Arsenic contamination of groundwater is a major public health concern worldwide. The problem has been reported mainly in southern Asia and, especially, in Bangladesh. Slow-sand filters (SSF) augmented with iron were proven to be a simple, low-cost and decentralized technique for the treatment of arsenic-contaminated sources. In this research, three pilot-scale SSF (flowrate 6 L·h−1) were tested regarding their capability of removing arsenic from groundwater in conditions similar to those found in countries like Bangladesh (70 µg As(III) L−1, 26 °C). From the three, two filters were prepared with mixed media, i.e., sand mixed with corrosive iron matter (CIM filter) and iron-coated sand (ICS filter), and a third conventional SSF was used as a reference. The results obtained showed that the CIM filter could remove arsenic below the World Health Organization (WHO) guideline concentration of 10 µg·L−1, even for inlet concentrations above 150 µg·L−1. After 230 days of continuous operation the arsenic concentration in the effluent started increasing, indicating depletion or saturation of the CIM layer. The effluent arsenic concentration, however, never exceeded the Bangladeshi standard of 50 µg·L−1 throughout the whole duration of the experiments.
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Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
DOCUMENT
In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
DOCUMENT
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
Kansen voor circulaire beademingszorg De gezondheidszorg is verantwoordelijk voor 7% van de totale Nederlandse CO2-uitstoot. Eén van de meest materiaal intensieve afdelingen in een ziekenhuis is de intensive care. Patiënten op een intensive care worden beademd en ontvangen daarbij zogenaamde beademingszorg. Tijdens beademingszorg wordt gemaakt van hulpmiddelen zoals beademingsslangen, uitzuigslangen, filters en materialen ter infectiepreventie. De meeste hulpmiddelen worden na gebruik weggegooid. Om de zorg te verduurzamen zijn in de Green Deal doelstellingen geformuleerd om grondstoffenverbruik te verminderen in 2030 en uiteindelijk toe te werken naar circulaire zorg 2050. Er is op dit moment echter weinig kennis over de milieubelasting van gebruikte hulpmiddelen tijdens beademingszorg en de mogelijkheden om circulaire strategieën toe te passen. Dit project heeft als doel om een inventarisatie te maken van de milieubelasting en de afvalstromen van hulpmiddelen rondom beademingszorg. Daarbij is het project ook gericht op een inventarisatie van de mate waarin milieubelasting een overweging is bij de besluitvorming door betrokken stakeholders. Vervolgens zal in kaart worden gebracht welke mogelijkheden er zijn om via circulaire strategieën een bijdrage te leveren om de milieubelasting van hulpmiddelen rondom beademingszorg te verminderen. Voor de uitvoering van dit project zijn unieke deskundigheidsgebieden samengebracht in een consortium. De praktijkpartners hebben expertise in zorgverlening op de intensive care afdeling (AmsterdamUMC) en afvalstromen in ziekenhuizen (adviesbureau Innomax). De betrokken kennisinstellingen hebben expertise in onderwijs- en onderzoek rondom duurzaamheid (de Hogeschool van Amsterdam, Technische Universiteit Delft en Radboudumc). Dit consortium is een unieke samenwerking waarbij om kennis van zorgprocessen, afvalstromen en de milieubelasting van de zorgverlening op de intensive care worden gebundeld om de kansen voor duurzame beademingszorg te inventariseren. De resultaten van dit project zullen een praktijkverandering in gang zetten op intensive care afdelingen van AmsterdamUMC en Radboudumc en vervolgens ook verspreid worden via de landelijke en internationale netwerken.
Het thema duurzaamheid komt steeds prominenter naar voren in onze samenleving (Griggs, 2013). Zo ook binnen de reissector waar het vervuilende effect veelal in de media verschijnt. Dit resulteert in bewustwording, echter is reizen nog nooit eerder zo populair geweest (Vermeulen, 2014); het verandert niet de manier waarop wij boeken. Dreamtraveller beoogt sector en reiziger samen te laten werken en vertegenwoordigt de stem van de bestemmingen die ondergaan aan toerisme. Dit onlinefiltersysteem dat op bestaande boekingswebsites geïnstalleerd kan worden laat de beste keuze binnen de (duurzame)wensen van de reiziger zien. Zo creëer je niet alleen droombestemmingen, maar ook droomreizigers.
