Unifying particle-based and continuum models of hillslope evolution with a probabilistic scaling technique

Relationships between sediment flux and geomorphic processes are combined with statements of mass conservation, in order to create continuum models of hillslope evolution. These models have parameters which can be calibrated using available topographical data. This contrasts the use of particle-based models, which may be more difficult to calibrate, but are simpler, easier to implement, and have the potential to provide insight into the statistics of grain motion. The realms of individual particles and the continuum, while disparate in geomorphological modeling, can be connected using scaling techniques commonly employed in probability theory. Here, we motivate the choice of a particle-based model of hillslope evolution, whose stationary distributions we characterize. We then provide a heuristic scaling argument, which identifies a candidate for their continuum limit. By simulating instances of the particle model, we obtain equilibrium hillslope profiles and probe their response to perturbations. These results provide a proof-of-concept in the unification of microscopic and macroscopic descriptions of hillslope evolution through probabilistic techniques, and simplify the study of hillslope response to external influences.Comment: 28 pages, 8 figure

Machine learning assists the classification of reports by citizens on disease-carrying mosquitoes

Mosquito Alert (www.mosquitoalert.com/en) is an expert-validated citizen science platform for tracking and controlling disease-carrying mosquitoes. Citizens download a free app and use their phones to send reports of presumed sightings of two world-wide disease vector mosquito species (the Asian Tiger and the Yellow Fever mosquito). These reports are then supervised by a team of entomologists and, once validated, added to a database. As the platform prepares to scale to much larger geographical areas and user bases, the expert validation by entomologists becomes the main bottleneck. In this paper we describe the use of machine learning on the citizen reports to automatically validate a fraction of them, therefore allowing the entomologists either to deal with larger report streams or to concentrate on those that are more strategic, such as reports from new areas (so that early warning protocols are activated) or from areas with high epidemiological risks (so that control actions to reduce mosquito populations are activated). The current prototype flags a third of the reports as “almost certainly positive” with high confidence. It is currently being integrated into the main workflow of the Mosquito Alert platform.Postprint (published version

A Conceptual Framework for the Prescriptive Causal Analysis of Construction Waste

An initial step towards a prescriptive theory (a set of concepts) to inform the elimination of waste on construction projects. The ultimate intention is to identify the most important types and causes of waste in construction and outline the principal causal relations between them. This is not a straightforward process: the relationships form a complex network of chains and cycles of waste. Waste is defined as the use of more resources than needed, or an unwanted output from production. A conceptual schema of Previous Production Stage > Production Waste > Effect Waste is proposed and applied to the causal analysis of two major types of waste: material waste and making do