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Item type: Publication , Access status: Metadata only , Infrastructure or foreign direct investment?. An examination of the implications of MNE strategy for economic development(2009) Yamin, Mo; Sinkovics, Rudolf R.This paper explores the paradoxical relationship between MNE current strategies and economic development. There is evidence that positive developmental impacts of FDI flows are conditional on high levels of human capital and thus on the existence of 'good' infrastructure in recipient countries. In this paper we suggest that current MNE strategies have a negative impact on the development of infrastructure in LDCs. The justification for this argument arises from the low developmental impact of current FDI patterns and from rising costs of attracting increasingly footloose investment. The overall effect is to aggravate government financial constraints on the development of basic infrastructure. We develop propositions for future empirical research. We also consider implications for MNE strategy and argue that current MNE strategies are not only ineffective for delivering poverty reduction but that current strategies in developing countries do not necessarily serve the interest of MNEs either.Item type: Publication , Access status: Metadata only , Stakeholder's consensus on activities to be delivered by the facilitators in a planned iSupport program in Australia: A mixed-methods study(2025-07-01) Yu, Ying; Hunter, Sarah C.; Kitson, Alison; Xiao, Lily; Meyer, Claudia; Chapman, Michael; Tan, Kai Ping; Chen, Langduo; Mckechnie, Sue; Ratcliffe, Julie; Ullah, Shahid; Andrade, Andre Q.; Whitehead, CraigThis study used an explanatory mixed-methods design utilising a modified nominal group technique to reach a consensus on activities to be delivered by the iSupport Program Facilitators in the Australian community through engagement with stakeholders. The study involved 45 health and social care professionals and 30 family carers of people living with dementia from two community aged care organisations and two tertiary hospitals in Australia between September 2021 and March 2022. Quantitative and qualitative data were collected through pre-workshop surveys, workshops or interviews, and post-workshop surveys. Descriptive statistics were used to analyse the survey data. Thematic analysis was used to analyse data collected from survey comments, workshops and interviews. Stakeholders agreed on 16 activities relevant to Australian dementia care, including 1) activities to reduce carer-recipient-related stressors, 2) activities to reduce carer-related stressors, and 3) activities to reduce system-related stressors.Item type: Publication , Access status: Metadata only , Sensor discovery and configuration framework for the Internet of Things paradigm(2014) Perera, Charith; Jayaraman, Prem Prakash; Zaslavsky, Arkady; Georgakopoulos, Dimitrios; Christen, PeterInternet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. The data generated by the Internet of Things are valuable and have the potential to drive innovative and novel applications. The data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. One of the most challenging tasks before collecting and processing data from these devices (e.g. sensors) is discovering and configuring the sensors and the associated data streams. In this paper, we propose a tool called SmartLink that can be used to discover and configure sensors. Specifically, SmartLink, is capable of discovering sensors deployed in a particular location despite their heterogeneity (e.g. different communication protocols, communication sequences, capabilities). SmartLink establishes the direct communication between the sensor hardware and cloud-based IoT middleware. We address the challenge of heterogeneity using a plugin architecture. Our prototype tool is developed on the Android platform. We evaluate the significance of our approach by discovering and configuring 52 different types of Libelium sensors.Item type: Publication , Access status: Metadata only , The role of turbulence, magnetic fields and feedback for star formation(2016-05-31) Federrath, ChristophStar formation is inefficient. Only a few percent of the available gas in molecular clouds forms stars, leading to the observed low star formation rate (SFR). The same holds when averaged over many molecular clouds, such that the SFR of whole galaxies is again surprisingly low. Indeed, considering the low temperatures, molecular clouds should be highly gravitationally unstable and collapse on their global mean freefall timescale. And yet, they are observed to live about 10-100 times longer, i.e., the SFR per freefall time (SFRff) is only a few percent. Thus, other physical mechanisms must provide support against quick global collapse. Magnetic fields, turbulence and stellar feedback have been proposed as stabilising agents controlling star formation, but it is still unclear which of these processes is the most important and what their relative contributions are. Here I present high-resolution, adaptive- mesh-refinement simulations of star cluster formation that include turbulence, magnetic fields, and protostellar jet/outflow feedback. These simulations produce nearly realistic star formation rates consistent with observations, but only if turbulence, magnetic fields and feedback are included simultaneously.Item type: Publication , Access status: Metadata only , Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System(2023) Basahel, Sarah B.; Bajaba, Saleh; Yamin, Mohammad; Mohanty, Sachi Nandan; Lydia, E. LaxmiThe current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT, AI, smartphones, wearables, and so on making it possible to design fall detection (FD) systems for smart home care. This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems (TWODL-FDSHS). The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system. Initially, the presented TWODL-FDSHS technique exploits IoT devices for the data collection process. Next, the TWODL-FDSHS technique applies the TWO with Capsule Network (CapsNet) model for feature extraction. At last, a deep random vector functional link network (DRVFLN) with an Adam optimizer is exploited for fall event detection. A wide range of simulations took place to exhibit the enhanced performance of the presented TWODL-FDSHS technique. The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30% on the URFD dataset.