The University of Texas at El Paso (UTEP) is a public research university in El Paso, Texas. It is a member of the University of Texas System. UTEP is the second-largest university in the United States to have a majority Mexican American student population (about 80%) after the University of Texas Rio Grande Valley. It is classified among "R1: Doctoral Universities – Very high research activity." The university's School of Engineering is the nation's top producer of Hispanic engineers with M.S. and Ph.D. degrees.UTEP is home to the Sun Bowl stadium, which hosts the annual college football competition the Sun Bowl every winter. The campus is one of the few places in the world outside of Bhutan or Tibet to have buildings created with the Dzong architectural style. It sits on hillsides overlooking the Rio Grande river, with Ciudad Juárez in view across the Mexico–United States border.
Understanding causal relationships among features is fundamental for explaining machine learning model decisions. However, traditional causal discovery methods face challenges with categorical variables due to numerical instability in conditional independence testing. We propose a dual-encoding causal discovery approach that addresses these limitations by running constraint-based algorithms with complementary encoding strategies and merging results through majority voting. Applied to the Titanic dataset, our method identifies causal structures that align with established explainable methods.
Managing water infrastructure systems has become increasingly challenging due to rapid economic growth, climate change effects, and the rising frequency of extreme hydro-meteorological events. Regions located in riverine floodplains, such as deltaic and low-lying areas, are particularly vulnerable to flooding and climate-induced disruptions. In this context, there is a critical need for systematic approaches to assess and enhance the resilience of urban water systems. This study develops an integrated framework for resilience assessment by combining the Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling (DEMATEL–ISM) with a Dynamic Bayesian Network (DBN). The proposed methodology captures both the causal interdependencies among resilience parameters and their temporal evolution under varying conditions. A total of 43 parameters were identified through literature review and expert consultation and applied to a case study in the Barak Valley region of Northeast India. Data were collected from field surveys, historical records, and official reports, including those from the District Disaster Management Authority, for the period 2022–2024. The results indicate that key driving factors are primarily associated with institutional capacity, climate adaptation investments, and infrastructure development, whereas resource availability factors such as insurance mechanisms and equipment support exhibit relatively lower influence within the system. The spatial assessment further identifies Kanakpur, Dullacherra, and Lalaghat as relatively more resilient locations, while Fanai Cherra Grant and Hailakandi are found to be more vulnerable under disaster conditions. The robustness of the framework is examined by sensitivity analysis and temporal stability assessment of the survey-derived resilience estimates. The proposed framework provides a structured and data-driven tool for evaluating water system resilience and can support decision-making for climate adaptation planning in vulnerable regions.
This research intends to analyze the impact of big data analytics capability (BDAC) toward supply chain innovation (SCI) and competitive advantage (CA) where green supply chain management (GSCM) performs the moderating role. A theoretical model was created, and hypotheses were formulated to investigate the links between these variables. Data collection was carried out through a survey of supply chain managers in manufacturing plants in Bangladesh. Total 341 valid responses were collected and analyzed with the method of partial least squares structural equation modeling (PLS-SEM) in WarpPLS. The findings of the study offer insights into how business can use BDAC to achieve SCI and CA. The results indicate that BDAC has a potential impact regarding a firm’s ability to enhance CA and SCI. Moreover, the moderating function of GSCM was examined, which was found to be a significant influence on one of these relationships. However, the industry age as a control variable was found to have no apparent impact on SCI or CA. The study also establishes the critical role of SCI in turning analytical skills into competitive results and partially mediates the relationship between BDAC and CA. In sum, this research illustrates how businesses can employ data-driven skills to increase productivity in complex and dynamic supply chain environments against the backdrop of a case study.
New zircon U-Pb geochronology, zircon geochemistry, and whole rock geochemistry are presented from the Magdalena granites in northern Sonora, Mexico that outcrop in the footwall of the Magdalena-Madera metamorphic core complex. Crystallization ages of the Magdalena granites are predominantly Eocene (51 to 38 Ma; n = 21) with two Paleocene ages (60 and 59 Ma). These ages are generally younger than subduction-related magmatism associated with the Cretaceous Eocene Mexican Magmatic Arc (CEMMA) in the northern Mexican Cordillera, including new ages (72 to 61 Ma; n = 8) from this study. The Magdalena granites are also mineralogically, compositionally, and texturally distinct from CEMMA rocks. The granites are moderately peraluminous, two-mica + garnet leucogranite (> 70 wt. % SiO2), relatively depleted in LREE and enriched in HREE, and have major and trace element chemistry consistent with water-absent, muscovite-to biotite-dehydration melting of metasedimentary to metaigneous protoliths. Zircon from the Magdalena granites have high U/Th ratios (median = 11.5) compared to CEMMA zircon (U/Th < 5) and are relatively enriched in HREE compared to CEMMA zircon. The results of this study suggest that the Magdalena granites are anatectic in origin and are part of the North American Cordilleran Anatectic Belt. The Magdalena granites crystallized from evolved melts that underwent early feldspar crystallization and were separated from a feldspar-rich residue, presumably a migmatitic source deeper in the crust. Field and petrographic relationships suggest the Magdalena granites intruded into relatively hot crust, including host rock mushes that exhibit evidence for melt infiltration and disaggregation.
Using a sample of A-share listed food-related firms in China from 2008 to 2023, this study investigates the impact of voluntary food safety information disclosure (VFSID) on firm innovation. Employing the philosophy–action–outcome framework, we apply large language model (LLM)-based text analysis to evaluate disclosure practices and construct a comprehensive VFSID index. Our results indicate that VFSID significantly enhances firm innovation, with a one-standard-deviation increase in disclosure quality associated with a 21
