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Item type:Publication, Unsupervised learning for deploying smart charging public infrastructure for electric vehicles in sprawling cities(Elsevier, 2020-09-01)This paper presents a novel methodology to study the deployment of public smart charging stations (CS) of electric vehicles (EV) in a sprawling Latin American city. A relevant difference between developed and emerging economies is the reduced access to home charging in emerging economies, which is the case in Latin American cities. Thus, developing public charging stations represents a crucial factor in the mass adoption of EVs by road commuters. We develop herein a methodology for optimizing the deployment of smart charging stations under the sprawling phenomenon perspective. Our method comprises two steps. In step one, we applied principal component analysis (PCA) to facilitate the analysis of a sprawling city, and then we define candidates for potential locations from ‘demand clusters’ within an urbanized area, by K-means clustering analysis. In the second step, a stochastic programming model was employed to optimize the integration of infrastructural facilities with distributed energy resources (DERs) and EV charging stations using a collaborative strategy to minimize its energy consumption cost under demand uncertainty. We demonstrate the capabilities of this approach through a case study in the city of Lima. Experimental results reveal managerial insights for different stakeholders (i.e., government, industry, academia, and civil society) to promote policies, investment, and incentives. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MODELAMIENTO DE COSTOS EN PERFORACIÓN Y VOLADURA SUBTERRÁNEA: ANÁLISIS PARAMÉTRICO DE BARRENOS CON SEGMENTACIÓN BASADA EN MACHINE LEARNING(2026-01-01)Las operaciones de perforación y voladura constituyen unidades críticas en minería subterránea por su impacto directo sobre la productividad del ciclo y los costos operacionales. Objetivo: Este estudio desarrolló un modelo paramétrico de costos directos para estimar y comparar el costo unitario (US$/m perforado) y costos normalizados por desempeño (US$/m de avance y US$/t) bajo dos alternativas operacionales de desarrollo: barrenos de 12 pies y 14 pies. Métodos: Se emplearon registros de campo de geometría de perforación, consumos de explosivos y accesorios, tiempos de operación y costos unitarios, consolidando resultados por frente/disparo y evaluando diferencias absolutas y relativas entre escenarios. Resultados: El escenario de 14 pies incrementó el metraje perforado por frente de 183.6 a 202.8 m (+10.5%) y el avance por disparo de 3.06 a 3.90 m (+27.5%). Aunque el explosivo total aumentó de 96.9 a 107.6 kg (+11.0%), el factor de potencia disminuyó de 0.61 a 0.56 kg/tn (−8.2%). El costo por metro perforado se redujo de 269.28 a 250.20 US$/m (−7.09%), con disminuciones en voladura (−13.16%) y perforación (−5.53%). Asimismo, el costo por metro de avance y por tonelada estimada disminuyeron en −19.47% y −15.15%. Conclusiones: La alternativa de 14 pies mejora la eficiencia costo–productividad al aumentar el avance efectivo, recomendándose su evaluación junto con criterios de control de contorno y estabilidad. Complementariamente, se propone un marco de aprendizaje automático y clustering no supervisado para fortalecer la predicción e interpretabilidad de los factores dominantes del costo. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development and Validation of a Methodology for Predicting Fuel Consumption and Emissions Generated by Light Vehicles Based on Clustering of Instantaneous and Cumulative Vehicle Power(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03-01)In the global context, transportation contributes 26% of the total CO2 emissions, with land transport responsible for 92% of the emissions within the sector. Given this significant contribution to climate change, it is crucial to quantify vehicular impacts to implement effective mitigation strategies. This study introduces an innovative method for predicting fuel consumption and emissions of carbon monoxide, hydrocarbons, and nitrogen oxides in vehicles, based on instantaneous vehicle-specific power (VSP) and mean accumulated power. VSP is a parameter that measures a vehicle's power in relation to its mass, providing an indicator of the efficiency with which the vehicle converts fuel into motion. This indicator is particularly useful for assessing how vehicles utilize their energy under different driving conditions and how this affects their fuel consumption and emissions. Using data collected from 10 vehicles over 2000 h and covering altitudes from 0 to 4000 m above sea level in Ecuador, the method not only improved the accuracy of consumption predictions, reducing the margin of error by up to 10% at high altitudes, but also provided a detailed understanding of how altitude affects both consumption and emissions. The precision of the new method was notable, with a standard deviation of only 0.25 L per 100 km, allowing for reliable estimates under various operational conditions. Interestingly, the study revealed an average increase in fuel consumption of 0.43 L per 1000 m of altitude gain, while CO2 emissions showed a significant reduction from 260.93 g/km to 215.90 g/km when ascending from 500 m to 4000 m. These findings underscore the relevance of considering altitude in route planning, especially in mountainous terrains, to optimize performance and environmental sustainability. However, the study also indicated an increase in CO and NOx emissions with altitude, a challenge that highlights the need for integrated strategies addressing both fuel consumption and air quality. Collectively, the results emphasized the complex interplay between altitude, energy efficiency, and vehicular emissions, underscoring the importance of a holistic approach to transportation management, to minimize adverse environmental impacts and promote sustainability.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Bibliometric Analysis on Network-Based Systemic Risk(Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-01)The vulnerability of the global financial system to systemic risk-related adverse events has become more evident in recent years, as shown by the 2008 financial crisis and the global pandemic. This study examines systemic risk and its contributing factors using network analysis to understand how contagion occurs. To achieve this, a bibliometric analysis was conducted using a cluster analysis of publications from 2020 to 2025. The bibliometric analysis covered 1642 papers related to systemic risk and financial transmission networks. The CiteSpace software was used to identify seven thematic clusters. The results show the relevance of topological analysis in explaining the connection between institutions and the spread of risk. There is also a clear tradition in the literature of applying the DY spillover index, which captures the temporal dynamics of systemic connectivity. Multilayer networks stand out as a trend in recent studies, as they have the potential to represent different types of relationships simultaneously between nodes. Finally, the literature pays attention to systemic connectivity problems during crises, which can amplify volatility and generate forced asset sales, highlighting the need to use advanced VAR-type models to anticipate risk transmission and guide macroprudential management.1