Combining Plant Pathogenic Fungi and the Leaf-Mining Fly, Hydrellia pakistanae, Increases Damage to Hydrilla

Four fungal species, F71PJ Acremonium sp., F531 Cylindrocarpon sp., F542, Botrytis sp., and F964 Fusarium culmorum [Wm. G. Sm.] Sacc. were recovered from hydrilla [ Hydrilla verticillata (L. f.) Royle] shoots or from soil and water surrounding hydrilla growing in ponds and lakes in Florida and shown to be capable of killing hydrilla in a bioassay. The isolates were tested singly and in combination with the leaf-mining fly, Hydrellia pakistanae (Diptera: Ephydridae), for their capability to kill or severely damage hydrilla in a bioassay.

Mining Biodiversity Progress Report

Progress report from the Mining Biodiversity, Digging into Data Challenge round 3, project.

Measurements Of In Situ Stress And Mining-Induced Stress In Beiminghe Iron Mine Of China

In order to obtain the distribution rules of in situ stress and mining-induced stress of Beiminghe Iron Mine, the stress relief method by overcoring was used to measure the in situ stress, and the MC type bore-hole stress gauge was adopted to measure the mining-induced stress. In the in situ stress measuring, the technique of improved hollow inclusion cells was adopted, which can realize complete temperature compensation. Based on the measuring results, the distribution model of in situ stress was established and analyzed. The in situ stress measuring result shows that the maximum horizontal stress is 1.75-2.45 times of vertical stress and almost 1.83 times of the minimum horizontal stress in this mineral field. And the mining-induced stress measuring result shows that, according to the magnitude of front abutment pressure the stress region can be separated into stress-relaxed area, stress-concentrated area and initial stress area. At the -50 m mining level of this mine, the range of stress-relaxed area is 0-3 m before mining face; the range of stress-concentrated area is 3-55 m before mining face, and the maximum mining-induced stress is 16.5-17.5 MPa, which is 15-20 m from the mining face. The coefficient of stress concentration is 1.85.

Incremental Control Synthesis for Robotics in the Presence of Temporal Logic Specifications

This thesis presents methods for incrementally constructing controllers in the presence of uncertainty and nonlinear dynamics. The basic setting is motion planning subject to temporal logic specifications. Broadly, two categories of problems are treated. The first is reactive formal synthesis when so-called discrete abstractions are available. The fragment of linear-time temporal logic (LTL) known as GR(1) is used to express assumptions about an adversarial environment and requirements of the controller. Two problems of changes to a specification are posed that concern the two major aspects of GR(1): safety and liveness. Algorithms providing incremental updates to strategies are presented as solutions. In support of these, an annotation of strategies is developed that facilitates repeated modifications. A variety of properties are proven about it, including necessity of existence and sufficiency for a strategy to be winning. The second category of problems considered is non-reactive (open-loop) synthesis in the absence of a discrete abstraction. Instead, the presented stochastic optimization methods directly construct a control input sequence that achieves low cost and satisfies a LTL formula. Several relaxations are considered as heuristics to address the rarity of sampling trajectories that satisfy an LTL formula and demonstrated to improve convergence rates for Dubins car and single-integrators subject to a recurrence task.

Flight delay forecast due to weather using Data Mining

A Data Mining model that is able to predict if a flight is going to leave late due to a weather delay. It is used, to be able to get a later connection if you have a connecting flight.

Combination of web usage, content and structure information for diverse web mining applications in the tourism context and the context of users with disabilities

188 p.

Contributions to time series data mining departing from the problem of road travel time modeling

194 p.