This study delves into media coverage of Norma Pi & ntilde;a's appointment as the first woman presiding the Supreme Court of Justice in Mexico in January 2023, amidst escalating political polarization and the rise of women in power. Conducting a content analysis aided by ChatGPT 4.0, researchers examined 114 articles from 23 sources during her first week. Pi & ntilde;a was portrayed alongside other figures, spotlighting her leadership attributes and her gender, and conveying a positive tone. However, her actions, experience, and direct quotes were scant. The reporting centered on external evaluations, underscoring leadership, and celebrating her being a woman fosters excessive performance expectations.
Purpose Efficient inventory management is crucial for cost control and operational continuity in modern industrial environments. It reduces waste directly and indirectly and promotes more sustainable inventory use (SDG 12). In addition, it helps reduce carbon footprints through optimised transportation and inventory handling (SDG 13). This paper aims to develop a Lean Six Sigma 5.0 (LSS 5.0) conceptual framework project and document its implementation in a case study focused on reducing inventory pieces by 20% across seven bulk materials. Design/methodology/approach An LSS 5.0 framework was developed using Operational Excellence (OPEX) principles and Industry 4.0 (I4.0) technologies. Later, this framework was tested in a manufacturing organisation located in Latin America. Findings Implementing standardised packaging practices (Kanban containers) and an inventory tracking system, as part of inventory management, facilitated more sustainable supply chain processes, reducing inventory by 18.6% without impacting service levels, approaching the 20% target. Research limitations/implications The LSS 5.0 project was conducted from the supplier to the plant; further research is needed to analyse the internal Plant IMP inventory. Practical implications This study demonstrates how OPEX 5.0 and I4.0 can be used across different phases of the DMAIC – define, measure, analyse, improve and control – problem-solving methodology to improve inventory management, delivering strategic operational and environmental benefits that contribute directly or indirectly to global sustainability efforts. Originality/value This continuous improvement project integrated OPEX 5.0 principles (human-centredness, resilience and environmental impact) with I4.0 technologies (e.g. big data, simulation and real-time dashboards).
La Conquista y el dominio de América provocaron la "reificación", es decir, la cosificación del referente "indio". Ello plantea un reto todavía actual y también ejemplar para otras latitudes del orbe, dada la pluralidad étnica contemporánea: la superación de esta injusta reificación por medio de aquella que otorga la hermenéutica más allá del mero conocimiento. Dicho en términos modernos: es necesario el reconocimiento de la fusión de horizontes de significatividad efectuado en tres momentos: concientización de la praxis agraviante hacia "los indios" en la Colonia y época poscolonial, la interpretación de lo ajeno desde lo propio y su apropiación, y el desenlace a la pluralidad cultural.
This study investigates the relationship between the importance that university faculty assign to the pedagogical functions of planning, assessment, and feedback, and their intention to use Generative Artificial Intelligence (GenAI) as a support tool in their teaching practice. Using a non-experimental cross-sectional design, data were collected through a questionnaire administered to 56 faculty members from a School of Education and Humanities at a private university located in the metropolitan area of Monterrey, Mexico. Results showed significant positive correlations between the importance assigned to planning and feedback and the intention to employ GenAI for these tasks, as well as an overall correlation between perceived importance and intention to use GenAI. ANOVA analyses revealed significant differences in intention to use GenAI between departments, with the strongest association found in the Film and Communication department for planning. Additionally, years of teaching experience correlated positively with intention to use GenAI for assessment. These findings highlight the role of disciplinary and experiential factors in shaping faculty adoption of GenAI. The study underscores the need for ongoing professional development and tailored implementation strategies that consider disciplinary contexts to optimize the integration of GenAI in higher education.
Reliable traffic analysis, safety assessment, and urban mobility planning increasingly require multi-class and disaggregated traffic data, particularly in regions characterized by mixed traffic conditions and limited sensing infrastructure. Based on recent advances in unmanned aerial vehicles (UAVs) and computer vision that enable flexible, intersection-wise traffic data collection, this paper presents a UAV-based framework for the automated collection of multi-class traffic data, enabling class-specific counting of standard and adapted motor vehicles, and pedestrians, including cyclists. The framework integrates deep learning–based object detection with multi-object tracking to extract disaggregated traffic observations from aerial imagery. To support this study, a new dataset, YucaMex-Drone, was created, comprising 4,448 high-resolution UAV images annotated into eight traffic categories: cars, trucks, buses, vans, motorcycles, bicycles, moto-taxis, and pedestrians. These videos were collected at multiple urban and semi-rural intersections in Yucatán, Mexico. The performance of object detection models based on Faster R-CNN and YOLO architectures is evaluated, along with three counting strategies using different tracking approaches. Experimental results show that the best-performing detection model achieves a mean average precision of 98.15
