Abstract
Water loss is one of the factors that most affect a concessionaire's financial sustainability. Early detection of any anomaly in water consumption is very valuable. This article aims to carry out a preliminary study to detect change points in consumption associated with water meter malfunction. The dataset is composed of water consumption measurements of two different companies (a hotel and a hospital) located in the north of Portugal, obtained during a complete year. Different methods were implemented in order to study its effectiveness in the detection of change points in the time series related to a sharp decrease in water consumption. Results suggest that the Seasonal Decomposition of Time Series by Loess method (STL) and the combination of several breakpoint detection methods is a suitable approach to be implemented in a software system, in order to help the company in anomaly detection and in the decision-making process of substituting the water meters.

Abstract
Breaks in water consumption records can represent apparent losses which are generally associated with the volumes of water that are consumed but not billed. The detection of these losses at the appropriate time can have a significant economic impact on the water company's revenues. However, the real datasets available to test and evaluate the current methods on the detection of breaks are not always large enough or do not present abnormal water consumption patterns. This study proposes an approach to generate synthetic data of water consumption with structural breaks which follows the statistical proprieties of real datasets from a hotel and a hospital. The parameters of the best-fit probability distributions (gamma, Weibull, log-Normal, log-logistic, and exponential) to real water consumption data are used to generate the new datasets. Two decreasing breaks on the mean were inserted in each new dataset associated with one selected probability distribution for each study case with a time horizon of 914 days. Three different change point detection methods provided by the R packages strucchange and changepoint were evaluated making use of these new datasets. Based on Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) performance indices, a higher performance has been observed for the breakpoint method provided by the package strucchange.

Abstract

Abstract
Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city's assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly.

Abstract
Population aging brings increased social problems. Solutions for this new reality must be devised. Providing care services at home may benefit patients, health service providers, and social security systems and needs to be seen as a possible solution for those social problems. By maintaining the patient at home, in his or her own environment, care services costs can be diminished and, at the same time, the comfort and well-being of the person in need are significantly increased. To pursue this goal, we explore the advantages that ambient assisted living can bring to people in a home environment, focusing on the problems of health care services at home. Specifically, in this chapter, we present a framework focused on the monitoring and assistance of the elderly that are living alone, focusing on those elderly with memory disabilities. We believe that this approach will enable the challenges that the current trend of population aging poses to be tackled. © 2015 by Taylor & Francis Group, LLC.

Abstract
As ESG regulations gain relevance, namely following the entry into force of EU's Corporate Sustainability Reporting Directive, there is an increased requirement for value chain transparency. Organizations that are heavily based on data, Artificial Intelligence or Machine Learning, may significantly contribute to the ESG profiles of their upstream clients through the resources they spend on data storage and processing, model training or model serving. However, the lack of standardized mechanisms to report ESG-relevant indicators from these organizations hinders effective integration into their clients' ESG disclosures. This paper is motivated by an identified need to facilitate ESG indicator reporting by ML organizations in the value chain, and proposes an architecture to automate the monitoring and reporting process. This architecture is based on the principle of observability, and on the integration and effective use of open-source tools to monitor data processing pipelines and MLOps. By implementing an automated observability-based monitoring framework, ML organizations can provide actionable ESG data that align with regulatory requirements, while reducing reporting overhead for clients. We address how this architecture can be seamlessly integrated into existing operational workflows of ML providers and their clients, enhancing transparency, accountability, and compliance. The proposed architecture ensures accurate, real-time reporting and creates a scalable foundation for ESG aligned innovation in Machine Learning and adjacent domains. © 2025 IEEE.