The impact of the number of high temporal resolution water meters on the determinism of water consumption in a district metered area.

Developments in data mining techniques have significantly influenced the progress of Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the development of increasingly reliable predictive models of water consumption. The non-stationary, non-linear, and inherent stochasticity of water consumption data at the level of a single water meter means that the characteristics of its determinism remain impossible to observe and their burden of randomness creates interpretive difficulties. A deterministic model of water consumption was developed based on data from high temporal resolution water meters. Seven machine learning algorithms were used and compared to build predictive models. In addition, an attempt was made to estimate how many water meters data are needed for the model to bear the hallmarks of determinism. The most accurate model was obtained using Support Vector Regression (8.9%) and the determinism of the model was achieved using time series from eleven water meters of multi-family buildings.

Emergency contraception - A review.

Emergency contraception (EC), or postcoital contraception, is a therapy aimed at preventing unintended pregnancy after an act of unprotected or under-protected sexual intercourse. Options include both emergency contraceptive pills (most commonly containing levonorgestrel or ulipristal acetate) and insertion of an intrauterine device. The aim of this paper is to summarize current evidence surrounding the use of emergency contraceptives and to present an evidence-based approach to EC provision. Emergency contraception is a safe and effective option in preventing unwanted pregnancy, irrespective of age, weight, or breastfeeding status. Efforts should be made to increase their availability, as well as knowledge of these methods, both among patients and healthcare providers.

Improving short-term water demand forecasting using evolutionary algorithms.

Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.