Modelling approach to an ultrafiltration process for the removal of dissolved and colloidal substances from treated wastewater for reuse in recycled paper manufacturing

[EN] In this work, ultrafiltration (UF) is used to remove dissolved and colloidal substances (DCS) from a secondary clarifier effluent from a wastewater treatment plant (WWTP) in a papermaking factory. The approach has been to examine and model the decline in permeate flux resulting from membrane fouling. Effluent from a WWTP at a papermaking factory, previously filtered, was used as feed. UF experiments were carried out in a laboratory-scale plant using a 10 kDa polyethersulfone (PES) UF membrane in a flat sheet module with an active area of 154.8 cm(2). The transmembrane pressure (TMP) (1-3 bar) and crossflow rate (1.5-4.5 L/min) were varied during the experiments, at constant temperature (22 +/- 0.5 degrees C). Experimental results from UF tests were expressed in terms of permeate flux (Jp) as a function of time to check modified Hermia's models adapted to crossflow filtration. The parameters of these models were theoretically estimated. The predicted results were compared with experimental data with a high goodness of fit. The results showed that the phenomenon controlling fouling, under most of the conditions tested, was intermediate blocking (R-2 > 0.96). Measurements of particle size distribution and zeta potential near the isoelectric point, showed a substantial reduction in colloidal compounds. Additionally, given that COD was removed down to 110 mg/L, it could be said that UF is suitable for producing water that can be reused in different papermaking processes.The Euro-Brazilian Windows + project (with financial support granted by the European Commission through the Erasmus Mundus Programme).Santos-Sousa, MR.; Lora-García, J.; López Pérez, MF. (2018). Modelling approach to an ultrafiltration process for the removal of dissolved and colloidal substances from treated wastewater for reuse in recycled paper manufacturing. Journal of Water Process Engineering. 21:96-106. https://doi.org/10.1016/j.jwpe.2017.11.017S961062

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Elementarni procesy v dohasinajicim plasmatu

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Augmented reality as a new way of exploring the city: unified platform for data providers

The purpose of the paper is to present the ways of using augmented reality as part of location based services. These tools have become very popular after the mass using of smart phones and navigation technologies. The objective of the paper is to introduce a unified web-based platform invented for data providers for smarter transformation of the static data. We used developer's interfaces of the most popular augmented reality mobile apps (Layar and Wikitude) to utilize them in to the user-friendly web platform which provides comfort for content creators. Thus we have a simple and very effective tool for dynamization of static data (points of interests in the cities) to the dynamize form suitable for augmented reality apps. The implementation of the web-based platform is being realized among Czech destination management organizations and will be released under the open source license. This approach comes with the unique benefits for data providers/content creators and could bring new point of view for big data presentation to the public.Technology Agency of the Czech Republi