1,701 research outputs found
2D Proactive Uplink Resource Allocation Algorithm for Event Based MTC Applications
We propose a two dimension (2D) proactive uplink resource allocation
(2D-PURA) algorithm that aims to reduce the delay/latency in event-based
machine-type communications (MTC) applications. Specifically, when an event of
interest occurs at a device, it tends to spread to the neighboring devices.
Consequently, when a device has data to send to the base station (BS), its
neighbors later are highly likely to transmit. Thus, we propose to cluster
devices in the neighborhood around the event, also referred to as the
disturbance region, into rings based on the distance from the original event.
To reduce the uplink latency, we then proactively allocate resources for these
rings. To evaluate the proposed algorithm, we analytically derive the mean
uplink delay, the proportion of resource conservation due to successful
allocations, and the proportion of uplink resource wastage due to unsuccessful
allocations for 2D-PURA algorithm. Numerical results demonstrate that the
proposed method can save over 16.5 and 27 percent of mean uplink delay,
compared with the 1D algorithm and the standard method, respectively.Comment: 6 pages, 6 figures, Published in 2018 IEEE Wireless Communications
and Networking Conference (WCNC
Timing of antiretroviral therapy in Cambodian hospital after diagnosis of tuberculosis: impact of revised WHO guidelines
OBJECTIVE: To determine if implementation of 2010 World Health Organization (WHO) guidelines on antiretroviral therapy (ART) initiation reduced delay from tuberculosis diagnosis to initiation of ART in a Cambodian urban hospital. METHODS: A retrospective cohort study was conducted in a nongovernmental hospital in Phnom Penh that followed new WHO guidelines in patients with human immunodeficiency virus (HIV) and tuberculosis. All ART-naïve, HIV-positive patients initiated on antituberculosis treatment over the 18 months before and after guideline implementation were included. A competing risk regression model was used. FINDINGS: After implementation of the 2010 WHO guidelines, 190 HIV-positive patients with tuberculosis were identified: 53% males; median age, 38 years; median baseline CD4+ T-lymphocyte (CD4+ cell) count, 43 cells/µL. Before implementation, 262 patients were identified; 56% males; median age, 36 years; median baseline CD4+ cell count, 59 cells/µL. With baseline CD4+ cell counts ≤ 50 cells/µL, median delay to ART declined from 5.8 weeks (interquartile range, IQR: 3.7–9.0) before to 3.0 weeks (IQR: 2.1–4.4) after implementation (P < 0.001); with baseline CD4+ cell counts > 50 cells/µL, delay dropped from 7.0 (IQR: 5.3–11.3) to 3.6 (IQR: 2.9–5.3) weeks (P < 0.001). The probability of ART initiation within 4 and 8 weeks after tuberculosis diagnosis rose from 23% and 65%, respectively, before implementation, to 62% and 90% after implementation. A non-significant increase in 6-month retention and antiretroviral substitution was seen after implementation. CONCLUSION: Implementation of 2010 WHO recommendations in a routine clinical setting shortens delay to ART. Larger studies with longer follow-up are needed to assess impact on patient outcomes
Finding Community Structure with Performance Guarantees in Complex Networks
Many networks including social networks, computer networks, and biological
networks are found to divide naturally into communities of densely connected
individuals. Finding community structure is one of fundamental problems in
network science. Since Newman's suggestion of using \emph{modularity} as a
measure to qualify the goodness of community structures, many efficient methods
to maximize modularity have been proposed but without a guarantee of
optimality. In this paper, we propose two polynomial-time algorithms to the
modularity maximization problem with theoretical performance guarantees. The
first algorithm comes with a \emph{priori guarantee} that the modularity of
found community structure is within a constant factor of the optimal modularity
when the network has the power-law degree distribution. Despite being mainly of
theoretical interest, to our best knowledge, this is the first approximation
algorithm for finding community structure in networks. In our second algorithm,
we propose a \emph{sparse metric}, a substantially faster linear programming
method for maximizing modularity and apply a rounding technique based on this
sparse metric with a \emph{posteriori approximation guarantee}. Our experiments
show that the rounding algorithm returns the optimal solutions in most cases
and are very scalable, that is, it can run on a network of a few thousand nodes
whereas the LP solution in the literature only ran on a network of at most 235
nodes
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