This study investigates the mechanical response of cemented silt subjected to 28 days of curing by integrating two predictive methodologies: porosity-cement index (η/Civ) and machine learning (ML) models. The soil was compacted over a wide range of molding water contents and dry densities, including optimum and off-optimum states, and stabilized with varying cement contents. Unconfined compressive strength (qu) and splitting tensile strength (qt) were evaluated as functions of cement dosage, curing time, porosity, water content, and the specific gravities of the soil and cement. The η/Civ index demonstrated a strong predictive capability for both qu and qt, with determination coefficients exceeding 0.980, and exhibited the expected power-law decay with increasing η/Civ. ML algorithms-particularly Gaussian Process Regression with a Matern 5/2 kernel-outperformed the empirical model, achieving R2 values of 0.963 (validation) and 0.997 (testing) for qu prediction. The qt model similarly reached R2 = 0.984-0.988, demonstrating high generalization and stability across curing and compaction conditions. Experimental results revealed substantial strength gains with decreasing η/Civ, with qu increasing from 100 kPa at η/Civ = 46 to 2900 kPa at η/Civ = 19, while qt rose from 10-15 kPa to 300 kPa across the same range.
Soybean (Glycine max [L.] Merrill) is one of the world’s most important agricultural crops, playing a strategic role in global protein and lipid production. However, its productivity is severely constrained by defoliating lepidopterans such as Anticarsia gemmatalis, Chrysodeixis includens, Helicoverpa armigera, and species of the genus Spodoptera, which cause substantial yield losses due to their intense herbivory and remarkable adaptive capacity. Conventional management strategies relying on chemical insecticides provide only partial control and are associated with negative environmental and ecological impacts. Although transgenic Bt soybeans have demonstrated efficacy against certain pest species, they exhibit limited toxicity toward Spodoptera spp. In this context, the exploitation of soybean genotypes with natural resistance represents a promising alternative within the framework of Integrated Pest Management. This review summarizes the principal chemical defense mechanisms underlying soybean resistance to lepidopterans, emphasizing the role of secondary metabolites, such as flavonoids, phenolics, tannins, and volatile organic compounds, that function as toxic, antinutritional, or repellent agents. Several genotypes, including IAC 100, PI 227687, and PI 227682, have displayed resistance to multiple caterpillar species, establishing themselves as valuable genetic resources for breeding programs. Furthermore, recent studies indicate that environmental conditions, plant developmental stage, and multitrophic interactions strongly modulate the expression of these defense traits. A comprehensive understanding of the chemical interactions within the soybean-lepidopteran system is therefore crucial for the development of more tolerant and sustainable cultivars, reducing dependency on insecticides and slowing the evolution of insect resistance. Future perspectives emphasize the integration of omics technologies, bioinformatics, and biotechnology to elucidate key metabolic pathways and accelerate the generation of resistant soybean varieties, ultimately promoting higher productivity and agricultural sustainability.
Gender-based violence (GBV) is a pervasive social and public health issue that increasingly manifests in digital communication platforms. This article presents a multidimensional framework, the Gender Discourse Violence Index ( GDVI_AI ), designed to detect and quantify violent discourse in WhatsApp conversations. The framework integrates four key dimensions: (i) toxicity detection using large language model prompts, (ii) sentiment analysis with BERT to capture emotional load and polarity, (iii) a weighted dictionary of over 2200 offensive expressions, and (iv) grammatical person identification to assess the directness of threats. By combining these components in a weighted formula, the GDVI_AI produces a score ranging from 0.1 for non-violent discourse to values exceeding 9 for explicit insults or threats. The model was evaluated against a reference dataset using confusion matrices and descriptive statistics, demonstrating high accuracy and robustness. Beyond classification, the framework enables temporal analysis of message-level violence, supporting the identification of escalation patterns in perpetrator–survivor dialogues. The proposed approach contributes to forensic psychology and digital criminology by offering a reliable tool for early detection, evidence collection, and the study of communicative dynamics in cases of gender-based violence.
IntroductionThis study examines academic engagement as a mechanism through which universities connect research with societal use. Drawing on a stimulus–organism–response perspective, it investigates the determinants of academic engagement and its effects on knowledge transfer and scientific productivity.MethodsWe administered a cross-sectional survey to 147 full-time faculty members in Colombia using a validated 32-item instrument measured on five-point Likert scales. The model specifies epistemic motivation, instrumental motivation, prior U–I experience, institutional support, and perceived social norms as antecedents; academic engagement as the focal construct; and knowledge transfer and scientific productivity as outcomes. Partial least squares structural equation modelling was used to assess reliability, validity, collinearity, and predictive performance.ResultsThe measurement model showed satisfactory psychometric properties, with standardised loadings above 0.70, AVE values ranging from 0.590 to 0.715, composite reliability between 0.852 and 0.899, and a maximum HTMT of 0.829. All antecedents were positively and significantly associated with academic engagement, with institutional support (β = 0.373) and epistemic motivation (β = 0.320) showing the strongest effects. Academic engagement was positively associated with knowledge transfer (β = 0.688) and scientific productivity (β = 0.563). The model explained 66.4% of the variance in academic engagement, 47.4% in knowledge transfer, and 31.7% in scientific productivity.DiscussionThe findings position academic engagement as a robust mechanism for translating academic work into external use while sustaining scholarly output. They suggest that universities can strengthen both societal impact and research performance by recognising engagement in workload and promotion systems, reinforcing faculty support structures, and embedding collaboration more systematically into institutional strategy.
Air pollution is a critical public health issue worldwide, South America faces unique challenges due to rapid urban growth, industrial expansion, and recurrent biomass burning. Existing studies have largely focused on regional or national scales, overlooking detailed spatio-temporal dynamics in cities. This study provides a comprehensive assessment of air pollution spatio-temporal trends from 2013 to 2023 in six major South American cities: Bogotá, Buenos Aires, Montevideo, Quito, Santiago de Chile, and São Paulo. We evaluated four key pollutants, NO2, O3, PM10, and PM2.5, using in situ monitoring networks complemented with reanalysis (boundary layer and pollution dynamics), and fire detections datasets (biomass burning). A key innovation is the use of a Lagrangian Tracker, which identifies persistent hotspots and transport pathways of pollutants, offering new insights into transboundary pollution. Results show that nearly all cities experienced reductions in particulate matter concentrations, while three of the six cities exhibited rising O3 levels, reflecting complex interactions between emissions, meteorology, and atmospheric chemistry. Santiago de Chile recorded the highest levels of NO2 and PM, strongly influenced by topography and biomass burning in JJA. Bogotá and Quito were notably impacted by regional fire emissions, whereas coastal cities such as Buenos Aires and Montevideo benefited from greater pollutant dispersion but still exceeded the World Health Organization guidelines. By integrating ground-based, satellite, and reanalysis data with advanced trajectory modeling, this research provides detailed spatio-temporal evaluations of air pollution in South America and highlights the urgent need for coordinated regional strategies to reduce health and economic burdens. The panel on the left illustrates the countries of interest along with the cities included in the analysis. The right section presents the mean concentrations of PM10 and PM2.5. The upper middle panel illustrates the degree of compliance with local air quality standards, while the lower middle panel shows the spatial distribution of fire hotspots across South America.
