Technological University of Pereira

Clima y Caf & eacute; [Climate and Coffee] supports Colombian coffee farmers' adaptation to climate change. The project finds and makes adaptation information available in accessible ways for farmers, while drawing attention to the crisis on a regional, national, and international scale. The website was built through iterative design testing with farmers and is targeted toward local associations that have internet access, are trusted information dissemination hubs, and are reported as central in conversations. Complementary forms of outreach redress internet connectivity challenges facing many farmers and include printed materials, radio segments, podcasts, WhatsApp videos, and in-person seminars. This field report reviews the project's development since its origins in 2018, providing a potential blueprint for other engaged research projects promoting environmental and agricultural outcomes. The report moves chronologically through the project's lifespan, beginning with the formative research that informed its design and moving through project development, external partnerships, leadership transitions, and change management.

This paper proposes a horizon-one nonlinear model predictive control (MPC) strategy for permanent magnet synchronous motors that combines formal stability guarantees with real-time implementability. The controller is derived directly from the original bilinear discrete-time model, avoiding linear approximations commonly used in motor-drive applications. Despite the nonlinear formulation, the associated optimal control problem admits a closed-form analytical solution with a unique global optimum, enabling efficient implementation on resource-constrained microcontrollers. Adaptivity is achieved through a recursive nonlinear identification scheme and a disturbance observer, which allow online estimation of uncertain parameters and mechanical torque without direct torque measurements. Closed-loop stability is established using discrete-time Lyapunov theory, and sufficient conditions for asymptotic stability and region-of-attraction estimation are provided. Simulation and experimental results demonstrate that the proposed approach achieves improved transient performance and reduced overshoot compared to conventional field-oriented control, while maintaining low computational cost and robust operation under disturbances.

Obtaining high-quality labeled data for supervised learning is costly, motivating the use of crowdsourcing, which distributes the annotation process across multiple workers with varying levels of expertise. A key challenge in crowdsourced data is annotation sparsity, as each worker labels only a limited subset of instances. This sparsity can amplify class imbalance, reduce supervision for minority classes, and bias standard cross-entropy-based models toward the majority classes. To address this problem, we propose a correlated chained Gaussian process framework trained on a focal-loss-based variational objective (CCGPFL). This probabilistic framework jointly models latent ground-truth and instance-dependent annotator reliability while accounting for correlations among annotators. In addition, the focal-weighted objective mitigates the imbalance induced by sparse annotations by assigning greater importance to harder examples during training. Experiments on synthetic, semi-synthetic, and fully real multi-annotator datasets show that CCGPFL achieves competitive and often superior performance relative to state-of-the-art learning-from-crowds baselines in terms of Overall Accuracy (OA) and Area Under the ROC Curve (AUC).

This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint P–Q control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty.

Objective: This article presents a study on Technological Surveillance and Competitive Intelligence (TSeCI) focused on identifying trends in whey valorization. Methodology: Through the identification of Critical Surveillance Factorsfor the context of the Colombian dairy industry, analysis of scientific publications in Scopus, and review of patents in Patentscope, products, technologies, and methods in this field were identified. Key findings: Show research thematic clusters that include the development of biofilms, beverages, bioethanol, biopolymers, and galacto-oligosaccharides. The developments are carried out mainly through ultrafiltration, hydrolysis, and fermentation methods, using microorganisms, protein extraction, and mixtures with other products such as fruits, plants, and cereals. Likewise, the patent analysis highlights a trend towards the production of protein beverages obtained through fermentation and ultrafiltration using microorganisms and mixtures with other products. Conclusion: The study emphasizes the importance of whey valorization within the circular economy and sustainability, while identifying limitations such as the need to expand the analysis to other geographical contexts.