Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.

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In Wireless Sensor Networks (WSNs), the users' objective is to extract useful global information by collecting individual sensor readings. Conventionally, this is done using in-network aggregation on a spanning tree from sensors to data sink. However, the spanning tree structure is not robust against communication errors; when a packet is lost, so is a complete subtree of values. Multipath routing can mask some of these errors, but on the other hand, may aggregate individual sensor values multiple times. This may produce erroneous results when dealing with duplicate-sensitive aggregates, such as SUM, COUNT, and AVERAGE. In this paper, we present and analyze two new fault tolerant schemes for duplicate-sensitive aggregation in WSNs: (1) Cascaded RideSharing and (2) Diffused RideSharing. These schemes use the available path redundancy in the WSN to deliver a correct aggregate result to the data sink. Compared to state-of-the-art, our schemes deliver results with lower root mean square (RMS) error and consume much less energy and bandwidth. RideSharing can consume as much as 50% less resources than hash-based schemes, such as SKETCHES and Synopsis Diffusion, while achieving lower RMS for reasonable link error rates.

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Denial-of-Service (DoS) attacks remain a challenging problem in the Internet. By making resources unavailable to intended legitimate clients, DoS attacks have resulted in significant loss of time and money for many organizations, thus, many DoS defense mechanisms have been proposed.In this paper we propose live baiting, a novel approach for detecting the identities of DoS attackers. Live baiting leverages group-testing theory, which aims at discovering defective members in a population using the minimum number of "tests". This leverage allows live baiting to detect attackers using low state overhead without requiring models of legitimate requests nor anomalous behavior. The amount of state needed by live baiting is in the order of number of attackers not number of clients. This saving allows live baiting to scale to large services with millions of clients. We analyzed the coverage, effectiveness (detection time, false positive and false negative probabilities), and efficiency (memory, message overhead, and computational complexity) of our approach. We validated our analysis using NS-2 simulations modeled after real Web traces.

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