University of Greenwich

About: University of Greenwich is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 3749 authors who have published 9958 publications receiving 234340 citations.

Mass seasonal bioflows of high-flying insect migrants

Abstract: Migrating animals have an impact on ecosystems directly via influxes of predators, prey, and competitors and indirectly by vectoring nutrients, energy, and pathogens. Although linkages between vertebrate movements and ecosystem processes have been established, the effects of mass insect “bioflows” have not been described. We quantified biomass flux over the southern United Kingdom for high-flying (>150 meters) insects and show that ~3.5 trillion insects (3200 tons of biomass) migrate above the region annually. These flows are not randomly directed in insects larger than 10 milligrams, which exploit seasonally beneficial tailwinds. Large seasonal differences in the southward versus northward transfer of biomass occur in some years, although flows were balanced over the 10-year period. Our long-term study reveals a major transport process with implications for ecosystem services, processes, and biogeochemistry.

Macroalgae-Derived Biofuel: A Review of Methods of Energy Extraction from Seaweed Biomass

Abstract: The potential of algal biomass as a source of liquid and gaseous biofuels is a highly topical theme, but as yet there is no successful economically viable commercial system producing biofuel. However, the majority of the research has focused on producing fuels from microalgae rather than from macroalgae. This article briefly reviews the methods by which useful energy may be extracted from macroalgae biomass including: direct combustion, pyrolysis, gasification, trans-esterification to biodiesel, hydrothermal liquefaction, fermentation to bioethanol, fermentation to biobutanol and anaerobic digestion, and explores technical and engineering difficulties that remain to be resolved.

A Multilevel Algorithm for Force-Directed Graph Drawing

Abstract: We describe a heuristic method for drawing graphs which uses a multilevel technique combined with a force-directed placement algorithm. The multilevel process groups vertices to form clusters, uses the clusters to define a new graph and is repeated until the graph size falls below some threshold. The coarsest graph is then given an initial layout and the layout is successively refined on all the graphs starting with the coarsest and ending with the original. In this way the multilevel algorithm both accelerates and gives a more global quality to the force-directed placement. The algorithm can compute both 2 & 3 dimensional layouts and we demonstrate it on a number of examples ranging from 500 to 225,000 vertices. It is also very fast and can compute a 2D layout of a sparse graph in around 30 seconds for a 10,000 vertex graph to around 10 minutes for the largest graph. This is an order of magnitude faster than recent implementations of force-directed placement algorithms.

Non-pharmaceutical interventions during the COVID-19 pandemic: a rapid review

Abstract: Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travels bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I aim to review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 347 articles written by more than 2518 of authors in the last $12$ months. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities