Bounded lower subdifferentiability optimization techniques : applications

In this article we develop a global optimization algorithm for quasiconvex programming where the objective function is a Lipschitz function which may have "flat parts". We adapt the Extended Cutting Angle method to quasiconvex functions, which reduces significantly the number of iterations and objective function evaluations, and consequently the total computing time. Applications of such an algorithm to mathematical programming problems inwhich the objective function is derived from economic systems and location problems are described. Computational results are presented.

Identifying crucial gaps in our knowledge of the life-history of avian influenza viruses - An Australian perspective

We review our current knowledge of the epidemiology and ecology of avian influenza viruses (AIVs) in Australia in relation to the ecology of their hosts. Understanding the transmission and maintenance of low-pathogenic avian influenza (LPAI) viruses deserves scientific scrutiny because some of these may evolve to a high-pathogenic AIV (HPAI) phenotype. That the HPAI H5N1 has not been detected in Australia is thought to be a result of the low level of migratory connectivity between Asia and Australia. Some AIV strains are endemic to Australia, with Australian birds acting as a reservoir for these viruses. However, given the phylogenetic relationships between Australian and Eurasian strains, both avian migrants and resident birds within the continent must play a role in the ecology and epidemiology of AIVs in Australia. The extent to which individual variation in susceptibility to infection, previous infections, and behavioural changes in response to infection determine AIV epidemiology is little understood. Prevalence of AIVs among Australian avifauna is apparently low but, given their specific ecology and Australian conditions, prevalence may be higher in little-researched species and under specific environmental conditions.

Negations generated by bounded lattices t-norms

From the birth of fuzzy sets theory, several extensions have been proposed changing the possible membership values. Since fuzzy connectives such as t-norms and negations have an important role in theoretical as well as applied fuzzy logics, these connectives have been adapted for these generalized frameworks. Perhaps, an extension of fuzzy logic which generalizes the remaining extensions, proposed by Joseph Goguen in 1967, is to consider arbitrary bounded lattices for the values of the membership degrees. In this paper we extend the usual way of constructing fuzzy negations from t-norms for the bounded lattice t-norms and prove some properties of this construction.

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Providing legal services to rural and regional small business : gaps and opportunities

This article examines the findings from a survey of 207 regional small businesses and 68 regional lawyers that explored the small business experience of accessing legal services in rural and regional Victoria. In particular, it considers small business expectations of local legal practices, their degree of satisfaction with existing legal services and identifies current and potential demand and supply gaps. By doing so it seeks to determine potential areas in which regional law firms can improve, expand and refine their services in response to the current and emerging demands on them and the communities they serve.

(S,N)-implications on bounded lattices

Since the birth of the fuzzy sets theory several extensions have been proposed. For these extensions, different sets of membership functions were considered. Since fuzzy connectives, such as conjunctions, negations and implications, play an important role in the theory and applications of fuzzy logics, these connectives have also been extended. An extension of fuzzy logic, which generalizes the ones considered up to the present, was proposed by Joseph Goguen in 1967. In this extension, the membership values are drawn from arbitrary bounded lattices. The simplest and best studied class of fuzzy implications is the class of (S,N)-implications, and in this chapter we provide an extension of (S,N)-implications in the context of bounded lattice valued fuzzy logic, and we show that several properties of this class are preserved in this more general framework.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Design of a norm-bounded LQG controller for power distribution networks with distributed generation

This paper presents a novel excitation control design to improve the voltage profile of power distribution networks with distributed generation and induction motor loads. The system is linearised by perturbation technique. Controller is designed using the linear-quadratic-Gaussian (LQG) controller synthesis method. The LQG controller is addressed with norm-bounded uncertainty. The approach considered in this paper is to find the smallest upper bound on the H∞ norm of the uncertain system and to design an optimal controller based on this bound. The design method requires the solution of a linear matrix inequality. The performance of the controller is tested on a benchmark power distribution system. Simulation results show that the proposed controller provides impressive oscillation damping compared to the conventional excitation controller.

Patient safety and gaps management by registered nurses

Patient safety is a global imperative aimed at reducing the incidence and impact of preventable adverse events in healthcare. This study has demonstrated how effective nursing surveillance and successful everyday nursing performance can bridge ‘gaps’ in the system of care, mitigate errors, and protect patients from suffering otherwise preventable harm.