Artículos de conferencia

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A new multi-spring element to simulate clt connections under combined loadings
(2023) Chacon De La Cruz, Matías Fernando Nicolás; Guindos Bretones, Pablo; Montano, J.
A new numerical model of five uncoupled spring elements is proposed to simulate the panel-to-panel Cross-Laminated Timber (CLT) connections made of discrete fasteners. The model accurately simulates the main phenomenological effects in the translational and rotational directions of connections: asymmetrical axial gap (open/closure), in-plane and out-of-plane sliding of panels, and radial/bending stiffness/strength of fasteners. Linear/nonlinear spring elements with different constitutive material models for each phenomenological effect are considered. For instance, a modified Richard-Abbott model is used for the radial/bending hysteresis of fasteners. The model is implemented in the ANSYS software through user elements and material routines. An illustrative example under different loading conditions is elaborated to show the main capabilities of the proposed model. Finally, the model is validated with two experimental monotonic in-plane CLT slab tests, where the initial/hardening stiffness, strength capacity, and failure mechanism of both specimens are correctly predicted.
Absolute Expressiveness of Subgraph-Based Centrality Measures
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2023) Pieris, Andreas; Salas Cornejo, Jorge Eduardo
In graph-based applications, a common task is to pinpoint the most important or “central” vertex in a (directed or undirected) graph, or rank the vertices of a graph according to their importance. To this end, a plethora of so-called centrality measures have been proposed in the literature. Such measures assess which vertices in a graph are the most important ones by analyzing the structure of the underlying graph. A family of centrality measures that are suited for graph databases has been recently proposed by relying on the following simple principle: the importance of a vertex in a graph is relative to the number of “relevant” connected subgraphs surrounding it; we refer to the members of this family as subgraph-based centrality measures. Although it has been shown that such measures enjoy several favourable properties, their absolute expressiveness remains largely unexplored. The goal of this work is to precisely characterize the absolute expressiveness of the family of subgraph-based centrality measures by considering both directed and undirected graphs. To this end, we characterize when an arbitrary centrality measure is a subgraph-based one, or a subgraph-based measure relative to the induced ranking. These characterizations provide us with technical tools that allow us to determine whether well-established centrality measures are subgraph-based. Such a classification, apart from being interesting in its own right, gives useful insights on the structural similarities and differences among existing centrality measures.
Advancements in MELD Score and Its Impact on Hepatology
(Thieme Medical Publishers, Inc., 2024) Hudson, David; Valentin Cortez, Francisco Javier; Hurtado Díaz de León, Ivonne; Malhi, Gurpreet; Rivas, Angélica; Afzaal, Tamoor; Rad, Mahsa Rahmany; Díaz Piga, Luis Antonio; Khan, Mohammad Qasim; Arab Verdugo, Juan Pablo
There continues to be an ongoing need for fair and equitable organ allocation. The Model for End-Stage Liver Disease (MELD) score has evolved as a calculated framework to evaluate and allocate patients for liver transplantation objectively. The original MELD score has undergone multiple modifications as it is continuously scrutinized for its accuracy in objectively representing the clinical context of patients with liver disease. Several refinements and iterations of the score have been developed, including the widely accepted MELD-Na score. In addition, the most recent updated iteration, MELD 3.0, has been created. The MELD 3.0 calculator incorporates new variables such as patient sex and serum albumin levels and assigns new weights for serum sodium, bilirubin, international normalized ratio, and creatinine levels. It is anticipated that the use of MELD 3.0 scores will reduce overall waitlist mortality and enhance access for female liver transplant candidates. However, despite the emergence of the MELD score as one of the most objective measures for fair organ allocation, various countries and healthcare systems employ alternative methods for stratification and organ allocation. This review article will highlight the origins of the MELD score, its iterations, the current MELD 3.0, and future directions for managing liver transplantation organ allocation.
An integrated model for rapid and slow transit network design
(2021) Gonzalez-Blanco N.; Mesa J.A.; Marianov Kluge, Vladimir; Mesa, Juan A.
Usually, when a rapid transit line is planned a less efficient system already partially covers the demand of the new line. Thus, when the rapid transit starts its regular services, the slow mode (e.g. bus lines) have to be cancelled or their routes modified. Usually this process is planned according to a sequential way. Firstly, the rapid transit line is designed taking into account private and public flows, and possibly surveys on mobility in order to predict the future utilization of the new infrastructure and/or other criteria. Then, in a second stage, the bus route network is redesigned. However, this sequential process can lead to a suboptimal solution, for which reason in this paper a cooperative model for rapid and slow transit network design is studied. The aim is to design simultaneously both networks and the objective is to maximize the number of passengers captured by both public modes against the private mode. We present a mathematical programming formulation and solve the problem by an improved Benders decomposition approach.
Automatic knowledge-graph creation from historical documents: The Chilean dictatorship as a case study
(2024) Díaz, Camila; Dunstan Escudero, Jocelyn Mariel; Etcheverry, Lorena; Fonck Larraín, Antonia; Grez, Alejandro; Mery Quiroz, Domingo Arturo; Reutter de la Maza, Juan Lorenzo; Rojas, Hugo
We present our results regarding the construction of a knowledge graph from historical documents related to the Chilean dictatorship period (1973-1990). Our approach uses LLMs to automatically recognize entities and relations between them and resolve conflicts between these values. To prevent hallucination, the interaction with the LLM is grounded in a simple ontology with four types of entities and seven types of relations. To evaluate our architecture, we use a gold standard graph constructed using a small subset of the documents, and compare this to the graph obtained from our approach when processing the same set of documents. Results show that the automatic construction manages to recognize a good portion of all the entities in the gold standard and that those not recognized are explained mainly by the level of granularity in which the information is structured in the graph and not because the automatic approach misses an important entity in the graph. Looking forward, we expect this report to encourage work on other similar projects focused on enhancing research in humanities and social science. However, we remark that better evaluation metrics are needed to accurately fine-tune these types of architectures.
CAI Asynchronous Methodology for Emergency Remote Teaching: An Experience in Introduction to Programming
(IEEE Computer Society, 2021) Muñoz Gama, Jorge; Salas-Morales J.; Herskovic V.
CLNews: The First Dataset of the Chilean Social Outbreak for Disinformation Analysis
(Association for Computing Machinery, 2022) Providel, Eliana; Toro, Daniel; Riquelme, Fabián; Mendoza Rocha, Marcelo Gabriel; Puraivan, E.
Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural propagation data is low. This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified claims checked during the Chilean social outbreak, which allows for studying the phenomenon of disinformation during this crisis. We study propagation patterns of verified content in CLNews, showing differences between false rumors and other types of content. Our results show that false rumors are more persistent than the rest of verified contents, reaching more people than truthful news and presenting low barriers of readability to users. The dataset is fully available and helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.
Collaborative health resources management model
(IEOM Society, 2021) Gutierrez, Janus, Amaru Leonhardt; Tabilo, Macarena Paz Navarro; Klapp Belmar Mathias Alberto
© IEOM Society Inweternational.In Chile, rural population faces long access times to specialized health services, as these tend to be concentrated in regional capitals. Against this background, the present study proposes the establishment of a Collaborative Health Resources Management Model, based on the practice of sharing hospital resources between medical centers, allowing daily transfers of doctors and medical equipment within a hospital network. The objective is to meet local demand and increase rural accessibility to healthcare, in terms of reducing patient transfers between hospitals and access times. To compare the current and proposed scenarios, an Integer Optimization Model with a Network Structure is formulated. The case study is focused on urology specialty in Zona del Reloncaví, a rural area located in southern Chile, on which it is concluded that allowing the mobility of hospital resources, specifically doctors and equipment, is optimal, as the average number of patient transfers and the average access time are reduced by 95.6% and 83%, respectively. By applying this model, hospitals are able to stretch their limited capital budgets by jointly purchasing equipment, providing network-wide access to resources needed for expanding healthcare accessibility.
Comparative Evaluation of Partially-Rated Energy Storage Integration Topologies for High Voltage Modular Multilevel Converters
(Institute of Electrical and Electronics Engineers Inc., 2022) Blatsi, Zoe; Neira, Sebastián; Finney, Stephen; Merlin, Michael M. C.
© 2022 EPE Association.This paper compares three partially-rated MMC topologies (Partially Rated Storage - PRS, Stack Parallel Branch - SPB, Inductor Parallel Branch - IPB) which integrate energy storage solutions for HVDC-scale Modular Multilevel Converters to provide with extra degrees of flexibility in the grid. The paper compares (i) the ES power that can be contributed from each topology under a given converter design and (ii) the trade-offs in terms of losses, and control adaptation required on top of a standard half-bridge MMC design in order to provide ES power. The results indicate that MMC stacks with full-bridge submodules have consistently higher ES power capability than their half-bridge counterparts - this comes at the expense of higher losses and extra devices, which for certain applications could be avoided.
Control of ciliary movement in mammalian oviductal ciliated cells
(1991) Villalon M.; Verdugo P.
CORE: a Complex Event Recognition Engine
(HUMANA PRESS INC, 2022) Bucchi M.; Grez A.; Quintana A.; Riveros C.; Vansummeren S.
© 2022, VLDB Endowment.Complex Event Recognition (CER) systems are a prominent technology for finding user-defined query patterns over large data streams in real time. CER query evaluation is known to be computationally challenging, since it requires maintaining a set of partial matches, and this set quickly grows super-linearly in the number of processed events. We present CORE, a novel COmplex event Recognition Engine that focuses on the efficient evaluation of a large class of complex event queries, including time windows as well as the partition-by event correlation operator. This engine uses a novel automaton-based evaluation algorithm that circumvents the super-linear partial match problem: under data complexity, it takes constant time per input event to maintain a data structure that compactly represents the set of partial matches and, once a match is found, the query results may be enumerated from the data structure with output-linear delay. We experimentally compare CORE against state-of-the-art CER systems on real-world data. We show that (1) CORE’s performance is stable with respect to both query and time window size, and (2) CORE outperforms the other systems by up to five orders of magnitude on different workloads.
Design Process of Extended Reality Educational Resources in Engineering: A Comparison of Three Cases in Latin American Universities
(HUMANA PRESS INC, 2022) Lara-Prieto V.; Ruiz-Cantisani M.I.; Ramirez-Robles L.A.; Mourgues C.; Pinzon-Salcedo L.A.; Bernal-Alvarado J.
© 2022 IEEE.The Covid-19 pandemic demanded educators to explore new methods to provide their students with practical activities, despite the lockdown measures around the world. The use of Extended Reality (XR) technology, which includes Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), offers special tools to transform the learning experience in engineering education with immersive environments. This study presents a comparison of the process followed to design XR resources for engineering education applications at three different universities in Latin America. The motivation of this work is to share the design experience, as well as the lessons learned and recommendations for educators that are looking into adopting XR technology in their courses. Even though the group of professors worked together along the project, the design of the immersive experience and the XR resource was different for each university according to its specific context and needs. The design process comprises the resource conceptual definition, the technological development, and the final product with academic use. The three design processes were compared to identify lessons learned, best practices, and recommendations for future XR resources in engineering education. XR technologies have a great potential in engineering education closing the gap between the classroom and the real-life practice using the principles of educational innovation.
Development and Operationalisation of Local Sustainability Indicators - A Global South Perspective on Data Challenges and Opportunities for GIScience
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2023) Steiniger, Stefan; Rojas Quezada, Carolina Alejandra; Truffello Robledo, Ricardo Enrique; Barton Jonathan, Richard
Evaluating and monitoring the sustainable development of nations and cities requires sets of indicators. Such indicator sets should measure equity, health, environmental, or governmental progress or recess - among other sustainability aspects. In 2015 the United Nations ratified 17 Sustainable Development Goals (SDG) assessed through 231 indicators. However, other - local - sets of indicators have been developed too. In this paper we review geodata challenges that emerged when we developed four sustainability indicator sets in Chile. Faced challenges include (geo)data availability and data representativeness, among others. We analyse how GIScience knowledge has contributed to indicator development and outline three priority research topics: (i) updating indicators based on automated processes, while respecting representativeness, (ii) tools for planning scenario generation, and (iii) methods for short- and long-term forecasting.
Economic and Environmental Impact of Military Electrical Vehicle Conversion and Solar Electricity Production
(2020) Pierre Simon, François; Roberts, C.; Girard, Aymeric; Ordoñez, Javier; Rossel, G.S.; CEDEUS (Chile)
Military vehicles carry out defence and training functions; however, they also participate extensively in military operations apart from war, such as supporting citizens in emergency situations. Nonetheless, they continue to contribute to pollution, and carbon emissions in Chile have doubled in recent years. Thus, this study seeks a partial solution to this problem, extending the useful life of an archetypal military vehicle, i.e. AIL Storm internal combustion engine vehicle (ICEV), 4.0L gasoline, through its conversion into an electric vehicle (EV) using lithium-ion batteries. AIL Storm ICE emissions are compared with an equivalent EV conversion prototype, analysing the CO emissions of the EV charged from the Chilean electricity grid or from a solar photovoltaic charging station, taking advantage of the high levels of solar radiation in Chile. This studied model obtains a travel cost of 0.04USD/km in the electric grid charge mode and 0.02USD/km in the Solar power station charge mode, affecting the environment with 155kgCO2 emissions per year of use. In the future, technologies to manufacture or assemble lithium batteries must be developed in the Chilean market. Given the availability of this mineral in Chile, this step would reduce costs and make electric mobility more attractive and affordable.
Evaluación por simulación de estrategias de control automático para fermentadores industriales de cerveza
(Institute of Electrical and Electronics Engineers Inc., 2024) Márquez Giordani, Cristóbal Alberto; Saa Higuera, Pedro; Pérez Correa, José Ricardo
The operation and control of beer fermentation at an industrial scale (> 3000 hL) pose significant challenges due to the dynamic nature of the process and the large reactor volumes involved. Effective control of fermentation temperature, a critical parameter, is hindered by the lack of advanced control models that consider volume heterogeneity. To address this, we implemented a compartmentalized phenomenological model to simulate the fermentation process, focusing on temperature control in the reactor. The model was utilized to compare two control strategies: a conventional Proportional-Integral-Derivative (PID) control system and a Model Predictive Control (MPC) framework, both designed to track a reference temperature profile. Simulation results highlight significant temperature variations across reactor compartments, necessitating the adoption of spatially aware control strategies. The MPC-based control system demonstrated superior performance compared to the PID approach, offering more precise tracking of the temperature reference in all compartments. These findings underscore the potential of MPC for optimizing temperature control in large-scale fermentation processes. Future work will focus on the industrial validation of the model and the control strategies for implementation in operational settings.
Expected First Occurrence Time of Uncertain Future Events in One-Dimensional Linear Systems
(Prognostics and Health Management Society, 2024) Acuña Ureta, David Esteban; Fuentealba Secul, Diego Ignacio; Orchard, Marcos E.
The rapid advancement of machine learning algorithms has significantly enhanced tools for monitoring system health, making data-driven approaches predominant in Prognostics and Health Management (PHM). In contrast, model-based approaches have seen modest progress, as they are often constrained by the need for prior knowledge of specific governing equations, limiting their applicability to a wide range of problems. Recently, rigorous theoretical foundations have been established to extend dynamical systems theory by incorporating prognosis of uncertain events. This article leverages this formal framework to introduce and demonstrate a fundamental mathematical result for one-dimensional linear dynamical systems. The presented theorem offers an analytical expression for approximating the expected time at which an event will first occur in the future. Unlike typical thresholds, this event is triggered by a hazard zone, defined as an uncertain event likelihood function over the system’s state space. Applications of this theorem can be found in implementing real-time prognostic frameworks, where it is crucial to quickly estimate the magnitude of impending failures. Emphasis is placed on minimizing computational burden to facilitate prognostic decision-making.
Exploring the Effects of Applying Learning Analytics for Teaching Procedural Skills in Health Sciences Education
(2021) Villagrán Gutiérrez, Ignacio Andrés
In Health Science Education (HSE), students must demonstrate technical skills in many procedures. However, traditional teaching methodologies limit the possibilities for personalized feedback from an instructor and generally do not allow students to achieve the required proficiency levels. Concerning this, technology has been installed as a learning resource with numerous benefits that allow new multidisciplinary lines of research, such as learning analytics (LA) and educational data mining (EDM). Both LA and EDM seek to improve educational practice based on the intensive use of educational data, such as analyzing online learning patterns, creating performance predictions, and incorporating artificial intelligence techniques. There is a range of possibilities in applying LA and EDM in teaching procedural skills that have not yet been explored. For this reason, this study aims to answer (1) How the educational data explain the development of procedural skills in virtual environments to support the teaching-learning process in Health Science Education?; and (2) How automatic feedback and adaptive personalization affect the performance and instructional design of the procedural skills learning process of health science students? We expect that this study contributes to the field of technical skills learning mediated by technology in higher education by using the data provided by the interaction of students with virtual resources to support educational decision-making and optimize the teaching and learning processes in HSE.
Hyperbolic Optimizer as a Dynamical System
(ML Research Press, 2024) Alvarado Monardez, Nicolas Felipe Jesús; Lobel Díaz, Hans Albert
During the last few years, the field of dynamical systems has been developing innovative tools to study the asymptotic behavior of different optimizers in the context of neural networks. In this work, we redefine an extensively studied optimizer, employing classical techniques from hyperbolic geometry. This new definition is linked to a non-linear differential equation as a continuous limit. Additionally, by utilizing Lyapunov stability concepts, we analyze the asymptotic behavior of its critical points.
Inspecting the concept knowledge graph encoded by modern language models
(Association for Computational Linguistics (ACL), 2021) Aspillaga, Carlos; Soto, Alvaro; Mendoza Rocha, Marcelo Gabriel
The field of natural language understanding has experienced exponential progress in the last few years, with impressive results in several tasks. This success has motivated researchers to study the underlying knowledge encoded by these models. Despite this, attempts to understand their semantic capabilities have not been successful, often leading to non-conclusive, or contradictory conclusions among different works. Via a probing classifier, we extract the underlying knowledge graph of nine of the most influential language models of the last years, including word embeddings, text generators, and context encoders. This probe is based on concept relatedness, grounded on WordNet. Our results reveal that all the models encode this knowledge, but suffer from several inaccuracies. Furthermore, we show that the different architectures and training strategies lead to different model biases. We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging. We hope our insights will motivate the development of models that capture concepts more precisely.
International collaboration between Latin American universities towards educational innovation in engineering: Case study
(HUMANA PRESS INC, 2021) Ruiz-Cantisani M.I.; Lara-Prieto V.; Lima-Sagui F.D.C.; Mourgues C.; Pinzon-Salcedo L.A.
© 2021 ACM.Collaboration between universities has become a driving force to develop and promote a culture of educational innovation with technology and innovative pedagogical strategies. This research exposes the case study of three Latin American universities that seek to identify problems or educational situations to be addressed and developed innovation proposals in exploring in the solution, through an international collaborative initiative with teams of research professors focused on developing educational innovations that inspire and train future professionals. The case study methodology has three phases: 1) case design, 2) case description and 3) case evaluation. The case is analyzed from two perspectives: international initiative managers and the faculty who collaborate in research with their colleagues from other universities using the funds for the awarded projects. The challenges presented are many: from the generation of faculty teams, communication and collaboration at a distance, differences in structure and schedules between universities, and more. However, the benefits have been equally many as synergies have been created not only between universities and within the institutions themselves, but also with companies; another benefit is the learning and knowledge generated by the collaboration is impacting the students, who are the ultimate goal and inspiration of all academic endeavors.

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