Analysis of wake vortex flight test data behind a T-33 aircraft

Measurements of the vortex system behind a T-33 aircraft were obtained by a Learjet equipped with a boom carrying a three-wire, hot-wire anemometry probe and other instrumentation. Analysis of the measurements using a computerized geometric method indicated the vortices had a core radius of approximately 0.11 meter with a maximum velocity of 25 meters per second. The hot-wire anemometer was found to be a practical and sensitive instrument for determining in-flight vortex velocities. No longitudinal instabilities, buoyant effects or vortex breakdowns were evident in the data which included vortex wake cross sections from 0.24 to 5.22 kilometers behind the T-33

Tight local approximation results for max-min linear programs

In a bipartite max-min LP, we are given a bipartite graph \myG = (V \cup I \cup K, E), where each agent $v \in V$ is adjacent to exactly one constraint $i \in I$ and exactly one objective $k \in K$. Each agent $v$ controls a variable $x_v$. For each $i \in I$ we have a nonnegative linear constraint on the variables of adjacent agents. For each $k \in K$ we have a nonnegative linear objective function of the variables of adjacent agents. The task is to maximise the minimum of the objective functions. We study local algorithms where each agent $v$ must choose $x_v$ based on input within its constant-radius neighbourhood in \myG. We show that for every $\epsilon>0$ there exists a local algorithm achieving the approximation ratio ${\Delta_I (1 - 1/\Delta_K)} + \epsilon$. We also show that this result is the best possible -- no local algorithm can achieve the approximation ratio ${\Delta_I (1 - 1/\Delta_K)}$. Here $\Delta_I$ is the maximum degree of a vertex $i \in I$, and $\Delta_K$ is the maximum degree of a vertex $k \in K$. As a methodological contribution, we introduce the technique of graph unfolding for the design of local approximation algorithms.Comment: 16 page

Gaia Stellar Kinematics in the Head of the Orion A Cloud: Runaway Stellar Groups and Gravitational Infall

This work extends previous kinematic studies of young stars in the Head of the Orion A cloud (OMC-1/2/3/4/5). It is based on large samples of infrared, optical, and X-ray selected pre-main sequence stars with reliable radial velocities and Gaia-derived parallaxes and proper motions. Stellar kinematic groups are identified assuming they mimic the motion of their parental gas. Several groups are found to have peculiar kinematics: the NGC 1977 cluster and two stellar groups in the Extended Orion Nebula (EON) cavity are caught in the act of departing their birthplaces. The abnormal motion of NGC 1977 may have been caused by a global hierarchical cloud collapse, feedback by massive Ori OB1ab stars, supersonic turbulence, cloud-cloud collision, and/or slingshot effect; the former two models are favored by us. EON groups might have inherited anomalous motions of their parental cloudlets due to small-scale `rocket effects' from nearby OB stars. We also identify sparse stellar groups to the east and west of Orion A that are drifting from the central region, possibly a slowly expanding halo of the Orion Nebula Cluster. We confirm previously reported findings of varying line-of-sight distances to different parts of the cloud's Head with associated differences in gas velocity. Three-dimensional movies of star kinematics show contraction of the groups of stars in OMC-1 and global contraction of OMC-123 stars. Overall, the Head of Orion A region exhibits complex motions consistent with theoretical models involving hierarchical gravitational collapse in (possibly turbulent) clouds with OB stellar feedback.Comment: Accepted for publication in MNRAS. 26 pages, 13 figures. The two 3-D stellar kinematic movies, aimed as Supplementary Materials, can be found on YouTube at: https://youtu.be/B4GHCVvCYfo (`restricted' sample) and https://youtu.be/6fUu8sP0QFI (`full' sample

Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting

In the Internet of Things (IoT) domain, various heterogeneous ubiquitous devices would be able to connect and communicate with each other seamlessly, irrespective of the domain. Semantic representation of data through detailed standardized annotation has shown to improve the integration of the interconnected heterogeneous devices. However, the semantic representation of these heterogeneous data sources for environmental monitoring systems is not yet well supported. To achieve the maximum benefits of IoT for drought forecasting, a dedicated semantic middleware solution is required. This research proposes a middleware that semantically represents and integrates heterogeneous data sources with indigenous knowledge based on a unified ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the 16th International Middleware Conference (Middleware Doct Symposium 2015), Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US