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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.Item type: Publication , Access status: Metadata only , ‘I think it's wrong, but it helps'–a focused ethnography of benzodiazepine practices in specialist palliative care(2025) Collier, Aileen; Chapman, Michael; Hosie, AnnmarieBenzodiazepines are a class of drug extensively used in palliative care. Their use has predominantly been studied within a biomedical framework. Our study instead focused on the sociocultural aspects of benzodiazepine practices. We aimed to explore clinicians, patients and family members’ values, beliefs, knowledge and feelings regarding use of benzodiazepines, including in-situ clinical decision-making processes, affects and actions. Social theory understandings of affect, the body and of suffering provided the theoretical lens through which data were analysed. Analysis generated the following themes: (1) A special specialty; (2) The suffering body and the moral imperative to ‘settle; (3) The liminal body–living and dying; and (4) Organizational realities. Use of benzodiazepines were largely governed by social and moral norms, cultural expectations and organizational realities as part of affective assemblages of care.Item type: Publication , Access status: Metadata only , Effect of Failure-Inducing Cyclic Loading on Pile Capacity in Sand(2026-01-01) Lv, Yaru; Hu, Jieming; Lehane, Barry M.; Zuo, DianjunAlthough the behavior of pile foundations under cyclic loading is critical in offshore engineering, the underlying mechanisms governing load transfer and the bearing capacity of piles at the ultimate limit state in calcareous sand (a problematic ocean soil) subjected to cyclic compression and uplift remain poorly understood. This study employs six centrifuge tests on single piles subjected to monotonic compression loading, monotonic uplift loading, and cyclic vertical loading in dry calcareous sand and silica sand. The results reveal a transition in the pile failure mode from progressive failure under monotonic loading to abrupt failure after ultimate cyclic loading. Cyclic loading reduces the ultimate uplift bearing capacity of the pile by approximately two-thirds, while enhancing the ultimate compressive bearing capacities of the pile by 143% in calcareous sand and 6% in silica sand, respectively. These trends are corroborated by the energy evolution patterns, where uplifting energy decreases with increasing cycles, while compressive energy exhibits an initial decrease followed by an increase. Such variations are attributed to cyclic compressive and uplift loading, which degrade skin friction on the pile shaft while improving the bearing performance of the sand beneath the pile tip. This result suggests that the end resistance of the pile in calcareous sand should be a focus of attention and can be reinforced by cyclic preload of the pile.