Cranfield University

About: Cranfield University is a education organization based out in Cranfield, United Kingdom. It is known for research contribution in the topics: Supply chain & Population. The organization has 10025 authors who have published 21130 publications receiving 678277 citations.

Interactions between microbial community structure and the soil environment found on golf courses

Abstract: Approximately 0.6% of the total UK land surface is occupied by golf courses, but little investigation into the biological properties of the soil under this type of amenity turf has been reported. The soil microbiota has a significant role within the majority of nutrient cycles. In order to analyse how golf course management affects the soil microbial community, an investigation of the phenotypic microbial community structure using phospholipid fatty acid (PLFA) analysis was carried out. Principal component analysis of PLFA biomarkers indicated that there were consistent relationships between the tees, fairways and greens and the soil microbial community structure. No conclusive mechanism could be demonstrated in one-way analysis with corresponding physical parameters (P>0.05 in all cases). Cannonical correlation analysis (CCA) using 28 PLFA biomarkers concurrently with 9 physicochemical parameters showed a highly significant relationship on different playing surfaces at all of the golf courses surveyed (P<0.01). The construction and maintenance of specific areas of a golf course, irrespective of geographical location, closely reflect the physicochemical status of the soil microbial habitat.

Multimodal control of sensors on multiple simulated unmanned vehicles.

Abstract: The use of multimodal (speech plus manual) control of the sensors on combinations of one, two, three or five simulated unmanned vehicles (UVs) is explored. Novice controllers of simulated UVs complete a series of target checking tasks. Two experiments compare speech and gamepad control for one, two, three or five UVs in a simulated environment. Increasing the number of UVs has an impact on subjective rating of workload (measured by NASA-Task Load Index), particularly when moving from one to three UVs. Objective measures of performance showed that the participants tended to issue fewer commands as the number of vehicles increased (when using the gamepad control), but, while performance with a single UV was superior to that of multiple UVs, there was little difference across two, three or five UVs. Participants with low spatial ability (measured by the Object Perspectives Test) showed an increase in time to respond to warnings when controlling five UVs. Combining speech with gamepad control of sensors on UVs leads to superior performance on a secondary (respond-to-warnings) task (implying a reduction in demand) and use of fewer commands on primary (move-sensors and classify-target) tasks (implying more efficient operation). STATEMENT OF RELEVANCE: Benefits of multimodal control for unmanned vehicles are demonstrated. When controlling sensors on multiple UVs, participants with low spatial orientation scores have problems. It is proposed that the findings of these studies have implications for selection of UV operators and suggests that future UV workstations could benefit from multimodal control.

Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Abstract: Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE (Hassall, 2019).