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Guide to MS366 Texas Western Press records
No full textTexas Western Press was the university press of the University of Texas at El Paso (UTEP). Established in 1952 by printer and book designer Carl Hertzog, it was named for what the institution was called at that time: Texas Western College. The records were transferred to the UTEP Library in 1998 and received several later accretions. The collection includes correspondence, editorial board minutes, book production files, manuscripts, proofs, artwork, book catalogs, marketing materials, memorabilia, scrapbooks, and archival copies of publications
Ultrathin-Layer Strain-Based Electronic Devices: From-First-Principles Derivation of the Corresponding Equation
No full textMost information about the world comes from sensors -- and from the results of processing sensor data. In many practical situations -- e.g., in biomedical applications -- it is desirable to make sure that the sensors are as invisible as possible, in particular, that they are as small as possible. One way to achieve such small size is to use ultrathin-layer materials such as graphene. It is known that for such materials, strain causes electromagnetic effects -- which can be used to detect small strains. Interestingly, it turned out that the same equation describes the relation between strain and electric effects and between strain and magnetic effects -- although in these two cases, physics is somewhat different. The fact that we get the same equation in two different physical situations leads to a natural conjecture that this equation should follow from first principles, without the need to use specific physical equations. In this paper, we show that this is indeed the case: one of the main equations of straintronics can be derived from first principles, without using specific equations of physics
7 Plus Minus 2 Law Revisited: Alternative Geometric Explanation, Mayan Arithmetic, and Using 9- and 18-Based Numbers in Jewish Tradition
No full textA recent paper showed that to make sure that the movements in the crowd are not chaotic, the directions of all the motions should deviate from some fixed direction by no more than 13 degrees. We show that this results provides a new geometric explanation for the seven plus minus two law in psychology, according to which we can keep in mind no more than 7 plus minus 2 items. We also show that all this is related to the somewhat mysterious appearance of 9- and 18-based number systems in Jewish and Mayan traditions
At Least k out of n under Fuzzy Uncertainty: Efficient Algorithm for General And -Operations
No full textIn medicine, many diagnoses are made when, for some value k, at least k of n possible symptoms are present. Many of such symptoms -- such as fever -- are, in reality, fuzzy. For example, it makes no sense that say that 38.0 is fever while 37.9 is not a fever, both are fever to some degree. Once such degrees are given, we need to use them to estimate the degree to which the patient has the corresponding disease. For this problem, the usual fuzzy techniques require exponentially many computational steps -- so it is desirable to have a more efficient algorithm. Such an algorithm was previously proposed for some specific and -operation (t-norm). However, in different application areas, different and -operation describe the reasoning within this domain. So, it is desirable to extend the existing feasible algorithm to the case of general and -operations. In this paper, we describe such an extension
Rethinking Iterative Proportional Fitting: Scalable And Hybrid Approaches To Joint Distribution Fitting
No full textThe Iterative Proportional Fitting (IPF) algorithm is widely used in contingency table estimation, survey weighting, and synthetic population generation due to its simplicity and strong theoretical foundation for matching observed marginal distributions. However, in high-dimensional settings, IPF faces substantial computational and memory demands, as well as statistical instability caused by sparse contingency tables. Moreover, IPF is less useful in modern population synthesis tasks that require both scalability and realism because, despite its superiority in matching known marginal distributions, it cannot produce realistic out-of-sample data points. To address these limitations, we first propose a blockwise IPF framework, in which the feature space is partitioned into smaller, correlated groups and IPF is applied independently within each group. This design significantly enhances computational efficiency while ensuring alignment with marginal distributions and preserving inter-variable dependencies. Second, we develop a hybrid framework to integrate IPF-derived weights into machine learning-based generative models. Two strategies are explored: (1) pre-sampling, where training data is reweighted using IPF weights to match marginal targets, and (2) weighted learning, where these weights are directly incorporated into the model\u27s training objective. While the framework is model-agnostic, we use Bayesian networks as a case study. Extensive simulation studies and real-world synthetic population generation experiments demonstrate that the proposed blockwise IPF framework scales efficiently to high-dimensional settings, maintaining statistical accuracy while offering substantial reductions in computational time. These experiments further show that the hybrid strategy produces synthetic data with greater sample diversity and improved alignment with marginal distributions. Finally, we introduce early-stage work on a neural network-based approach for estimating the joint distribution of a contingency table given expected marginals. Preliminary results suggest that this new paradigm holds significant promise for addressing several fundamental limitations of IPF
Comparative Study of Non-Iterative Seqential Method for Biot\u27s Poroelasticity Model
No full textLinear poroelasticity theory describes the interaction between the motion of fluids and deformation of porous media. The theory serves various applications in a wide range of science and engineering fields, such as soil mechanics, oil reservoir modeling, and bio-medical applications. Partial differential equations are used to model the complex interaction between fluid flow and solid deformation in porous media. Since analytical solutions to these equations are rarely available for realistic problems, we resort to numerical solutions. One major advantage is their ability to provide accurate approximations of the solutions. Various numerical methods have been proposed to solve Biot\u27s poroelasticity system. These methods are mainly classified into three main classes: monolithic methods, sequential methods, and iterative methods. The sequential methods decouple the system into flow and mechanics subproblems, then solve them sequentially one after the other at each time step. In this thesis we propose two sequential finite element methods to solve Biot\u27s poroelasticity system. The methods both use stabilization terms to ensure convergence and stability of the displacement and pressure solutions, and differ mainly by the type of stabilization used. Particularly, the first uses a L2 -type stabilization term while the second uses an H 1 -type stabilization term. An extensive numerical convergence study of both sequential methods was conducted. First, we used two manufactured solutions with pure Dirichlet boundary conditions. The performance of both sequential methods is analyzed for two different parameter regimes. The H1 -type stabilization method proves more robust and delivers better and optimal convergence rates for both parameter regimes. Another numerical convergence study for the sequential method with H 1 -type stabilizing approach is carried out with another manufactured solution and mixed boundary conditions, where the approach offers optimal convergence rates. The performance of this method was also tested against the well-known Barry and Mercer problem
Optimal Experimental Plan For Multi-Level Stress Testing Under Progressively Hybrid Censoring
No full textReliability analysis is essential for understanding how products perform over time, particularly in environments where failure data is limited or costly to obtain. One effective approach is multi-level stress testing, where test units are subjected to varying levels of stress to accelerate failures and extract more information within constrained timeframes. This study presents a novel optimization-based framework for designing life-testing experiments under progressively Type-II hybrid censoring, assuming Weibull lifetime distributions. Leveraging a Variable Neighborhood Search (VNS) algorithm, we determine efficient allocations of test units and censoring parameters across multiple stress levels to enhance the precision of estimates of the model parameters. Optimal designs are obtained under both A-optimality and D-optimality criteria, with comprehensive numerical results illustrating the impact of stress configuration, sample size, and failure patterns on estimator performance and information gain. The proposed approach consistently identifies robust and computationally efficient designs, revealing that strategic allocation and selective censoring can substantially improve the determinant and trace of the Fisher information matrix. These results provide practical guidance for designing effective life-testing experiments, particularly in reliability studies involving limited sample sizes and multiple stress conditions
Improving Glycemic Control Mechanisms By Electrical Muscle Stimulation
No full textIntroduction: Neuromuscular electrical stimulation (NMES) has emerged as a promising intervention for improving metabolic health in sedentary individuals with overweight or obesity. Its capacity to induce muscle contraction without voluntary movement presents a viable alternative to traditional exercise, especially in mobility-limited populations. NMES may serve as a non-invasive alternative to traditional exercise, especially in high-risk populations who face elevated type 2 diabetes incidence linked to hyperglycemia, obesity, and inactivity. This study investigated the impact of NMES on glycemic control and metabolic health in a sedentary, predominantly Hispanic, overweight or obese population. Methods: This study examined the acute and long-term effects of NMES on metabolic and psychosocial health. Acute outcomes were assessed via a single 30-minute NMES session measuring fasting glucose, energy expenditure, and substrate utilization. Longitudinal impact was evaluated over an 8-week intervention, focusing on glycemic variability and insulin sensitivity. Results: A single NMES session resulted in significant reductions in fasting blood glucose and glucose variability, with increases in energy expenditure and whole-body carbohydrate utilization. Longitudinal NMES training improved post-prandial glucose management outcomes and tended to prevent rises in insulin resistance observed in control groups. Additionally, trained individuals showed enhanced responses to acute NMES, including greater fasting glucose reductions, higher energy expenditure, and increased stimulation tolerance. Conclusion: NMES offers a feasible and effective adjunct to physical activity for populations with limited exercise capacity. These findings support its potential as a preventive strategy to enhance metabolic health and insulin sensitivity in underserved communities, warranting further exploration of whole-body stimulation protocols and mechanistic pathways