The occurrence of escherichia coli in groundwater of bekasi city (Case Study: Jatiluhur, sumur batu, and jatirangga urban villages)

© 2020 Institute of Physics Publishing. All rights reserved. The self-supply of groundwater at the household level, and especially in densely populated areas, is vulnerable to fecal contamination. The aim of this study was to assess the level of fecal contamination in groundwater of three urban villages in Bekasi City that depend greatly on groundwater: Jatiluhur, Sumur Batu, and Jatirangga. Water samples were taken from 255 households with various types of water sources in the rainy season from February-March 2020. Escherichia coli (E. coli) concentration was quantified with Colilert-18 using IDEXX Quanti-Tray/2000 based on Most Probable Number (MPN) method. E. coli levels were beyond the WHO standard and found in 60% of the sources; 24% were above 100 MPN/100 mL. The presence of E. coli in groundwater indicated a requirement for further treatment prior to the point of consumption and an urgent need to replace the water supply infrastructure for improved water sources

Fear Learning for Flexible Decision Making in RoboCup: A Discussion

In this paper, we address the stagnation of RoboCup com- petitions in the fields of contextual perception, real-time adaptation and flexible decision-making, mainly in regards to the Standard Platform League (SPL). We argue that our Situation-Aware FEar Learning (SAFEL) model has the necessary tools to leverage the SPL competition in these fields of research, by allowing robot players to learn the behaviour profile of the opponent team at runtime. Later, players can use this knowledge to predict when an undesirable outcome is imminent, thus having the chance to act towards preventing it. We discuss specific scenarios where SAFEL’s associative learning could help to increase the positive outcomes of a team during a soccer match by means of contextual adaptation