OPTIma:a tracking solution for proton computed tomography in high proton flux environments

Currently there is a large discrepancy between the currents that are used for treatments in proton beam therapy facilities and the ultra low beam currents required for many proton CT imaging systems. Here we provide details of the OPTIma silicon strip based tracking system, which has been designed for performing proton CT imaging in conditions closer to the high proton flux environments of modern spot scanning treatment facilities. Details on the physical design, sensor testing, modelling, and track reconstruction are provided along with Monte-Carlo simulation studies of the expected performance for proton beam currents of up to 50 pA at the nozzle when using a σ = ∼10 mm spot scanning cyclotron system. Using a detailed simulation of the proposed OPTIma system, a discrepancy of less than 1% on the Relative Stopping Power is found for various tissues when embedded within a 150 mm diameter Perspex sphere. It is found that by accepting up to 7 protons per bunch it is possible to operate at cyclotron beam currents up to 5 times higher than would be possible with a single proton based readout, significantly reducing the total beam time required to produce an image, while also reducing the discrepancy between the beam currents required for treatment and those used for proton CT

Trajectories toward maximum power and inequality in resource distribution networks

Resource distribution networks are the infrastructure facilitating the flow of resources in both biotic and abiotic systems. Both theoretical and empirical arguments have proposed that physical systems self-organise to maximise power production, but how this trajectory is related to network development, especially regarding the heterogeneity of resource distribution in explicitly spatial networks, is less understood. Quantifying the heterogeneity of resource distribution is necessary for understanding how phenomena such as economic inequality or energetic niches emerge across socio-ecological and environmental systems. Although qualitative discussions have been put forward on this topic, to date there has not been a quantitative analysis of the relationship between network development, maximum power, and inequality. This paper introduces a theoretical framework and applies it to simulate the power consumption and inequality in generalised, spatially explicit resource distribution networks. The networks illustrate how increasing resource flows amplify inequality in power consumption at network end points, due to the spatial heterogeneity of the distribution architecture. As increasing resource flows and the development of hierarchical branching can both be strategies for increasing power consumption, this raises important questions about the different outcomes of heterogeneous distribution in natural versus human-engineered networks, and how to prioritise equity of distribution in the latter. © 2020 Davis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Evolutionary Algorithm for Decision Tree Induction

Part 2: AlgorithmsInternational audienceDecision trees are among the most popular classification algorithms due to their knowledge representation in form of decision rules which are easy for interpretation and analysis. Nonetheless, a majority of decision trees training algorithms base on greedy top-down induction strategy which has the tendency to develop too complex tree structures. Therefore, they are not able to effectively generalise knowledge gathered in learning set. In this paper we propose EVO-Tree hybrid algorithm for decision tree induction. EVO-Tree utilizes evolutionary algorithm based training procedure which processes population of possible tree structures decoded in the form of tree-like chromosomes. Training process aims at minimizing objective functions with two components: misclassification rate and tree size. We test the predictive performance of EVO-Tree using several public UCI data sets, and we compare the results with various state-of-the-art classification algorithms

Estimating soil properties from smartphone imagery in Ethiopia

The links between soil properties and smartphone imagery were investigated for 273 samples in the Halaba area of south-west Ethiopia. The aim of this was to explore the possibility of using a smartphone-based system to estimate soil properties in the field, without the need for sampling and laboratory analysis. This presents an opportunity to develop low cost soil assessment in remote locations. Imagery and associated site characteristics were captured using an ODK (Open Data Kit) interface developed specifically for the project. Two types of model linking image information to soil properties were explored, backpropagation neural networks (NN) and partial least squares (PLS). Models were generated with colour alone, spatial covariates alone and a combination of colour and spatial covariates. Two sets of data, for soil chemistry and soil physical properties, were modelled. For both NN and PLS models, estimation accuracy for chemical properties was consistently higher using colour and spatial covariate information together rather than colour or spatial covariates alone. For physical properties a similar pattern was seen but this was less clear, and estimation of physical properties was less successful based on statistical model validation.</p