The University of Texas at Arlington (UTA or UT Arlington) is a public research university in Arlington, Texas, midway between Dallas and Fort Worth. The university was founded in 1895 and was in the Texas A&M University System for several decades until joining The University of Texas System in 1965. The university is classified among "R1: Doctoral Universities – Very high research activity." The fall 2019 campus enrollment consisted of 43,863 students making it the largest university in North Texas and fourth-largest in Texas. UT Arlington is the third-largest producer of college graduates in Texas and offers over 180 baccalaureate, masters, and doctoral degree programs.UT Arlington participates in 15 intercollegiate sports as a Division I member of the NCAA and Sun Belt Conference. UTA sports teams have been known as the Mavericks since 1971.
One of the key challenges in shallow geothermal technology is improving the thermal performance of ground heat exchangers (GHEs) while reducing operational costs. Existing enhancement techniques are typically applied uniformly along the GHE depth, despite field data showing non-uniform thermal performance. This paper presents a spatiotemporal characterization of a single u-pipe GHE, consolidating theoretical framework, laboratory characterization, and numerical modeling to identify where and when coupled heat and moisture transfer becomes significant within the GHE geometry. The coupled model is validated against a laboratory column test under ambient, and non-insulated boundary conditions and applied to characterize the three-dimensional axial and radial extent of soil drying and wetting around a GHE operating in heat rejection mode. Results reveal an asymmetric temperature and moisture distribution, with greater desaturation near the inlet leg, concentrated in the upper 40–50% of the GHE depth and extending 1.21 m - 1.52 m (4 - 5 ft) radially from the pipe. This desaturation is self-limiting, stabilizing within one to two months of operation, and corresponds to an approximately fivefold increase in matric suction near the pipe, from approximately 348 kPa (7268.13 psf) to approximately 1851 kPa (38,658.94 psf) over four months. The associated reduction in thermal conductivity and heat capacity substantially alters the heat exchange capacity of the surrounding soil. These findings establish the spatiotemporal basis for future design and operational strategies, such as targeted use of high-conductivity materials near the GHE inlet and periodic flow reversal, to improve the cost-effectiveness of shallow geothermal systems.
Accurate characterization of nanoscale pore geometry and pore size distributions (PSDs) in hydrocarbon-bearing unconventional reservoirs (e.g., shale) is crucial for evaluating hydrocarbon storage and fluid mobility. However, a reliable inversion of PSDs from small-angle scattering (SAS) analyses of such dense porous geomaterials remains challenging. We extend empirical-Bayes/Maximum Entropy (MaxEnt) inversion in two directions: joint nonparametric PSD-S(q; theta) inference, and a Poisson-consistent hierarchical inference strategy with profiled regularization. Specifically, structure-factor parameters, which characterize effective inter-pore spatial correlations in dense geological media, are estimated in the outer layer by Laplace-profile evidence, while PSDs are reconstructed in positivity-preserving log space in the inner layer through a Poisson-MaxEnt maximum a posteriori solve. This Poisson formulation models count statistics directly through a Poisson likelihood rather than a Gaussian approximation. Across synthetic tests and shale samples with small-and very-small-angle neutron scattering (SANS/VSANS) applications, the joint treatment reduces correlation-induced false large-size PSD features relative to the baseline of a unity structure-factor and yields more stable PSD derivation. The framework supports more reliable nanoscale pore-structure characterization for unconventional reservoir evaluation, hydrocarbon storage and mobility assessment, and subsurface energy-storage applications.
The demand for lightweight and mechanically reliable structures has motivated increased interest in Additive Manufacturing (AM), particularly Fused Filament Fabrication (FFF), while underscoring the importance of an integrated understanding of the design-to-fabrication workflow. While numerous studies have focused on individual aspects of the FFF process in fabrication of such structures, detailed review linking design strategies, optimization techniques, and manufacturing challenges are scarce. This work addresses that gap by reviewing current strategies for designing and fabricating lightweight structures using FFF. First, it investigates sophisticated design and optimization strategies, consisting of Design for Additive Manufacturing (DfAM) concepts and Topology Optimization (TO) techniques specifically adapted for thin-walled and lattice structures. The limitations of traditional CAD tools are extensively investigated along with a summary of major manufacturing challenges. The review combines multidisciplinary insights to create an integrated understanding for generating structurally efficient and reliable FFF components. It concludes by outlining prospective future research avenues, including artificial intelligence–driven process control and digital twins for predictive modeling. This work offers a detailed resource for researchers and practitioners aiming to address existing limitations and drive FFF towards resilient next-generation lightweight systems.
Recent advances in artificial intelligence (AI) have significantly enhanced quality management, enabling more effective handling of complex, high-dimensional, and multi-modal data. AI methods, including machine learning (ML) and deep learning (DL), have been pivotal in advancing key areas such as quality optimization, monitoring, and diagnosis. These methods have increased adaptability, efficiency, and scalability, making them particularly suitable for modern industrial applications. This review provides a comprehensive examination of AI methods in quality management, covering the integration of surrogate models, Bayesian optimization (BO), intelligent control charts, change-point detection (CPD), and interpretable quality diagnosis. The review concludes with proposed directions for future research aimed at overcoming existing challenges and enhancing the deployment of AI in real-world quality management implementation.
Retailers struggle with late deliveries, thus motivating research to improve e-fulfillment performance. Studies have primarily investigated order processing and delivery individually but have ignored the interplay between these two e-fulfillment activities. The Theory of Swift and Even Flow (TSEF) provides a useful frame for examining the e-fulfillment process, yet it neglects important behavioral factors. We elaborate the TSEF using logic from the Queue-Length Visibility and Misperception of Feedback Dynamics perspectives to unveil how behaviors in order processing and delivery contribute to delivery performance. We analyze 11,241 orders from a major Vietnamese retailer using econometric methods informed by practitioner interviews. We find a concave relationship between order processing time (OPT) ratio (defined as the proportion of planned lead time consumed by order processing) and lateness. Late orders have OPT ratios exceeding 25% of the planned lead time, and they exhibit higher OPT variance. We also find a U-shaped relationship between OPT ratio and order delivery time (ODT); expediting deliveries mitigates delays until OPT ratios reach a threshold of 58%. Finally, we argue that workers and managers prioritize the processing of focused orders. Understanding behaviors in the e-fulfillment process offers insights that extend the TSEF, new research areas, and managerial implications.
