paper describes a preliminary study to investigate the effects of IE on Hong Kong residential care homes (RCH) dementia residents. This involved six purposively selected focus groups, each comprising the main stakeholders of the dementia residents’ caregivers, RCH staff and/or registered nurses, and architects. Using the Critical Incident Technique, the main context and experiences of behavioural problems of dementia residents caused by IE were explored and the key causal RCH IE quality factors identified, together with the associated responses and stress levels involved. The findings indicate that the acoustic environment, lighting and thermal environment are the most important influencing factors. Many of the remedies provided by the focus groups are quite simple to carry out and are summarised in the form of recommendations to current RCHs providers and users. The knowledge acquired in this initial study will help enrich the knowledge of IE design for dementiaspecific residential facilities. It also provides some preliminary insights for healthcare policymakers and practitioners in the building design/facilities management and dementia-care sectors into the IE factors contributing to a more comfortable, healthy and sustainable RCH living environment in Hong Kong.

Development of empirical equations for irradiance profile of a standard parabolic trough collector using Monte Carlo ray tracing technique

Irradiance profile around the receiver tube (RT) of a parabolic trough collector (PTC) is a key effect of optical performance that affects the overall energy performance of the collector. Thermal performance evaluation of the RT relies on the appropriate determination of the irradiance profile. This article explains a technique in which empirical equations were developed to calculate the local irradiance as a function of angular location of the RT of a standard PTC using a vigorously verified Monte Carlo ray tracing model. A large range of test conditions including daily normal insolation, spectral selective coatings and glass envelop conditions were selected from the published data by Dudley et al. [1] for the job. The R2 values of the equations are excellent that vary in between 0.9857 and 0.9999. Therefore, these equations can be used confidently to produce realistic non-uniform boundary heat flux profile around the RT at normal incidence for conjugate heat transfer analyses of the collector. Required values in the equations are daily normal insolation, and the spectral selective properties of the collector components. Since the equations are polynomial functions, data processing software can be employed to calculate the flux profile very easily and quickly. The ultimate goal of this research is to make the concentrating solar power technology cost competitive with conventional energy technology facilitating its ongoing research.

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.

A Human identification technique using images of the iris and wavelet transform

A new approach for recognizing the iris of the human eye is presented. Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1-D) signals are compared with model features using different dissimilarity functions.

An evaluation of clinical procedures used in dental implant treatment in posterior maxilla using flapless technique

This study was a measure forward in cultivating the scientific basis for an approach to examine clinical procedure in Flapless dental implant surgery. The thesis is based on: the systematic review, retrospective study of flapless implants, and in vivo study on the osseo-integration in osteoporotic rats. Dr Doan investigated "clinical procedures used in dental implant treatment in posterior maxilla using flapless technique". The work has yielded significant contributions to the area of implant flapless surgery and its effects on osteoporotic patients having implants in the posterior maxilla.

Measuring exposure to solar ultraviolet radiation using a dosimetric technique : understanding participant compliance issues

Personal ultraviolet dosimeters have been used in epidemiological studies to understand the risks and benefits of individuals' exposure to solar ultraviolet radiation (UVR). We investigated the types and determinants of non-compliance associated with a protocol for use of polysulphone UVR dosimeters. In the AusD Study, 1,002 Australian adults (aged 18-75 years) were asked to wear a new dosimeter on their wrist each day for 10 consecutive days to quantify their daily exposure to solar UVR. Of the 10,020 dosimeters distributed, 296 (3%) were not returned or used (Type I non-compliance) and other usage errors were reported for 763 (8%) returned dosimeters (Type II non-compliance). Type I errors were more common in participants with predominantly outdoor occupations. Type II errors were reported more frequently on the first day of measurement; weekend days or rainy days; and among females; younger people; more educated participants or those with outdoor occupations. Half (50%) the participants reported a non-compliance error on at least one day during the 10-day period. However, 92% of participants had at least 7 days of usable data without any apparent non-compliance issues. The factors identified should be considered when designing future UVR dosimetry studies.

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.

Evaluating leadership development using the Most Significant Change technique

Course evaluations are now a serious matter for universities trying to meet stakeholder needs and expectations, quality assurance, improvements and strategic decision making. Typically, students are invited to participate in surveys on how well the design and delivery aspects meet predetermined learning objectives, quality of teaching, and the types of improvements needed for future deliveries. We used the Most Significant Change technique to gather data on the impact of a leadership course on 18 Pacific Islanders who completed a Master of Education (Educational Leadership). Participants' views highlighted impacts that were of significance to the students and their workplaces. The findings demonstrate that the Most Significant Change technique offers a more comprehensive understanding of the impact of leadership development courses.

Spatial metaphor as spatial technique in the work of Michel Foucault

This essay argues that the deployment of spatial metaphor in the writing of Michel Foucault is indivisible from his spatial politics. Beginning with his 1967 essay "Of Other Spaces," the development of Foucault's spatial politics and his growing awareness of the importance to his work of spatial (particularly geographic) metaphors can be charted. The focus here is not the concretisation of Foucault's early spatial obsessions—particularly with regard to the concept of "heterotopia"—into a theory or model. Rather, I am concerned with the way in which those obsessions inform Foucault's major works, in particular The Archaeology of Knowledge and Discipline and Punish. These works, I argue, do not develop a theory of space, but instead perform, through their rhetoric, a kind of spatial praxis. In this sense, Foucault's metaphors become "spatial techniques" for the practice and production of power–knowledge.