Adsorption of chromium and phosphate on the fine fraction of granulated ferric hydroxide - influence of ph and water matrix on adsorption equilibrium

- Study the adsorption equilibrium of granulated ferric hydroxide, using micro and nano (agglomerated) particles. - Adsorption of inorganic substances (phosphat and chromium), which are contaminants coming from the industrial residual water, dangerous for human health and life in ecosystems. - Determination of influence of pH value and water matrix

Recognition of transport means in GPS data using machine-learning methods

Bicycle transport is today one of the most important measures in urban traffic with a view to moving towards more sustainable mobility. Nowadays, smartphones are equipped with Global Positioning System (GPS), which allows cyclists, through smartphone applications, to record their own routes on a daily basis, which is very useful information for traffic and transport planners.The problem appears when there is invalid data due to errors in the measurement or in the GPS signal. The solution is transport mode recognition, which consists of classifying the different existing transport modes on the basis of a set of data. The emerging techniques of machine learning allow the development of very powerful models capable of recognizing means of transport with great effectiveness, based on other studies.Accordingly, this study aims to separate GPS bicycle tracks from the other modes studied (inner-city train (S-Bahn), walk, bike, tram, bus), also classifying the tracks of each means of transport separately. The key contribution of this study is the design and implementation of a machine learning model capable of classifying existing modes of transport in urban traffic in the city of Dresden in Germany.For this purpose, a cascading classifiers model was designed so that in each phase tracks belonging to a different mode are separated, studying in each phase which of the machine learning algorithms used (Decision Tree, Support Vector Machine and Neural Network) has the best performance. The GPS data was collected with the application for smartphone Cyface and from there it was carried out the structuring of data and calculation and selection of features that serve as inputs of the model.To separate inner-city train (S-Bahn), bike and walk tracks (first three phases) accuracy values above 98 % are obtained for any of the mentioned algorithms. For the fourth phase, where the classification between bus and tram tracks is carried out, the performance of the model is not so outstanding, due to its similar characteristics, but nevertheless reaches an accuracy value of 83 % using a Neural Network Multi-layer Perceptron model. The great performance of the model after the training phase allowed its implementation using unlabeled tracks, achieving very good results with an accuracy of 92.6 % in the prediction of the tracks used, making only mistakes in distinguishing between tram and bus tracks.<br /