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The main components of anthropogenic emissions are fertilised agricultural soils and livestock manure (42%), runoff and leaching of fertilisers (25%), biomass burning (10%), fossil fuel combustion and industrial processes (10%), biological degradation of other nitrogen-containing atmospheric emissions (9%) and human sewage (5%).[113][114][115][116][117] Agriculture enhances nitrous oxide production through soil cultivation, the use of nitrogen fertilisers and animal waste handling.[118] These activities stimulate naturally occurring bacteria to produce more nitrous oxide. Nitrous oxide emissions from soil can be challenging to measure as they vary markedly over time and space,[119] and the majority of a year's emissions may occur when conditions are favorable during \"hot moments\"[120][121] and/or at favorable locations known as \"hotspots\".[122]

Abstract:In this work, we present a mobile health system for the automated detection of crackle sounds comprised by an acoustical sensor, a smartphone device, and a mobile application (app) implemented in Android. Although pulmonary auscultation with traditional stethoscopes had been used for decades, it has limitations for detecting discontinuous adventitious respiratory sounds (crackles) that commonly occur in respiratory diseases. The proposed app allows the physician to record, store, reproduce, and analyze respiratory sounds directly on the smartphone. Furthermore, the algorithm for crackle detection was based on a time-varying autoregressive modeling. The performance of the automated detector was analyzed using: (1) synthetic fine and coarse crackle sounds randomly inserted to the basal respiratory sounds acquired from healthy subjects with different signal to noise ratios, and (2) real bedside acquired respiratory sounds from patients with interstitial diffuse pneumonia. In simulated scenarios, for fine crackles, an accuracy ranging from 84.86% to 89.16%, a sensitivity ranging from 93.45% to 97.65%, and a specificity ranging from 99.82% to 99.84% were found. The detection of coarse crackles was found to be a more challenging task in the simulated scenarios. In the case of real data, the results show the feasibility of using the developed mobile health system in clinical no controlled environment to help the expert in evaluating the pulmonary state of a subject.Keywords: respiratory sounds; smartphone; time-varying autoregressive model; crackles; automatic detection

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The slow opening of the 11th Berlin Biennale began a year ago, and since then it has been exploring the many cracks we carry, the fissures that keep us apart and those that bring us together. Many of the invited artists and participants in the Biennale have been exploring and practicing this, each in their own artistic terms, in their own contexts and temporalities. Making space to share these experiences demanded that we slow down the unsustainable pace of biennials and forgo the expectation of a singular concept, a novel idea to once again fix things into place. When the coronavirus pandemic hit the European fortress several months ago, it felt for a moment that the earth wanted to stand still. The virus exposed the cruelty of everyday life and the inequality endured by the vast majority of people imprisoned by patriarchal capitalism. As we write, many of those whose works are present in the exhibition are in the South and continue living under lockdown, in places where professional healthcare is a luxury, safeguarding only the privileged. 1e1e36bf2d