Clinical factors and technological barriers as determinants for the intention to use wireless handheld technology in healthcare environment: an Indian case study

Abstract]: Traditional technology adoption models identified ‘ease of use’ and ‘usefulness’ as the dominating factors for technology adoption. However, recent studies in healthcare have established that these two factors are not always reliable on their own and other factors may influence technology adoption. To establish the identity of these additional factors, a mixed method approach was used and data were collected through interviews and a survey. The survey instrument was specifically developed for this study so that it is relevant to the Indian healthcare setting. We identified clinical management and technological barriers as the dominant factors influencing the wireless handheld technology adoption in the Indian healthcare environment. The results of this study showed that new technology models will benefit by considering the clinical influences of wireless handheld technology, in addition to known factors. The scope of this study is restricted to wireless handheld devices such as PDAs, smart phones, and handheld PCs Gururajan, Raj and Hafeez-Baig, Abdul and Gururajan, Vijaya

Managing knowledge for enterprise systems in indian organizations: Case study insights

Many Enterprise Systems (ES) projects have reported nil or detrimental impacts despite the substantial investment in the system. Having expected positive outcomes for the organization and its functions through the weighty spend, the effective management of ES-related knowledge has been suggested as a critical success factor for these ES projects in ES implementations. This paper suggests theoretical views purporting the importance of understanding on knowledge management for ES success. To explain the complex, dynamic and multifaceted of knowledge management, we adopt the concepts in Learning Network Theory. We then conceptualized the impact of knowledge management on ES by analyzing five case studies in several industries in India, based on the Knowledge-based Theory of the Firm that captures the performance of the system.

Robust designs for Poisson regression models

We consider the problem of how to construct robust designs for Poisson regression models. An analytical expression is derived for robust designs for first-order Poisson regression models where uncertainty exists in the prior parameter estimates. Given certain constraints in the methodology, it may be necessary to extend the robust designs for implementation in practical experiments. With these extensions, our methodology constructs designs which perform similarly, in terms of estimation, to current techniques, and offers the solution in a more timely manner. We further apply this analytic result to cases where uncertainty exists in the linear predictor. The application of this methodology to practical design problems such as screening experiments is explored. Given the minimal prior knowledge that is usually available when conducting such experiments, it is recommended to derive designs robust across a variety of systems. However, incorporating such uncertainty into the design process can be a computationally intense exercise. Hence, our analytic approach is explored as an alternative.

Contribution of take-out food consumption to socioeconomic differences in fruit and vegetable intake : a mediation analysis

Lower fruit and vegetable intake among socioeconomically disadvantaged groups has been well documented, and may be a consequence of a higher consumption of take-out foods. This study examined whether, and to what extent, take-out food consumption mediated (explained) the association between socioeconomic position and fruit and vegetable intake. A cross-sectional postal survey was conducted among 1500 randomly selected adults aged 25–64 years in Brisbane, Australia in 2009 (response rate = 63.7%, N = 903). A food frequency questionnaire assessed usual daily servings of fruits and vegetables (0 to 6), overall take-out consumption (times/week) and the consumption of 22 specific take-out items (never to ≥once/day). These specific take-out items were grouped into “less healthy” and “healthy” choices and indices were created for each type of choice (0 to 100). Socioeconomic position was ascertained by education. The analyses were performed using linear regression, and a bootstrap re-sampling approach estimated the statistical significance of the mediated effects. Mean daily serves of fruits and vegetables was 1.89 (SD 1.05) and 2.47 (SD 1.12) respectively. The least educated group were more likely to consume fewer serves of fruit (B= –0.39, p<0.001) and vegetables (B= –0.43, p<0.001) compared with the highest educated. The consumption of “less healthy” take-out food partly explained (mediated) education differences in fruit and vegetable intake; however, no mediating effects were observed for overall and “healthy” take-out consumption. Regular consumption of “less healthy” take-out items may contribute to socioeconomic differences in fruit and vegetable intake, possibly by displacing these foods.

Investigation of methods for increasing the energy efficiency on unmanned aerial vehicles (UAVS)

In recent years, development of Unmanned Aerial Vehicles (UAV) has become a significant growing segment of the global aviation industry. These vehicles are developed with the intention of operating in regions where the presence of onboard human pilots is either too risky or unnecessary. Their popularity with both the military and civilian sectors have seen the use of UAVs in a diverse range of applications, from reconnaissance and surveillance tasks for the military, to civilian uses such as aid relief and monitoring tasks. Efficient energy utilisation on an UAV is essential to its functioning, often to achieve the operational goals of range, endurance and other specific mission requirements. Due to the limitations of the space available and the mass budget on the UAV, it is often a delicate balance between the onboard energy available (i.e. fuel) and achieving the operational goals. This thesis presents an investigation of methods for increasing the energy efficiency on UAVs. One method is via the development of a Mission Waypoint Optimisation (MWO) procedure for a small fixed-wing UAV, focusing on improving the onboard fuel economy. MWO deals with a pre-specified set of waypoints by modifying the given waypoints within certain limits to achieve its optimisation objectives of minimising/maximising specific parameters. A simulation model of a UAV was developed in the MATLAB Simulink environment, utilising the AeroSim Blockset and the in-built Aerosonde UAV block and its parameters. This simulation model was separately integrated with a multi-objective Evolutionary Algorithm (MOEA) optimiser and a Sequential Quadratic Programming (SQP) solver to perform single-objective and multi-objective optimisation procedures of a set of real-world waypoints in order to minimise the onboard fuel consumption. The results of both procedures show potential in reducing fuel consumption on a UAV in a ight mission. Additionally, a parallel Hybrid-Electric Propulsion System (HEPS) on a small fixedwing UAV incorporating an Ideal Operating Line (IOL) control strategy was developed. An IOL analysis of an Aerosonde engine was performed, and the most efficient (i.e. provides greatest torque output at the least fuel consumption) points of operation for this engine was determined. Simulation models of the components in a HEPS were designed and constructed in the MATLAB Simulink environment. It was demonstrated through simulation that an UAV with the current HEPS configuration was capable of achieving a fuel saving of 6.5%, compared to the ICE-only configuration. These components form the basis for the development of a complete simulation model of a Hybrid-Electric UAV (HEUAV).

Cost efficiency v quality service : using CHAT as a tool for understanding change and transformation in work practices

This chapter provides an analysis of feedback from key stakeholders, collected as part of a research project, on the problems and tensions evident in the collective work practices of learning advisers employed in learning assistance services at an Australian metropolitan university (Peach, 2003). The term 'learning assistance' is used in the Australian higher education sector generally to refer to student support services that include assistance with academic writing and other study skills. The aim of the study was to help learning advisers and other key stakeholders develop a better understanding of the work activity with a view to using this understanding to generate improvements in service provision. Over twenty problems and associated tensions were identified through stakeholder feedback however the focus of this chapter is the analysis of tensions related to a cluster of problems referred to as cost-efficiency versus quality service. Theoretical modelling derived from the tools made available through cultural historical activity theory and expansive visibilsation (Engestrom and Miettinen, 1999) and excerpts from data are used to illustrate how different understandings of the purpose of learning assistance services impacts on the work practices of learning advisers and creates problems and tensions in relation to the type of service available (including use of technology),level of service available, and learning adviser workload.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

Application of operations research techniques to improve efficiency in the emergency department

The main aim of this thesis is to analyse and optimise a public hospital Emergency Department. The Emergency Department (ED) is a complex system with limited resources and a high demand for these resources. Adding to the complexity is the stochastic nature of almost every element and characteristic in the ED. The interaction with other functional areas also complicates the system as these areas have a huge impact on the ED and the ED is powerless to change them. Therefore it is imperative that OR be applied to the ED to improve the performance within the constraints of the system. The main characteristics of the system to optimise included tardiness, adherence to waiting time targets, access block and length of stay. A validated and verified simulation model was built to model the real life system. This enabled detailed analysis of resources and flow without disruption to the actual ED. A wide range of different policies for the ED and a variety of resources were able to be investigated. Of particular interest was the number and type of beds in the ED and also the shift times of physicians. One point worth noting was that neither of these resources work in isolation and for optimisation of the system both resources need to be investigated in tandem. The ED was likened to a flow shop scheduling problem with the patients and beds being synonymous with the jobs and machines typically found in manufacturing problems. This enabled an analytic scheduling approach. Constructive heuristics were developed to reactively schedule the system in real time and these were able to improve the performance of the system. Metaheuristics that optimised the system were also developed and analysed. An innovative hybrid Simulated Annealing and Tabu Search algorithm was developed that out-performed both simulated annealing and tabu search algorithms by combining some of their features. The new algorithm achieves a more optimal solution and does so in a shorter time.

Energy efficiency and sustainability in residential property

The issue of a more sustainable environment has been the aim of many governments and institutions for decades. Current research and literature has shown the continuing impact of global development and population increases on the planet as a whole. Issues such as carbon emissions, global warming, resource sustainability, industrial pollution, waste management and the decline in scarce resources, including food, are now realities and are being addressed at various levels. All levels of government, business and the public now equally share responsibility for the continued sustainable environment in general. Although these issues of global warming, climate change and the overuse of scarce resources are well documented, and constantly covered in all media forms, public attitudes to these issues vary significantly. Despite being aware of these issues many individuals consider that the problem is one for governments to tackle and that their individual efforts are not important or necessary. In many cases individuals are concerned with sustainability, but are either not in the position to take action due to economic circumstances or are not prepared to offset sustainability gains with personal interests...

Handling incomplete data in survival analysis with multiple covariates

This paper studies the missing covariate problem which is often encountered in survival analysis. Three covariate imputation methods are employed in the study, and the effectiveness of each method is evaluated within the hazard prediction framework. Data from a typical engineering asset is used in the case study. Covariate values in some time steps are deliberately discarded to generate an incomplete covariate set. It is found that although the mean imputation method is simpler than others for solving missing covariate problems, the results calculated by it can differ largely from the real values of the missing covariates. This study also shows that in general, results obtained from the regression method are more accurate than those of the mean imputation method but at the cost of a higher computational expensive. Gaussian Mixture Model (GMM) method is found to be the most effective method within these three in terms of both computation efficiency and predication accuracy.

Air Conditioning Systems : Design and Energy efficiency

Air conditioning systems have become an integral part of many modern buildings. Proper design and operation of air conditioning systems have significant impact not only on the energy use and greenhouse gas emissions from the buildings, but also on the thermal comfort and productivity of the occupants. In this paper, the purpose and need of installing air conditioning systems is first introduced. The methods used for the classification of air conditioning systems are then presented. This is followed by a discussion on the pros and cons of each type of the air conditioning systems, including both common and new air conditioning technologies. The procedures used to design air conditioning systems are also outlined, and the implications of air conditioning systems, including design, selection, operation and maintenance, on building energy efficiency is also discussed.

Reduced sampling efficiency causes degraded Vernier hyperacuity with normal aging: Vernier acuity in position noise

Vernier acuity, a form of visual hyperacuity, is amongst the most precise forms of spatial vision. Under optimal conditions Vernier thresholds are much finer than the inter-photoreceptor distance. Achievement of such high precision is based substantially on cortical computations, most likely in the primary visual cortex. Using stimuli with added positional noise, we show that Vernier processing is reduced with advancing age across a wide range of noise levels. Using an ideal observer model, we are able to characterize the mechanisms underlying age-related loss, and show that the reduction in Vernier acuity can be mainly attributed to the reduction in efficiency of sampling, with no significant change in the level of internal position noise, or spatial distortion, in the visual system.

Taxation of multinational banks : using formulary apportionment to reflect economic reality (Part 1)

Formulary apportionment does not attempt to undertake a transactional division of a highly integrated multinational entity. Rather, it allocates income to the jurisdictions based on an economically justifiable formula. Opposition to formulary apportionment is generally based on the argument that it is not a theoretically superior (or optimal) model because of the implementation difficulties. The conclusion that the unitary taxation model may be theoretically superior to the current arm's-length model that applies to multinational banks, despite significant implementation, compliance, and enforcement issues, is based on the unitary taxation model providing greater alignment with the unique features of these banks. The formulary apportionment model looks to the economic substance of the multinational entity and, in this sense, adopts a substance-over- form approach. Formulary apportionment further recognizes the impossibility of using arm's-length pricing for economically interdependent multinational entities. A final advantage to formulary apportionment, which is also a consequence of this model achieving greater inter-nation equity, is the elimination of double taxation.