17,764 research outputs found
Spruce Budworm and Other Lepidopterous Prey of Eumenid Wasps (Hymenoptera: Eumenidae) in Spruce-Fir Forests of Maine
Three species of eumenid wasps, Ancistrocerus adiabatus, Ancistrocerus antilope, and Euodynerus Ieucomelas, accepted and provisioned trap-nesting blocks with lepidopterous larvae, A pyralid. Nephopteryx sp., was the most commonly provisioned prey. A. adiabarus and E. leucomelas preyed on late instars of the spruce budworm, Choristoneura fumiferana: however. budworms accounted for only 6% of the provisioned prey. Estimates of budworm population densities before and after wasp predation, and subsample-s of provisioned prey indicated no significant reductions i = 0.065%) in budworm populations attributable to trap-nesting wasps
Detection and emotional evaluation of an electric vehicleâs exterior sound in a simulated environment
Electric vehicles are quiet at low speeds and thus potentially pose a threat to pedestriansâ safety. Laws are formulating worldwide that mandate these vehicles emit sounds to alert the pedestrians of the vehiclesâ approach. It is necessary that these sounds promote a positive perception of the vehicle brand, and understanding their impact on soundscapes is also important. Detection time of the vehicle sounds is an important measure to assess pedestriansâ safety. Emotional evaluation of these sounds influences assessment of the vehicle brand. Laboratory simulation is a new approach for evaluating exterior automotive sounds. This study describes the implementation of laboratory simulation to compare the detection time and emotional evaluation of artificial sounds for an electric vehicle. An Exterior
Sound Simulator simulated audio-visual stimuli of an electric car passing a crossroad of a virtual town at 4.47 ms-1 (10 mph), from the perspective of a pedestrian standing at the crossroad. In this environment, 15 sounds were tested using experiments where participants detected the car and evaluated its sound using perceptual dimensions. Results show that these sounds vary significantly in their detection times and emotional evaluations, but crucially that traditional metrics like dB(A) do not always relate to the detection of these sounds. Detection time and emotional evaluation do not have significant correlation. Hence, sounds of a vehicle could be detected
quickly, but may portray negative perceptions of the vehicle. Simulation provides a means to more fully evaluate potential electric vehicle sounds against the competing criteria
Too sick to drive : how motion sickness severity impacts human performance
There are multiple concerns surrounding the development and rollout of self-driving cars. One issue has largely gone unnoticed - the adverse effects of motion sickness as induced by self-driving cars. The literature suggests conditionally, highly and fully autonomous vehicles will increase the onset likelihood and severity of motion sickness. Previous research has shown motion sickness can have a significant negative impact on human performance. This paper uses a simulator study design with 51 participants to assess if the scale of motion sickness is a predictor of human performance degradation. This paper finds little proof that subjective motion sickness severity is an effective indicator of the scale of human performance degradation. The performance change of participants with lower subjective motion sickness is mostly statistically indistinguishable from those with higher subjective sickness. Conclusively, those with even acute motion sickness may be just as affected as those with higher sickness, considering human performance. Building on these results, it could indicate motion sickness should be a consideration for understanding user ability to regain control of a self-driving vehicle, even if not feeling subjectively unwell. Effectiveness of subjective scoring is discussed and future research is proposed to help ensure the successful rollout of self-driving vehicles
Heuristic bidding strategies for multiple heterogeneous auctions
This paper investigates utility maximising bidding heuristics for agents that participate in multiple heterogeneous auctions, in which the auction format and the starting and closing times can be different. Our strategy allows an agent to procure one or more items and to participate in any number of auctions. For this case, forming an optimal bidding strategy by global utility maximisation is computationally intractable, and so we develop two-stage heuristics that first provide reasonable bidding thresholds with simple strategies before deciding which auctions to participate in. The proposed approach leads to an average gain of at least 24% in agent utility over commonly used benchmarks
An investigation on the effect of driver style and driving events on energy demand of a PHEV
Environmental concerns, security of fuel supply and CO2 regulations are driving innovation in the automotive industry towards electric and hybrid electric vehicles. The fuel economy and emission performance of hybrid electric vehicles (HEVs) strongly depends on the energy management system (EMS). Prior knowledge of driving information could be used to enhance the performance of a HEV. However, how the necessary information can be obtained to use in EMS optimisation still remains a challenge. In this paper the effect of driver style and driving events like city and highway driving on plug in hybrid electric vehicle (PHEV) energy demand is studied.
Using real world driving data from three drivers of very different driver style, a simulation has been exercised for a given route having city and highway driving. Driver style and driving events both affect vehicle energy demand. In both driving events considered, vehicle energy demand is different due to driver styles. The major part of city driving is reactive driving influenced by external factors and driver leading to variation in vehicle speed and hence energy demand. In free highway driving, the driver choice of cruise speed is the only factor affecting vehicle energy demand
A smart driving smartphone application : real-world effects on driving performance and glance behaviours
A smart driving Smartphone application â which offers real-time fuel efficiency and safety feedback to the driver in the vehicle â was evaluated in a real-world driving study. Forty participants drove an instrumented vehicle over a 50 minute mixed route driving scenario, with 15 being selected for video data analysis. Two conditions were adopted, one a control, the other with smart driving advice being presented to the driver. Key findings from the study showed a 4.1% improvement in fuel efficiency when using the smart driving system, and an almost 3-fold reduction in time spent travelling closer than 1.5 seconds to the vehicle in front. Glance behavior results showed that drivers spent an average of 4.3% of their time looking at the system, at an average of 0.43 seconds per glance, with no glances of greater than two seconds. In conclusion this study has shown that a smart driving system specifically developed and designed with the driversâ information requirements in mind can lead to significant improvements in real-world driving behaviours, whilst limiting visual distraction, with the task being integrated into normal driving
SOME ISSUES CONCERNING SPECIFICATION AND INTERPRETATION OF OUTDOOR RECREATION DEMAND MODELS
Resource /Energy Economics and Policy,
Consensus Acceleration in Multiagent Systems with the Chebyshev Semi-Iterative Method
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
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