43,433 research outputs found

    The intention to use mobile digital library technology: A focus group study in the United Arab Emirates

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    IGI Global (“IGI”) granted Brunel University London the permission to archive this article in BURA (http://bura.brunel.ac.uk).This paper presents a qualitative study on student adoption of mobile library technology in a developing world context. The findings support the applicability of a number of existing constructs from the technology acceptance literature, such as perceived ease of use, social influence and trust. However, they also suggest the need to modify some adoption factors previously found in the literature to fit the specific context of mobile library adoption. Perceived value was found to be a more relevant overarching adoption factor than perceived usefulness for this context. Facilitating conditions were identified as important but these differed somewhat from those covered in earlier literature. The research also uncovered the importance of trialability for this type of application. The findings provide a basis for improving theory in the area of mobile library adoption and suggest a number of practical design recommendations to help designers of mobile library technology to create applications that meet user needs

    Foundations of coverage algorithms in autonomic mobile sensor networks

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    Drones are poised to become a prominent focus of advances in the near future as hardware platforms manufactured via mass production become accessible to consumers in higher quantities at lower costs than ever before. As more ways to utilize such devices become more popular, algorithms for directing the activities of mobile sensors must expand in order to automate their work. This work explores algorithms used to direct the behavior of networks of autonomous mobile sensors, and in particular how such networks can operate to achieve coverage of a field using mobility. We focus special attention to the way limited mobility affects the performance (and other factors) of algorithms traditionally applied to area coverage and event detection problems. Strategies for maximizing event detection and minimizing detection delay as mobile sensors with limited mobility are explored in the first part of this work. Next we examine exploratory coverage, a new way of analyzing sensor coverage, concerned more with covering each part of the coverage field once, while minimizing mobility required to achieve this level of 1-coverage. This analysis is contained in the second part of this work. Extending the analysis of mobility, we next strive to explore the novel topic of disabled mobility in mobile sensors, and how algorithms might react to increase effectiveness given that some sensors have lost mobility while retaining other senses. This work analyzes algorithm effectiveness in light of disabled mobility, demonstrates how this particular failure mode impacts common coverage algorithms, and presents ways to adjust algorithms to mitigate performance losses. --Abstract, page iv

    Throughput optimization strategies for large-scale wireless LANs

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    Thanks to the active development of IEEE 802.11, the performance of wireless local area networks (WLANs) is improving by every new edition of the standard facilitating large enterprises to rely on Wi-Fi for more demanding applications. The limited number of channels in the unlicensed industrial scientific medical frequency band however is one of the key bottlenecks of Wi-Fi when scalability and robustness are points of concern. In this paper we propose two strategies for the optimization of throughput in wireless LANs: a heuristic derived from a theoretical model and a surrogate model based decision engine

    Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

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    Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

    Rehabilitation Therapy in Older Acute Heart Failure Patients (REHAB-HF) trial: Design and rationale.

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    BACKGROUND: Acute decompensated heart failure (ADHF) is a leading cause of hospitalization in older persons in the United States. Reduced physical function and frailty are major determinants of adverse outcomes in older patients with hospitalized ADHF. However, these are not addressed by current heart failure (HF) management strategies and there has been little study of exercise training in older, frail HF patients with recent ADHF. HYPOTHESIS: Targeting physical frailty with a multi-domain structured physical rehabilitation intervention will improve physical function and reduce adverse outcomes among older patients experiencing a HF hospitalization. STUDY DESIGN: REHAB-HF is a multi-center clinical trial in which 360 patients ≥60 years hospitalized with ADHF will be randomized either to a novel 12-week multi-domain physical rehabilitation intervention or to attention control. The goal of the intervention is to improve balance, mobility, strength and endurance utilizing reproducible, targeted exercises administered by a multi-disciplinary team with specific milestones for progression. The primary study aim is to assess the efficacy of the REHAB-HF intervention on physical function measured by total Short Physical Performance Battery score. The secondary outcome is 6-month all-cause rehospitalization. Additional outcome measures include quality of life and costs. CONCLUSIONS: REHAB-HF is the first randomized trial of a physical function intervention in older patients with hospitalized ADHF designed to determine if addressing deficits in balance, mobility, strength and endurance improves physical function and reduces rehospitalizations. It will address key evidence gaps concerning the role of physical rehabilitation in the care of older patients, those with ADHF, frailty, and multiple comorbidities
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