A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

Polynomial based key predistribution scheme in wireless mesh networks

Wireless mesh networks (WMNs) have the ability to integrate with other networks while providing a fast and cost-saving deployment. The network security is one of important challenge problems in this kind of networks. This paper is focused on key management between mesh and sensor networks. We propose an efficient key pre-distribution scheme based on two polynomials in wireless mesh networks by employing the nature of heterogeneity. Our scheme realizes the property of bloom filters, i.e., neighbor nodes can discover their shared keys but have no knowledge on the different keys possessed by the other node, without the probability of false positive. The analysis presented in this paper shows that our scheme has the ability to establish three different security level keys and achieves the property of self adaptive security for sensor networks with acceptable computation and communication consumption.

A protocol for a discrete choice experiment: understanding preferences of patients with cancer towards their cancer care across metropolitan and rural regions in Australia.

Medical decision-making in oncology is a complicated process and to date there are few studies examining how patients with cancer make choices with respect to different features of their care. It is also unknown whether patient choices vary by geographical location and how location could account for observed rural and metropolitan cancer differences. This paper describes an ongoing study that aims to (1) examine patient and healthcare-related factors that influence choices of patients with cancer; (2) measure and quantify preferences of patients with cancer towards cancer care using a discrete choice experiment (DCE) and (3) explore preference heterogeneity between metropolitan and rural locations.

H∞ observer-based control for discrete-time one-sided Lipschitz systems with unknown inputs

This paper investigates the problem of robust observer-based stabilization for a class of one-sided nonlinear discrete-time systems subjected to unknown inputs. We propose a simple simultaneous state and input estimator. A nonlinear controller is then proposed to compensate for the effects of unknown inputs and to ensure asymptotic stability in a closed loop. Several mathematical artifacts are used to deduce stability conditions expressed in terms of linear matrix inequalities. To show high performances of the proposed technique, a relevant example is provided with comparisons to recent results.

New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.

A dynamic time warped clustering technique for discrete event simulation-based system analysis

Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.

Discrete Wirtinger-based inequality and its application

In this paper, we derive a new inequality, which encompasses the discrete Jensen inequality. The new inequality is applied to analyze stability of linear discrete systems with an interval time-varying delay and a less conservative stability condition is obtained. Two numerical examples are given to show the effectiveness of the obtained stability condition.

Discrete inequalities based on multiple auxiliary functions and their applications to stability analysis of time-delay systems

Abstract
This paper presents new discrete inequalities for single summation and double summation. These inequalities are based on multiple auxiliary functions and include the Jensen discrete inequality and the discrete Wirtinger-based inequality as special cases. An application of these discrete inequalities to analyse stability of linear discrete systems with an interval time-varying delay is studied and a less conservative stability condition is obtained. Three numerical examples are given to show the effectiveness of the obtained stability condition.

Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol

INTRODUCTION: Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting. METHODS AND ANALYSIS: We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results. ETHICS AND DISSEMINATION: This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.

A discrete choice experiment to examine the preferences of patients with cancer and their willingness to pay for different types of health care appointments

BACKGROUND: This study sought to understand the preferences of patients with cancer and the trade-offs between appointment attributes using discrete choice experiment (DCE). METHODS AND STUDY DESIGN: Patients with cancer at 3 hospitals completed a self-administered DCE. Each scenario described 6 attributes: expertise of health care professionals (HCPs), familiarity of doctors with patients' medical history, waiting time, accompaniment by family/friends, travel time, and out-of-pocket costs. Patient preferences were estimated using logistic regression. Willingness to pay (WTP) estimates were derived from regression coefficients. RESULTS: Of 512 patients contacted, 185 returned the questionnaire. The mean age was 61 years, and 60% of respondents were female. The mean time since cancer diagnosis was 34 months, 90% had received treatment; and 61% had early-stage disease. The most important attributes were expertise and familiarity of doctors with patients' medical history; distance traveled was least likely to influence patient preferences. The WTP analysis estimated that patients were willing to pay $680 (95% CI, 470-891) for an appointment with a specialist, $571 (95% CI, 388-754) for doctors familiar with their history, $422 (95% CI, 262-582) for shorter waiting times, $399 (95% CI, 249-549) to be accompanied by family/friends, and $301 (95% CI, 162-441) for shorter traveling times. Male patients had a stronger preference for accompaniment by family/friends. The expertise of HCP was the most important attribute for patients regardless of geographic remoteness. CONCLUSIONS: Our study can assist the development of patient-centered health care models that improve patient access to experienced HCPs, support the role of primary care providers during the cancer journey, and educate patients about the roles of non-oncology HCPs to cope with increasing demand for cancer care.

Evaluation of tender solutions for aviation training using discrete event simulation and best performance criteria

This paper describes a novel discrete event simulation (DES) methodology for the evaluation of aviation training tenders where performance is measured against “best performance” criteria. The objective was to assess and compare multiple aviation training schedules and their resource allocation plans against predetermined training objectives. This research originated from the need to evaluate tender proposals for the Australian Defence Aviation Training School that is currently undergoing aviation training consolidation and helicopter rationalization. We show how DES is an ideal platform for evaluating resource plans and schedules, and discuss metric selection to objectively encapsulate performance and permit an unbiased comparison. DES allows feasibility studies for each tender proposal to assure they satisfy system and policy constraints. Consequently, to create an objective and fair environment to compare tendered solutions, what-if scenarios have been strategically examined to consider improved implementations of the proposed solutions.