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A biomimetic smart kirigami soft metamaterial with multimodal remote locomotion mechanisms
(2023) Silva, Benjamín; Govan, Joseph; Zagal, Juan Cristóbal; Grossi, Bruno; Roldan, Alejandro; Nunez, Alvaro S.; Acuña, Daniel; Palza, Humberto
Several efforts have been made to develop walking smart soft robots through different strategies such as the use of complex aligned magneto-active materials. Here, we show a simple approach for the design of a smart soft robot using an elastomer film with randomly distributed ferrimagnetic nanoparticles able to be remotely controlled by a magnetic field. The magneto-active robot has a rotating-square kirigami geometry resulting in a flexible smart auxetic metamaterial (i.e., a negative Poisson-ratio structure). Alongside the standard translational locomotion on a smooth-surface under a steady magnetic force, the auxetic kirigami structure mimics the crawling-locomotion of worms over a high-roughness surface under an oscillatory horizontal field, even climbing vertical-obstacles. A theoretical understanding for this new locomotion mechanism stresses the relevance of the kirigami metamaterial design and the ferrimagnetic response of the particles. The soft robot can also transport a payload having weights higher than the weight of the smart elastomeric film. The smart auxetic structure further presents a rolling locomotion by properly orienting the magnetic field, meaning multiple remote locomotion mechanisms.
A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona
(2021) Graells-Garrido, Eduardo; Rowe, Francisco; Serra-Burriel, Feliu; Cucchietti, Fernando M.; Reyes, Patricio
As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the “15-minutes city” have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.
A class of solutions for the graphene hamiltonian operator
(2022) Conca, C.; San Martín, J.; Solano, Viviana
The graphene is a substance with carbon atoms arranged in a honeycomb lattice. The dynamics of the electrons in the structure is governed by the Hamilton equations of the system in the form of its associated spectral problem: HΨ = λΨ, with the additional condition that the eigenfunction Ψ must satisfy the so-called Kirchhoff’s conditions. In this paper, we study a class of solutions (λ, Ψ) that, in addition to meeting these conditions, are periodic in one of the two main directions of the lattice, and satisfy a pseudo-periodicity type like condition in the other direction. Our main results lead to an adequate characterization of the dispersion relationships of the honeycomb lattice, providing a precise description of the regions of stability and instability of the eigenfunctions in terms of λ. As a consequence, a tool is thus obtained for a better understanding of the propagation properties and the behavior of the wave function of electrons in a hexagonal lattice, a key issue in graphene-based technologies.
A combined photovoltaic and novel renewable energy system: An optimized techno-economic analysis for mining industry applications
(01/04/2017) Vyhmeister, Eduardo; Muñoz, Cristina Aleixendri; Bermúdez, José Miguel; Pina Moya, Javier; Fúnez Guerra, Carlos; Rodríguez Mayor, Lourdes; Godoy Faúndez, Alex; Higueras, Pablo; Clemente-Jul, Carmen; Valdés-González, Héctor; Reyes-Bozo, Lorenzo
The productivity of the mining industry in Chile, currently the main driver of Chilean economy, is closely tied to foreign demand for ores. Ore-processing is known for involving energy–intensive processes, such as comminution, concentrating and cathodic processes. As mining activities take place in the arid north of Chile, they are affected by water scarcity. Water shortage has forced the industry to pump desalinated seawater up to mining sites over 2000 m above sea level, further increasing electricity consumption. Given these energy needs, and the fact that electrical energy supply in the north of Chile is based on fossil fuels, it is important to consider the use of renewable energies as environment-friendly and economic alternatives. The aim of this work is to evaluate, by an optimized techno-economic analysis, the use of photovoltaic and a novel wind-based technology to supply at least 10% of the current and the predicted electrical energy requirements of the mining industry in the Antofagasta region. A combination of an optimization problem and technical evaluation was performed using Matlab to obtain the optimal number of solar and wind-based technology units in a case study. Total energy generation from a novel wind-based technology unit is 67,616 MWh/y, corresponding to 14.45·106 Nm3 (1298 t) of hydrogen and 7.41·106 Nm3(10,323 t) of oxygen after electrolytical transformation. Considering a 65% efficiency of the combined cycle fed with hydrogen and oxygen, 28,133 MWh/y of electrical energy would be obtained. For the cases studied the cost of energy from the combined system was estimated to be between 0.255 US$/kWh and 0.273 US$/kWh, slightly higher than the average energy regional cost. According to the analysis, the renewable energy system could be a sustainable alternative to supply economic green energy to the mining industry in Chile.
A comparison of optimization models for lumber production planning
(2015) Palma, Cristian D.; Sepúlveda, Héctor; Vergara, Francisco P.
The performance of sawmills is strongly dependent on how logs are sawn into lumber in order to satisfy the customer demands. To do this, sawmill managers have to decide which cutting patterns have to be applied to logs of different dimensions. Optimization models have been proposed to assist decision makers in this process, but only the profit maximization and the cost minimization of the decisions have been considered as the models objective. In this paper, a linear optimization model was formulated to address lumber production planning and applied to a real problem. The current decisions at sawmills were compared with five different objective functions: the two previously mentioned plus waste minimization, log number minimization and production time minimization. Only profit maximization and waste minimization models reported positive economic returns. Although the current decision at sawmills also reported a positive economic return, the same economic result was obtained with significantly fewer resources using the waste minimization model. The effects of the different objectives on the production indicators were discussed
A Conceptual Model Considering Multiple Agents for Water Management
(2022-06-30) Rivadeneira Tassara, Benjamín; Valdés González, Héctor; Fúnez Guerra, Carlos; Reyes-Bozo, Lorenzo
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.
A Consistently Processed Strong-Motion Database for Chilean Earthquakes
(2022) Castro, Sebastián; Benavente, Roberto; Crempien, Jorge G.F.; Candia, Gabriel; Llera, Juan Carlos de la
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.
A Cross-Cutting Approach for Relating Anthropocene, Environmental Injustice and Sacrifice Zones
(2022) Gayo, Eugenia M.; Muñoz, Ariel A.; Maldonado, Antonio; Lavergne, Céline; Francois, Jean Pierre; Rodríguez-Oroz, Delia; Klock-Barría, Karin; Sheppard, Paul R.; Aguilera-Betti, Isabella; Alonso-Hernández, Carlos; Mena-Carrasco, Marcelo; Urquiza, Anahí; Gallardo, Laura
The Anthropocene is an uneven phenomenon. Accelerated shifts in the functioning of the Earth System are mainly driven by the production and consumption of wealthy economies. Social, environmental and health costs of such industrialization, however, bear on low-income communities inhabiting severely degraded territories by polluting activities (i.e., sacrifice zones). How global, national and local socioeconomic and governance processes have interacted in perpetuating socio-environmental inequalities in these territories has been rarely explored. Here, we develop an historical quantitative approach integrating a novel chemostratigraphic record, data on policy making, and socio-economic trends to evaluate the feedback relationship between environmental injustice and Anthropocene in sacrifice zones. We specifically outline a case study for the Puchuncaví valley -one of the most emblematic sacrifice zones from Chile-. We verify an ever-growing burden of heavy metals and metalloids over the past five decades paced by the staggering expansion of local industrial activities, which has ultimately been spurred by national and transnational market forces. Local poverty levels have declined concomitantly, but this path toward social equality is marginal as costs of pollution have grown through time. Indeed, national and international pollution control actions appear insufficient in mitigating the cumulative impact brought by highly toxic elements. Thus, our sub-decadal reconstruction for pollution trends over the past 136 years from a sediment record, emerges as a science-based tool for informing the discussion on Anthropocene governance. Furthermore, it helps to advance in the assessment of environmental inequality in societal models that prioritize economic growth to the detriment of socio-environmental security.
A data fusion approach with mobile phone data for updating travel survey-based mode split estimates
(2023) Graells-Garrido, Eduardo; Opitz, Daniela; Rowe, Francisco; Arriagada, Jacqueline
Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorisation techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit (all public transportation), motorised (cars and similar vehicles), active (pedestrian and cycle trips), and taxi (traditional taxi and ride-hailing services). Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. Findings also point to a rise in the share of taxi usage, and a drop in active mobility, suggesting a modal shift towards less sustainable modes of travel. We validate our findings comparing our updated estimates with official smart card transaction data. The consistency of findings with expert domain knowledge from the literature and historical transport usage trends further support the robustness of our approach.
A dataset to assess mobility changes in Chile following local quarantines
(2023) Pappalardo, Luca; Cornacchia, Giuliano; Navarro, Victor; Bravo, Loreto; Ferres, Leo
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.
A deep learning approach for real-time crash prediction using vehicle-by-vehicle data
(2021) Basso, Franco; Pezoa, Raúl; Varas, Mauricio; Villalobos, Matías
In road safety, real-time crash prediction may play a crucial role in preventing such traffic events. However, much of the research in this line generally uses data aggregated every five or ten minutes. This article proposes a new image-inspired data architecture capable of capturing the microscopic scene of vehicular behavior. In order to achieve this, an accident-prediction model is built for a section of the Autopista Central urban highway in Santiago, Chile, based on the concatenation of multiple-input Convolutional Neural Networks, using both the aggregated standard traffic data and the proposed architecture. Different oversampling methodologies are analyzed to balance the training data, finding that the Deep Convolutional Generative Adversarial Networks technique with random undersampling presents better results when generating synthetic instances that permit maximizing the predictive performance. Computational experiments suggest that this model outperforms other traditional prediction methodologies in terms of AUC and sensitivity values over a range of false positives with greater applicability in real life.
A framework to account for structural damage, functional efficiency and reparation costs within the optimal design of countermeasures: Application to snow avalanche risk mitigation
(2022) Favier, Philomène; Eckert, Nicolás; Faug, Thierry; Bertrand, David; Ousset, Isabelle; Candia, Gabriel; Llera, Juan Carlos de la
In mountain areas, long-term snow avalanche risk evaluation is of paramount importance for land use planning. In avalanche-prone areas, when real estate demand is high, for instance, building protective structures may be a sensible choice for reaching a compromise between safety and development. Specifically, minimizing the risk within a quantitative framework can provide optimal defense structure configurations (size, localization, construction technology, etc.). However, existing approaches based on a proper theoretical decision-making framework still suffer from limitations making them hardly usable in practice. It is herein proposed to account for the physical, functional, and monetary dimensions of a protective measure within the assessment of total risk. Total risk, which is calculated as the mean expected loss, is quantified within a four-state system in which the failure of the dam and the failure of the dwellings to be protected are assessed with specific vulnerability relations. Bounds for the risk and subsequent optimal dam design values are quantified using minimum and maximum (min/max) functional efficiency relations of the dam. Additional assumptions regarding the functional-structural efficiency relation allow for the optimal design and corresponding minimum risk to be reached. An application is proposed with a case study from the French Alps. A comprehensive parametric study shows that the min/max bounds risk quantification is worth implementing in some cases, such as, for instance: if there is a high uncertainty of the functional efficiency of the dam, of if the assets to be protected have a monetary value. However, when the failure of the dam is unlikely to occur (due to its location or to its material resistance), it is shown that quantification of the intermediate risk without the min/max bounds approach is sufficient. In the future, the framework could be extended to many other mountain hazards (debris flows, landslides, etc.), more complex elements at risk, and even to problems going beyond the sole question of land-use planning such as traffic road regulation.
A Framework to Understand Attitudes towards Immigration through Twitter
(2021) Freire-Vidal, Yerka; Graells-Garrido, Eduardo; Rowe, Francisco
Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.
A home hospitalization assignment and routing problem with multiple time windows, mandatory returns and perishable biological samples: A Chilean case study
(2024) Varas, Mauricio; Baesler, Felipe; Basso, Franco; Contreras, Juan Pablo; Pezoa, Raúl; Rojas-Goldsack, María Francisca
The increase in life expectancy and formal care has fostered the demand for home care services, including home hospitalization. For this service, decision-makers must allocate the staff and route the visits as efficiently as possible. To tackle this problem, in this paper, we devise a new mixed-integer programming formulation that incorporates several industry-specific features, including matching patients to medical specialties and synchronized visits of multiple specialists. Moreover, the proposed formulation also includes three features that have not been tackled simultaneously in the previous literature: multiple time windows, mandatory lunch breaks at the hospital, and fast delivery of perishable biological samples. The proposed model can be reduced to a vehicle routing problem with multiple times windows, known as NP-hard. Therefore, for solving large instances, we design a heuristic procedure composed of a constructive heuristic coupled with an improvement heuristic, which builds on a local branching scheme. To test the applicability of our approach, we conduct a case study focusing on the actual operations of Hospital Padre Hurtado of Santiago, Chile. Our computational experiments show that the model provides fully implementable solutions. Moreover, the heuristic procedure provides high-quality routes (regarding quality and solution times), making it a promising alternative to experience-based scheduling methods and state-of-the-art solvers.
A horizontal collaborative approach for planning the wine grape harvesting
(2022) Basso, Franco; Varas, Mauricio; Bosch, Paul; Contreras, Juan Pablo; Pezoa, Raúl
Horizontal collaboration is a strategy that has increasingly been used for improving supply chain operations. In this paper, we analyze the benefits of using a collaborative approach when optimally planning the wine grape harvesting process. Particularly, we assess how labor and machinery collaborative planning impacts harvesting costs. We model cooperation among wineries as a coalitional game with transferable costs for which the characteristic function vector is computed by solving a new formulation for planning the wine grape harvesting. In order to obtain stable coalitions, we devise an optimization problem that incorporates both rationality and efficiency conditions and uses the Gini index as a fairness criterion. Focusing on an illustrative case, we develop several computational experiments that show the positive effect of collaboration in the harvesting process. Moreover, our computational results reveal that the results depend strongly on the fairness criteria used. The Gini index, for example, favors the formation of smaller coalitions compared to other fairness criteria such as entropy.
A mathematical basis for the graphene
(2020) Conca, C.; San Martín, J.; Solano, Viviana
We present a new basis of representation for the graphene honeycomb structure that facilitates the solution of the eigenvalue problem by reducing it to one dimension. We define spaces in these geometrical basis that allow us to solve the Hamiltonian in the edges of the lattice. We conclude that it is enough to analyze a one-dimensional problem in a set of coupled ordinary second-order differential equations to obtain the behavior of the solutions in the whole graphene structure
A multi-objective approach for supporting wine grape harvest operations
(2020) Varas, Mauricio; Basso, Franco; Maturana, Sergio; Osorio, David; Pezoa, Raúl
In this paper, we present a novel multi-objective mixed-integer linear programming model to support wine grape harvesting. The proposed model considers the opposing nature of operational cost minimization and grape quality maximization, subject to several constraints, such as grape requirements and routing decisions. Based on the operations of a winery we worked with, we develop a negotiation protocol that can lead to an agreed final harvest schedule. The protocol includes an initial Pareto optimal solution obtained through the augmented weighted Tchebycheff method. Then, the solutions are presented to the two decision-makers and, if no agreement is reached, we conduct an iterative process, which includes finding Pareto optimal solutions in a neighborhood using the augmented ∊ -constraint method. Finally, we choose, within this set, the solution following a substitution rate criteria. We illustrate our procedure using an educational example.
A Multiobjective Model for the Cutting Pattern Problem with Unclear Preferences
(2016) Palma, Cristian D.; Vergara, Francisco P.
The cutting pattern problem has been traditionally approached using single objective optimization models, although the sawmill performance is usually measured using more than a single indicator. One of the shortcoming of using multiobjective approaches is that they need a preference relationship among the objectives, which is difficult to determine in practice, and solutions are very sensitive to these preferences. In this article, we consider different criteria in a sawmill decisionmaking context using a multiobjective linear optimization model and handle the unclear definition of the objective preferences by formulating a robust version of the model. Although the deterministic formulation assumes perfect information of the objective preferences, in the robust formulation we consider that preferences may be different from their estimate. We show that deterministic decisions are more balanced in terms of the different criteria than the traditional single objective models, although their quality is very sensitive to the objective preferences. We also show that robust decisions are also balanced but less sensitive to the preferences. We explore how the level of the different indicators and the cutting decisions are affected when the preferences are unclear.
A New Genetic Algorithm Encoding for Coalition Structure Generation Problems
(2020) Contreras, Juan Pablo; Bosch, Paul; Varas, Mauricio; Basso, Franco
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.
A New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries
(2023) Gúzman Montoto, José Ignacio; Herrera Marín, Mauricio René; Rodríguez Beltrán, Camilo Ignacio
This study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA.

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