Tecnológico de Monterrey

Culinary herbs and spices have been valued globally for both flavor enhancement and health benefits. This review synthesizes the health-promoting benefits of these culinary materials, focusing on bioactive compounds such as phenolics, terpenoids, and alkaloids, which demonstrate validated potential in promoting metabolic health and preventing chronic diseases, including obesity, diabetes, cardiovascular disorders, and neurodegenerative conditions. We discuss their roles in food processing and functional food development, emphasizing innovative thermal and nonthermal approaches to integrate these natural ingredients into modern food systems. From delivery systems and preservation techniques to their incorporation into food matrices, herbs and spices offer versatile applications that extend shelf life, improve safety, and enhance nutritional value. By bridging traditional knowledge with contemporary food science, this review highlights the potential of culinary herbs and spices in functional foods, preventive nutrition, and sustainable food production, providing valuable insights to researchers, industry professionals, and consumers navigating the intersection of gastronomy, nutrition science, and food innovation.

Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging, aiding resilience in the face of challenges, and enhancing overall well-being. This paper introduces Social Support Detection (SSD) as a Natural Language Processing (NLP) task aimed at identifying supportive interactions within online communities. We define SSD through three subtasks: (1) binary classification of whether a comment expresses social support or not social support, (2) binary classification of the intended support target (individual or group), and (3) multiclass classification of the specific group being supported, including Nation, Other, LGBTQ, Black Community, Religion, and Women. We conducted experiments on a manually annotated dataset of 9,998 YouTube comments. Traditional machine learning models were employed using various combinations of linguistic, psycholinguistic, emotional, and sentiment-based features. Additionally, neural network-based models incorporating word embeddings were evaluated to enhance performance across the subtasks. The results indicate a prevalence of group-oriented support in online discourse, highlighting broader societal dynamics. The findings show that integrating psycholinguistic and affective features with unigram representations improves classification performance. The best macro F1-scores achieved across the subtasks range from 0.72 to 0.82.

Urban air quality is a key component of environmental sustainability and public health in large metropolitan areas. Following the substantial but temporary improvements in air quality observed during the COVID-19 lockdowns, it remains unclear whether structural changes in urban air pollution have persisted in the post-pandemic period. This study analyzes the temporal dynamics of major atmospheric pollutants in Mexico City between 2021 and 2024, including CO, NO2, NOx, O3, PM10, PM2.5, and SO2, using hourly data from the Mexico City Atmospheric Monitoring System (SIMAT). Annual and monthly median concentrations were computed to reduce the influence of extreme values and short-term pollution episodes. Station-level monotonic trends were evaluated using the non-parametric Mann–Kendall test, complemented by the use of Sen’s slope estimator to quantify the magnitude and direction of change. Absolute and relative changes between 2021 and 2024 were also analyzed to capture incremental variations not reflected by trend significance tests and performed together with hourly monthly analyses to characterize diurnal and seasonal patterns. Results indicate that no statistically significant monotonic trends were detected for any pollutant across the analyzed stations (p > 0.05), suggesting an overall stabilization of air quality levels during the post-pandemic period. Nevertheless, moderate increases in annual median concentrations were observed at specific locations, particularly for PM10, PM2.5, NO2, and NOx, with relative changes ranging from approximately 5% to 35%. Persistent diurnal and seasonal patterns were identified, closely associated with traffic activity, photochemical processes, and meteorological conditions. These findings suggest that, although no robust long-term trends are evident, incremental increases and stable temporal structures remain relevant from a sustainability perspective. Continued monitoring and targeted air quality management strategies are therefore necessary to support long-term urban environmental sustainability.