12,443 research outputs found

    Field Tests of Kairomones to Increase Parasitism of Spruce Budworm (Lepidoptera: Tortricidae) Eggs by \u3ci\u3eTrichogramma\u3c/i\u3e Spp. (Hymenoptera: Trichogrammatidae)

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    Hexane extracts of spruce budworm, Choristoneura fumiferana, moth scales, applied at 0.04 moth-gram equivalents/branch and at 0.06 moth-gram equivalents/tree, failed to increase parasitism rates of Trichogramma spp. in two cutover spruce-fir stands in Maine. Releasing Maine-strain T. minutum apparently increased parasitism rates about 20-fold. However, application of kairomone extracts to whole branches and to upper crowns of small trees may have interfered with host-searching behaviors of Trichogramma parasitoids

    On-orbit operational scenarios, tools and techniques

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    This paper concentrates on methods and techniques used to develop operational scenarios for orbital missions, including development of models to analyze alternatives, modification of tools and refinement of techniques for future missions. Many of these tools and techniques have been derived from previous tools, techniques and experience from the Orbital Maneuvering Vehicle (OMV) program. Results from use of these tools show the current Cargo Transfer Vehicle nominal mission scenario, with 95 discrete events defined for the CTV mission from the NLS Heavy Lift Launch Vehicle (HLLV) to Space Station Freedom (SSF)

    Learning users' interests by quality classification in market-based recommender systems

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    Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

    Designing an Adaptive Interface: Using Eye Tracking to Classify How Information Usage Changes Over Time in Partially Automated Vehicles

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    While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during ‘steady state’ operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles

    Consensus Acceleration in Multiagent Systems with the Chebyshev Semi-Iterative Method

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    We consider the fundamental problem of reaching consensus in multiagent systems; an operation required in many applications such as, among others, vehicle formation and coordination, shape formation in modular robotics, distributed target tracking, and environmental modeling. To date, the consensus problem (the problem where agents have to agree on their reported values) has been typically solved with iterative decentralized algorithms based on graph Laplacians. However, the convergence of these existing consensus algorithms is often too slow for many important multiagent applications, and thus they are increasingly being combined with acceleration methods. Unfortunately, state-of-the-art acceleration techniques require parameters that can be optimally selected only if complete information about the network topology is available, which is rarely the case in practice. We address this limitation by deriving two novel acceleration methods that can deliver good performance even if little information about the network is available. The first proposed algorithm is based on the Chebyshev semi-iterative method and is optimal in a well defined sense; it maximizes the worst-case convergence speed (in the mean sense) given that only rough bounds on the extremal eigenvalues of the network matrix are available. It can be applied to systems where agents use unreliable communication links, and its computational complexity is similar to those of simple Laplacian-based methods. This algorithm requires synchronization among agents, so we also propose an asynchronous version that approximates the output of the synchronous algorithm. Mathematical analysis and numerical simulations show that the convergence speed of the proposed acceleration methods decrease gracefully in scenarios where the sole use of Laplacian-based methods is known to be impractical

    Using fractals and power laws to predict the location of mineral deposits

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    Around the world the mineral exploration industry is interested in getting that small increase in probability measure on the earth's surface of where the next large undiscovered deposit might be found. In particular WMC Resources Ltd has operations world wide looking for just that edge in the detection of very large deposits of, for example, gold. Since the pioneering work of Mandelbrot, geologists have been familiar with the concept of fractals and self similarity over a few orders of magnitude for geological features. This includes the location and size of deposits within a particular mineral province. Fractal dimensions have been computed for such provinces and similarities of these aggregated measures between provinces have been noted. This paper explores the possibility of making use of known information to attempt the inverse process. That is, from lesser dimensional measures of a mineral province, for example, fractal dimension or more generally multi-fractal measures, is it possible to infer, even with small increase in probability, where the unknown (preferably large) deposits might be located

    A SEQUENTIAL CHOICE MODEL OF RECREATION BEHAVIOR

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    The travel cost model is the standard model used in the recreation demand literature. This model assumes that the decision on the number of trips to a particular site in a given period (a season, for example) is determined at the beginning of the period. For certain types of recreation activity, this decision may be more appropriately modeled as a sequential process, in which the decision of whether or not to take each additional trip is made after previous trips have occurred. This decision is dependent on the realization of random variables on previous trips as well as travel costs. A model is developed in which the choice of a discrete number of sequentially chosen trips to a given site is specified as function of site-specific variables and variables realized on previous trips. This models advantage over the traditional travel cost model is that it specifies discrete, nonnegative integer values for the number of trips and allows intraseasonal effects to determine the probability of taking each additional trip.Resource /Energy Economics and Policy,

    TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

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    In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate

    Predation by Amphibians and Small Mammals on the Spruce Budworm (Lepidoptera: Tortricidae)

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    Stomach-content analyses of pitfall-trapped amphibians and small mammals showed that the eastern American toad, Bujo americanus americanus, and the wood frog, Rana sylvatica, preyed on late instars and moths of the spruce budworm, Choristoneura fumiferana. The spotted salamander, Ambystoma maculatum, and the masked shrew, Sorex cinereus, also preyed on late instars of the spruce budworm
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