Universidad Privada Boliviana

Air pollution monitoring in Bolivian high-altitude cities—La Paz (3,600 m a.s.l.), El Alto (4,100 m), Oruro (3,706 m), Potosí (3,976 m), and Cochabamba (2,558 m)—relies exclusively on conventional criteria pollutants (PM₂.₅, PM₁₀, NOₓ, SO₂, CO, and O₃), which fail to fully capture the oxidative toxicity to which urban populations are exposed. Reactive Oxygen Species (ROS) constitute a chemically diverse group of highly reactive oxygen-derived compounds whose atmospheric formation is intensified by the particular physicochemical conditions of the Andean atmosphere: elevated ultraviolet irradiance, an aging high-emission vehicle fleet, redox-active transition metals in resuspended mineral dust, and frequent thermal inversion episodes. This paper presents the theoretical framework justifying the urgent need to incorporate ROS monitoring into Bolivia's air quality surveillance systems, and evaluates the methodological proposal of the MonROS project, which employs an active sampling system with an ABTS (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) capturing solution at pH 7.4 for the measurement of total atmospheric oxidant capacity. The proposed methodology is assessed in terms of its scientific soundness, analytical advantages, and current limitations, and its potential to generate the first baseline dataset of atmospheric oxidative potential (OP) for high-altitude Andean cities is discussed.

This paper explores the functioning of the repo market as a core mechanism for liquidity provision and short-term funding, combining international evidence with an analysis of the Bolivian case from 2014 through the first half of 2025. At the global level, repos have become foundational instruments for collateral management and the transmission of monetary policy. In Bolivia, the empirical assessment reveals three well-defined phases: i) a period of sustained expansion between 2014 and 2022, characterized by the predominance of Time Deposits (DPF), Treasury bills and bonds, and relatively stable rate dynamics; ii) a structural break beginning in June 2023, validated through formal regime-shift tests, marked by a sharp contraction in volumes, the collapse of DPF-backed transactions, and a significant deterioration in funding conditions; and iii) a recent shift toward UFV-denominated operations and private collateral, accompanied by heightened rate volatility, reflecting the scarcity of inflation-indexed collateral. Overall, the findings highlight the need to strengthen the regulatory framework, risk-management practices, and operational standardization to consolidate a more efficient repo market aligned with international benchmarks.

In this work, we present a pipeline for automated knowledge graph construction from materials science literature using large language models (LLMs). The proposed method performs entity and relationship extraction guided by a data model based on the logic of the Elementary Multiperspective Material Ontology (EMMO), structuring the output into a machine-interpretable graph format. The pipeline integrates several key components, including prompt-based extraction, a hierarchical chunking strategy that leverages document structure and section headers, and post-processing steps such as normalization, LLM-assisted deduplication, and alignment of node identifiers. A central focus of this study is the evaluation of different chunking strategies. Specifically we compare fixed-size splitting with a hierarchical chunking approach that incorporates document structure and header information. Our results show that hierarchical chunking consistently outperforms fixed-size chunking across both entity and relationship extraction tasks, achieving higher precision, recall, and F1 scores through more context-aware segmentation. Extracted entities and relationships are aligned with a curated ground truth dataset through manual verification to ensure semantic correctness. Overall, these findings indicate that LLMs, when combined with domain-specific ontological guidance and well-designed pre-and post-processing, can effectively extract high quality knowledge graphs from complex materials science literature. This benefits materials scientists and researchers by reducing manual curation effort and accelerating data-driven materials discovery.

El acuerdo de París y el establecimiento de los Objetivos de Desarrollo Sostenible (ODS) han impulsado a los países que firmaron estos acuerdos a invertir en la investigación y desarrollo de alternativas de generación de energía limpia y reducir la dependencia en los combustibles fósiles. Una de las estrategias para esta transición es la microgeneración en sistemas productivos urbanos, donde la energía eólica destaca debido a su flexibilidad de diseño y adaptabilidad. En este sentido, en este trabajo se diseña y construye un Aerogenerador de Eje Vertical (VAWT por su sigla en inglés) tipo Banki para microgeneración urbana. Para este propósito, primero se verifican mediante simulaciones numéricas los parámetros de diseño óptimos (número de álabes, ángulo de ataque del álabe y relación altura-diámetro) sugeridos en un trabajo previo del mismo grupo de investigación. Posteriormente, se analiza la influencia del perfil de los álabes del aerogenerador, un parámetro que no había sido considerado anteriormente. A partir de los resultados obtenidos de velocidad angular de los diferentes perfiles de álabes se llegó a un diseño final del aerogenerador. Finalmente, se logró construir un prototipo de aerogenerador de eje vertical el cual, en las pruebas, llegó hasta 600 RPM y consiguió generar una diferencia de voltaje de hasta 100 mV. Además, se verificó experimentalmente la importancia del ángulo de incidencia del viento en la rotación del aerogenerador.

Digital platforms and appliances shaped the modern delivery services and last-mile logistics worldwide. Today, logistics and operational strategies are focused on the new consumer behaviors in both developed and emerging markets. In this landscape, delivery apps play a crucial role in enhancing firms’ competitiveness, customer responsiveness, and optimized resource allocation. Nevertheless, adoption patterns in emerging economies remain largely unexplored. That is why this study examines the determinants of the intention to use delivery applications in Bolivia through an extended UTAUT2 model that incorporates perceptions of artificial intelligence, legality, gamification, and social influence. Using a structured survey, data were collected from 403 users and analyzed using multiple linear regression. The results show that facilitating conditions are the highest predictor of intention and actual use behavior, confirming the critical importance of digital infrastructure, reliable connectivity, and secure payment systems in technology adoption. Additionally, the perception of artificial intelligence, legality, and social influence has a significant and positive effect on intention, while habit influences intention but is not reflected in actual use behavior. On the other hand, gamification does not present a statistically significant impact, which indicates that utilitarian and trust-related factors prevail over hedonic factors. These findings expand the literature on technology adoption by contextualizing the UTAUT2 model in the Bolivian delivery market and offer managerial implications for small and medium-sized enterprises that have their own delivery applications, providing evidence-based strategies to enhance competitiveness. Keywords: Delivery applications, Technology adoption, UTAUT2 model, Facilitating conditions, Bolivia.

This work presents an alternative post-processing approach for optimizing mobile robot trajectories by combining vector quantization techniques with information theory. We developed an algorithm based on Vector Quantization (VQ) and Kullback-Leibler Divergence (VQKL) that maintains the original RRT*'s obstacle avoidance capabilities. When comparing VQKL and VQ with the Ramer-Douglas-Peucker (RDP) algorithm, our methods demonstrate significant superiority: VQ achieves a 13% reduction in path length (versus RDP's 10%) while VQKL achieves 14%, along with an 83% (VQ) and 84% (VQKL) reduction in node count compared to the original RRT* output. These results are obtained through an adaptive optimization process that iteratively adjusts centroids using a progressive annealing scheme. To ensure trajectory feasibility, we implemented a validation system that verifies both geometric deviation from the original path and collision-free operation with obstacles. Extensive simulations across 20 different environments with 100 trials each confirm that our method generates significantly shorter, more efficient, and safer trajectories, establishing a viable alternative for robotic path optimization.

A better characterization of the satellite propagation channel remains of paramount importance. Specifically, assessing the impact of the atmospheric effects in a Q/V band (40/50 GHz) satellite signal is critical when designing systems operating at such high frequency bands, which are receiving increasing attention and interest in last years. A long-term propagation experiment is being carried out in Madrid, receiving the 40-GHz beacon signal of the Alphasat satellite since 2014. This paper presents the rainfall rate and attenuation results for the first completed 9 years of the experiment, as well as to compare them with several ITU-R models. Moreover, excess attenuation is also compared with a distribution obtained from synthetizing 100 years of attenuation time series with the procedure of Rec. ITU-R P.1853-2. Both Rec ITU-R P.618-14 and the synthetized series approach yield similar results, each one behaves better for a specific probability interval.

Considerando que la ética y los valores de las personas, constituyen un componente fundamental para el desenvolvimiento de los empleados del sector de intermediación financiera sin conductas improcedentes que generen riesgo operativo (RO), el presente trabajo de investigación se realizó a partir de las proposiciones teóricas realizadas por Pereira [1], con el objetivo central de la validación indirecta de sus hipótesis P1A y P1B [1], realizándose un análisis de las variables que se relacionan con el interés de trabajar en una entidad de intermediación financiera (EIF) mediante un estudio empírico en Bolivia aplicando encuestas con muestreo probabilístico simple y estratificado a 1656 estudiantes de 9 universidades y 1164 empleados de EIFs del citado país; considerando expectativas y valores universales. Al evidenciarse que mayoritariamente manifestaron interés en trabajar en EIFs (82.4% estudiantes y 98.4% empleados), los resultados obtenidos a través de modelo logit de regresión que permiten sugerir en orden de importancia, que las principales variables que están asociadas o relacionadas con el interés de trabajar para los estudiantes universitarios son el logro, seguido de benevolencia (ambos negativamente) y hedonismo (positivamente), mientras que para los funcionarios de EIFs son el logro y estimulación (ambos negativamente).