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