Fondos para apoyar la realizacion de la Fourth Latin American Summer School on Robotics (LACORO 2025). La primera edición se realizó online en octubre de 2020; la segunda fue presencial en enero de 2023; la tercera 2024 en la Universidad de O'Higgins en Rancagua, Chile. La cuarta edición tendrá lugar en diciembre de 2025 en la Universidad de O'Higgins. https://lacoro.org/ Esta Escuela de Verano beneficiará principalmente a Estudiantes y Académicos de las Américas interesados en la Investigación en Inteligencia Artificial aplicada a la Robótica. Nuestro objetivo es fomentar la colaboración nacional y regional en esta área de investigación. Para la primera edición, alcanzamos 241 inscripciones para actividades online de todo el mundo, y la segunda versión tuvo 166 inscripciones para actividades presenciales en enero de 2023, principalmente de Chile, México, Argentina, Brasil y Uruguay.

Fondos para apoyar la realizacion de la Fourth Latin American Summer School on Robotics (LACORO 2025). La primera edición se realizó online en octubre de 2020; la segunda fue presencial en enero de 2023; la tercera 2024 en la Universidad de O'Higgins en Rancagua, Chile. La cuarta edición tendrá lugar en diciembre de 2025 en la Universidad de O'Higgins. https://lacoro.org/ Esta Escuela de Verano beneficiará principalmente a Estudiantes y Académicos de las Américas interesados en la Investigación en Inteligencia Artificial aplicada a la Robótica. Nuestro objetivo es fomentar la colaboración nacional y regional en esta área de investigación. Para la primera edición, alcanzamos 241 inscripciones para actividades online de todo el mundo, y la segunda versión tuvo 166 inscripciones para actividades presenciales en enero de 2023, principalmente de Chile, México, Argentina, Brasil y Uruguay.

Combinatorial objects frequently appear in various areas of computer science and discrete mathematics. These objects are central to questions in algorithmic design, where we aim to program a computer to efficiently perform tasks involving them. These tasks may include counting objects based on certain parameters, sampling an object uniformly at random, optimizing with respect to an objective function, searching for objects that satisfy specific properties, or generating all objects exactly once. This project focuses on two of these problems: combinatorial generation and the search for highly distinct combinatorial objects. While many of the aforementioned tasks have general-purpose techniques that allow them to tackle multiple problems simultaneously, the situation becomes less clear when dealing with combinatorial generation or the search for distant objects. Much of the effort in these areas has been devoted to developing ad hoc methods. Despite this, these last two problems can be naturally phrased in the language of flip graphs, which encode the similarity between combinatorial objects. In this context, the problem transforms into the traditional graph problems of Hamiltonicity (finding a path that traverses all the vertices exactly once) and diameter (finding two vertices that are farthest apart). Recent research has highlighted the significant value of exploiting polytopal properties and symmetry of flip graphs, leading to unified frameworks that can address many problems simultaneously. The main objective of this project is to contribute to this perspective. Specifically, it aims to enhance our understanding of the polytopal and symmetric properties of flip graphs and use this knowledge to develop efficient algorithms for tackling Hamiltonicity and diameter problems

Plants, with their two-layered immune system, are equipped to combat pathogen invasion. The first layer, Pattern Triggered Immunity (PTI), is a powerful defense mechanism. It relies on Pattern Recognition Receptors (PRRs) to detect Microbe-Associated Molecular Patterns (MAMPs) from microbes, triggering a robust defense response. This response, including signaling cascades, gene expression changes, and production of antimicrobials and defense hormones, contributes to restricting pathogen colonization. PTI activation can trigger a systemic response known as Induced Systemic Resistance (IRS), enhancing plant defenses throughout the organism and leading to Non-Host-Resistance. The potential of PTI activation to enhance a plant's overall defensive capacity is a promising strategy to improve crop health. PTI activation at infection sites triggers the production of mobile signals within the plant, which then spread IRS throughout the plant, enhancing its overall defensive capacity. Flg22 and xyn11, two well-known MAMPs, trigger PTI in tomato, activating various defense responses and, interestingly, including IRS in tomatoes and other plants. Plant roots, often overlooked in discussions of plant immune systems, possess their own immune system, though less potent than leaves. They respond to MAMPs like Flg22 and chitin, but with weaker production of defense chemicals. Despite this difference, roots activate various defenses like PR proteins and callose deposition. Uniquely, roots secrete antifungal secondary metabolites like flavonoids. These root exudates play a crucial role in shaping the surrounding microbiome, attracting beneficial microbes, and possess antimicrobial activity itself. Studies have shown that root exudate composition can be manipulated to influence the soil microbiome and potentially enhance plant growth. This underlines the importance of considering roots in our understanding of plant immune systems, particularly how defense responses are displayed in the root after immune activation in leaves in terms of a systemic immune response. This often overlooked aspect is crucial for a comprehensive understanding of plant immunity. Plants and microbes communicate two-way, establishing an interaction, by instance, plant root exudates influence the composition of the rhizosphere microbiome, which in turn regulates plant growth and immunity. Research suggests that specific bacteria within the rhizosphere microbiome can enhance plant immunity. In fact, transplanting the microbiome from a resistant tomato variety to a susceptible one improved disease resistance. Understanding this plant-microbiome-soil interaction is crucial for developing sustainable agriculture. Our ongoing research investigates how soil type influences tomato immunity and its connection to the soil microbiome. Preliminary results show that different soil types affect the strength of plant immunity responses, even though the overall bacterial types (phyla) are similar. Interestingly, specific bacterial isolates from a soil type with higher immunity were able to directly trigger plant defense mechanisms. Unraveling the intricate interplay between soil type, the rhizosphere microbiome, and tomato immunity holds the key to unlocking sustainable and resilient agricultural practices. This proposal aims to investigate the potential of targeted Pattern-Triggered Immunity (PTI) activation in tomato leaves to enhance plant defense against diverse pathogens. We hypothesize that leaf application of microbial elicitors (flg22 and Xyn11) will trigger PTI, leading to changes in root gene expression and root exudate composition. These alterations are expected to enrich beneficial bacteria in the rhizosphere microbiome, ultimately enhancing resistance against both the foliar pathogen Pseudomonas syringae pv. tomato and the soil-borne pathogen Fusarium oxysporum f.sp. lycopersici. To achieve this, we have defined three specific objectives: 1) Evaluate the impact of leaf-applied elicitors on pathogen susceptibility, root gene expression, root exudate composition, and soil microbiome composition. 2) Develop synthetic exudates mimicking PTI-activated plants and construct synthetic microbial communities potentially containing beneficial bacteria. 3) Assess the effectiveness of leaf-applied elicitors and synthetic microbial communities on the root microbiome and plant health under field conditions. With this, we aim to elucidate the mechanisms by which leaf-based PTI activation influences root-level processes and shapes the rhizosphere microbiome to enhance tomato plant defense against various pathogens. The findings hold promise for developing novel and sustainable strategies for disease management in tomato production.

The primary objective of this research is to evaluate the feasibility of using ultrasonic acoustic imaging as a non-intrusive, in situ technique to assess the plastic behavior of commercial metals and alloys. Specifically, it aims to explore the potential of ultrasonic acoustic imaging to identify and monitor various plastic deformation mechanisms in stainless steel and aluminum. The selection of materials is based on their distinct plastic deformation behaviors: aluminum releases internal energy through dislocation mechanisms, while stainless steel releases energy through deformation, first by dislocation and then by twinning. To achieve this goal, the study will continuously measure changes in sound velocity and the nonlinear acoustic parameter β while subjecting the materials to uniaxial tensile tests at different levels of applied stress. Previous studies conducted by our research group have demonstrated that changes in sound velocity, in relation to strain, offer a reliable means of quantifying dislocation density in local measurements on aluminum, copper, and stainless steel specimens. Furthermore, these studies have observed that alterations in the nonlinear acoustic parameter, specifically second harmonic generation, exhibit more pronounced changes compared to variations in linear acoustics (speed of sound). Building upon these findings, the proposed research involves the generation of both linear and nonlinear acoustic images over wider spatial regions to advance our understanding of the plastic behavior of materials undergoing different microstructural changes. The challenge of applying the results of this research to in situ measurements in the industry is not trivial, as the highly controlled laboratory conditions are not maintained in service components. In this regard, the incorporation of machine learning tools in the proposal aims to identify the parameters most sensitive to the various deformation mechanisms through clustering techniques. It is expected that the correlation of different acoustic parameters with the various plastic deformation mechanisms of both materials under study will generate an optimal database that reflects the variety of scenarios present in service components, thus paving the way for the industrial use of the proposed characterization system. The adoption of diagnostic techniques and the utilization of metallic material state analysis in service significantly enhance our ability to comprehend and control plastic deformation mechanisms, contributing to improved material reliability and robustness, and facilitating informed decision-making and maintenance strategies. Additionally, ex-situ standard microstructural tests, including XRD (X-ray diffraction), EBSD (electron backscatter diffraction), and TEM (transmission electron microscopy), will be performed to characterize the material’s state after deformation. These complementary tests will provide valuable microstructural information, enabling the correlation of deformation states with the acquired acoustic images. All the acoustic and microstructural information described above, in conjunction with previous research group data, will be stored in a robust and comprehensive database. This database will serve as the input for a Machine Learning algorithm, which will facilitate the identification of patterns of correspondence between acoustic and microstructural parameters. This approach aims to enable the future prediction, with a high level of probability, of the specific type of plastic deformation mechanism that a material is undergoing based on the acoustic parameter measurements. The successful development of this research proposal would yield several significant outcomes. Firstly, it would enable the early detection of microstructural changes in materials long before fractures occur. Moreover, it would establish a non-intrusive tool for characterizing materials by identifying the underlying mechanisms driving plastic deformation and monitoring the evolution of materials in service over time. Ultimately, this research has the potential to advance our understanding of the plastic behavior of stainless steel and aluminum, opening avenues for improved analysis, design, and performance evaluation of materials in various industrial applications.

Medium manganese steels (MMnS) are currently a subject of active scientific research due to a number of reasons. First, their unique combination of strength and ductility makes them promising candidates for lightweight structural applications in automotive and aerospace industries, where reducing weight without sacrificing mechanical properties is critical. Second, their ability to retain austenite at room temperatures offers advantages in terms of formability and resistance to hydrogen embrittlement, which are significant challenges in steel manufacturing. Third, medium Mn steels have shown potential in enhancing wear and impact resistance, making them suitable for applications in mining, construction, and machinery sectors. Additionally, their corrosion resistance and potential for cost-effective alloying with other elements further expand their utility across various engineering fields. Scientific research on medium Mn steels aims to optimize their microstructure, processing parameters, and alloy compositions to unlock their full potential, thereby contributing to the development of advanced materials that meet the performance requirements of modern industries while promoting sustainability and efficiency in manufacturing processes. The proposed research aims to investigate the stability of austenite in medium manganese steels within ternary Fe-C-Mn and Fe-C-Mn-X systems (X: Al, Si, Cr), focusing on its correlation with processing parameters. The primary objective is to assess the stability of austenite via (i) experimentally determining the martensite start temperature (thermal stability) using dilatometry and thermal analysis techniques, and (ii) to evaluate the fraction of austenite as a function of strain (mechanical stability) under tensile test. These measurements will provide crucial data to understand how variations in processing conditions influence austenite stability. Else, the study will correlate austenite stability with mechanical properties through mechanical tests and in-depth microstructural characterization, aiming to establish predictive models. Additionally, thermodynamic and kinetic calculations will aid in assessing the phase transformation behavior under different thermal histories. The research will extend its scope to evaluate impact and wear properties in relation to austenite stability, crucial for applications in industries requiring high strength and toughness, such as mining and construction. By systematically exploring these relationships, the project seeks to advance the fundamental understanding of medium Mn steels, potentially leading to the development of lightweight, durable materials with enhanced performance characteristics. Ultimately, the findings aim to contribute to the optimization of steel manufacturing processes and the realization of more efficient and reliable engineering solutions in demanding operational environments