In Chile, as in many other areas of the world, water supplies have been poorly managed and water availability is decreasing. In order to manage water resources more sustainably and equitably, it is necessary to understand and predict their supply and use considering the characteristics of a particular zone. This study aimed to develop a conceptual model for water management in the Libertador Bernardo O’Higgins Region in Chile. The model considers the water needs of industries with production activities, human consumption, and the ecological flow of each sub-basin in the area. The results show that the proposed model contributes to the understanding of the critical variables, their agents, and the interaction between the hydric demands, which enables the prioritization of human consumption and the ecological flow. Furthermore, the cross-analysis between the offer and demand indicates that current and predicted consumption levels will only be sustainable up to the year 2031. The findings may be of use to decision-makers seeking to improve water management plans in this area and elsewhere, and to others interested in modeling water management in different areas.

Since the 1985 M 8.0 central Chile earthquake, national strong-motion seismic networks have recorded ten megathrust earthquakes with magnitudes greater than M 7.5 at the convergent margin, defined by the contact between the Nazca and South American plates. The analysis of these earthquake records have led to improved hazard analyses and design codes for conventional and seismically protected structures. Although strong-motion baseline correction is required for a meaningful interpretation of these records, correction methods have not been applied consistently in time. The inconsistencies between correction methods have been neglected in the practical use of these records in practice. Consequently, this work aims to provide a new strong-motion database for researchers and engineers, which has been processed by traceable and consistent data processing techniques. The record database comes from three uncorrected strong motion Chilean databases. All the records are corrected using a four-step novel methodology, which detects the P-wave arrival and introduces a baseline correction based on the reversible-jump Markov chain Monte Carlo method. The resulting strong motion database has more than 2000 events from 1985 to the date, and it is available to download at the Simulation Based Earthquake Risk and Resilience of Interdependent Systems and Networks (SIBER-RISK) project website.

Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Unlike many other heavily affected countries, Chile implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available may be useful to understand the effect of localized quarantines in containing the COVID-19 pandemic.

Genetic algorithms have proved to be a useful improvement heuristic for tackling several combinatorial problems, including the coalition structure generation problem. In this case, the focus lies on selecting the best partition from a discrete set. A relevant issue when designing a Genetic algorithm for coalition structure generation problems is to choose a proper genetic encoding that enables an efficient computational implementation. In this paper, we present a novel hybrid encoding, and we compare its performance against several genetic encoding proposed in the literature. We show that even in difficult instances of the coalition structure generation problem, the proposed approach is a competitive alternative to obtaining good quality solutions in reasonable computing times. Furthermore, we also show that the encoding relevance increases as the number of players increases.

ABSTRACTA new computational platform for seismic hazard assessment is presented. The platform, named SeismicHazard, allows characterizing the intensity, uncertainty, and likelihood of ground motions from subduction‐zone (shallow interface and intraslab) and crustal‐zone earthquakes, considering site‐specific as well as regional‐based assessments. The platform is developed as an object‐oriented MATLAB graphical user interface, and it features several state‐of‐the‐art capabilities for probabilistic and deterministic (scenario‐based) seismic hazard assessment. The platform integrates the latest developments in performance‐based earthquake engineering for seismic hazard assessment, including seismic zonation models, ground‐motion models (GMMs), ground‐motion correlation structures, and the estimation of design spectra (uniform hazard spectra, classical conditional mean spectrum (CMS) for a unique tectonic setting). In addition to these standard capabilities, the platform supports advanced features, not commonly found in existing seismic hazard codes, such as (a) computation of source parameters from earthquake catalogs, (b) vector‐probabilistic seismic hazard assessment, (c) hazard evaluation based on conditional GMMs and user‐defined GMMs, (d) uncertainty treatment in the median ground motions through continuous GMM distributions, (e) regional shaking fields, and (f) estimation of CMS considering multiple GMMs and multiple tectonic settings. The results from the platform have been validated against accepted and well‐documented benchmark solutions.

Arsenic contamination in water remains a critical global challenge, particularly in rural and resource-limited regions. Clays have been widely studied as cost-effective and efficient adsorbents for arsenic removal. This systematic review provides a comprehensive analysis of the application of clays in arsenic adsorption, focusing on clay types, operational units, and study methodologies. The review classifies the adsorption mechanisms, highlights key factors influencing adsorption performance—such as pH, ionic strength, and surface modifications—and examines the effectiveness of various modifications. Furthermore, the study categorizes adsorption research into kinetic, iso-thermal, thermodynamic, and efficiency studies, providing insights into the state of the art and the experimental conditions that govern arsenic removal. It also discusses the scalability and practical application of clay-based adsorption technologies, emphasizing gaps in field validation, regeneration studies, and large-scale implementation. The findings highlight the potential of natural and modified clays in arsenic remediation, while underscoring the need for further research to optimize adsorption conditions and enhance sustainability in water treatment systems.