Twitter data mining for the diagnosis of leaks in drinking water distribution networks

This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks

Assessing Urban Accessibility in Monterrey, Mexico: A Transferable Approach to Evaluate Access to Main Destinations at the Metropolitan and Local Levels

Cities demand urgent transformations in order to become more affordable, livable, sustainable, walkable and comfortable spaces. Hence, important changes have to be made in the way cities are understood, diagnosed and planned. The current paper puts urban accessibility into the centre of the public policy and planning agenda, as a transferable approach to transform cities into better living environments. To do so, a practical example of the City of Monterrey, Mexico, is presented at two planning scales: the metropolitan and local level. Both scales of analysis measure accessibility to main destinations using walking and cycling as the main transport modes. The results demonstrate that the levels of accessibility at the metropolitan level are divergent, depending on the desired destination, as well as on the planning processes (both formal and informal) from different areas of the city. At the local level, the Distrito Tec Area is diagnosed in terms of accessibility to assess to what extent it can be considered a part of a 15 minutes city. The results show that Distrito Tec lacks the desired parameters of accessibility to all destinations for being a 15 minutes city. Nevertheless, there is a considerable increase in accessibility levels when cycling is used as the main travelling mode. The current research project serves as an initial approach to understand the accessibility challenges of the city at different planning levels, by proving useful and disaggregated data. Finally, it concludes providing general recommendations to be considered in planning processes aimed to improve accessibility and sustainability

Twitter data mining for the diagnosis of leaks in drinking water distribution networks

This article presents a methodology for using data from social networks, specifically from Twitter, to diagnose leaks in drinking water distribution networks. The methodology involves the collection of tweets from citizens reporting leaks, the extraction of information from the tweets, and the processing of such information to run the diagnosis. To demonstrate the viability of this methodology, 358 Twitter leak reports were collected and analyzed in Mexico City from 1 May to 31 December 2022. From these reports, leak density and probability were calculated, which are metrics that can be used to develop forecasting algorithms, identify root causes, and program repairs. The calculated metrics were compared with those calculated through telephone reports provided by SACMEX, the entity that manages water in Mexico City. Results show that metrics obtained from Twitter and phone reports were highly comparable, indicating the usefulness and reliability of social media data for diagnosing leaks

Control of an Automotive Semi-Active Suspension

Two controllers for an automotive suspensions with Magneto-Rheological (MR) dampers are proposed. One is a model-based using the Linear Parameter Varying (LPV) approach, and the other is a model-free controller with a Frequency Estimation Based (FEB) principle. The LPV controller includes an experimental nonlinear model of an MR damper using only one scheduling parameter. A comparison with a several semiactive controllers for comfort and road holding is discussed. The FEB controller is the best option based on frequency and time response analysis for comfort (10–20%), suspension deflection (30–50%), and road holding (1–5%)

LPV Control for a Semi-Active Suspension Quarter of Car-One Parameter Case

The actual semi-active suspension control systems with a balance between comfort and road holding goals are not optimal because in these solutions one goal or the other always dominates in the suspension performance. This paper is centered in a new proposal to control an automotive semi-active suspension to achieve the comfort and maintain the road holding.The output in the control strategy is the electric current. A nonlinear quarter of vehicle model simulation compares and validates the proposal versus different controllers. The controller is designed with the H∞ criteria and the Linear Varying Parameter (LPV) considering the saturation and sigmoid shape of the F-V characteristic diagram. Unlike the solutions in literature, which use at least two scheduling parameters, the proposed LPV controller scheme for a semi-active suspension uses only one scheduling parameter

LPV Control for a Semi-Active Suspension Quarter of Car-One Parameter Case

The actual semi-active suspension control systems with a balance between comfort and road holding goals are not optimal because in these solutions one goal or the other always dominates in the suspension performance. This paper is centered in a new proposal to control an automotive semi-active suspension to achieve the comfort and maintain the road holding.The output in the control strategy is the electric current. A nonlinear quarter of vehicle model simulation compares and validates the proposal versus different controllers. The controller is designed with the H∞ criteria and the Linear Varying Parameter (LPV) considering the saturation and sigmoid shape of the F-V characteristic diagram. Unlike the solutions in literature, which use at least two scheduling parameters, the proposed LPV controller scheme for a semi-active suspension uses only one scheduling parameter

The Importance of Robust Datasets to Assess Urban Accessibility: A Comparable Study in the Distrito Tec, Monterrey, Mexico, and the Stanford District, San Francisco Bay Area, USA

Urban planning has a crucial role in helping cities meet the United Nations' Sustainable Development Goals and robust datasets to assess mobility accessibility are central to smart urban planning. These datasets provide the information necessary to perform detailed analyses that help develop targeted urban interventions that increase accessibility in cities as related to the emerging vision of the 15 Minute City. This study discusses the need for such data by performing a comparative urban accessibility analysis of two university campuses and their surrounding urban areas, here defined as the Stanford District, located in the San Francisco Bay Area in the United States, and Distrito Tec in Monterrey, Mexico. The open-source tool Urban Mobility Accessibility Computer (UrMoAC) is used to assess accessibility measures in each district using available data. UrMoAC calculates distances and average travel times from block groups to major destinations using different transport modes considering the morphology of the city, which makes this study transferable and scalable. The results show that both areas have medium levels of accessibility if cycling is used as the primary mode of transportation. Hence, improving the safety and quality of cycling in both cities emerges as one of the main recommendations from the research. Finally, the results obtained can be used to generate public policies that address the specific needs of each community's urban region based on their accessibility performance

Novel Design Methodology for DC-DC Converters Applying Metaheuristic Optimization for Inductance Selection

Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter’s performance, leading to smaller and more efficient designs. Hence, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters, in order to optimize their performance through an optimal inductance selection. Therefore, this work presents a novel design methodology for DC-DC converters, where the inductance selection is optimized, in order to achieve an optimal relation between the inductance size and the required energy. Moreover, a multi-objective metaheuristic optimization is presented through the Earthquake Algorithm, for parameter estimation and component selection, using the inductance of a buck DC-DC converter as a case study. The experimental results validate the design methodology, showing ripple improvement and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Results also confirm the Small-Signal Model of the circuit, as a correct objective function for the parameter optimization, achieving more than 90% of accuracy on the presented behavior