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Pasos Seguros: Investigating the Impact of Latin Dance and Fall Prevention Education on Fall Risk in Older Adults
No full textIntroduction: Falls are a leading cause of injury and mortality among older adults, posing a significant public health concern worldwide. By participating in fun, culturally relevant, and recreational fall prevention interventions, older adults are more likely to be motivated and stay engaged in fall prevention educational programs.
Methods: Pasos Seguros (confident steps) provided six weekly 60-minute sessions at the UTEP Rehab Sciences Complex, each including 45 minutes of chair-supported Latin dance instruction (cumbia, bachata, salsa) followed by 15 minutes of fall prevention education (home safety, medication effects, and emergency preparedness) through the UTEP Osher Lifelong Learning Institute.
Results: A paired-samples t-test revealed a statistically significant improvement in TUG performance from pretest (M = 8.64, SD= 1.52) to posttest (M = 7.32, SD = 1.56), t(16) = -4.60, p \u3c .001, 95% CI [-1.93, -0.71]. A paired-samples t-test demonstrated statistically significant improvement in fall prevention knowledge from pretest (M= 13.18, SD = 2.46) to posttest (M = 14.18, SD = 1.74), t(16) = 2.55, p = .022, 95% CI [0.17, 1.83].
Discussion: The Pasos Seguros program demonstrated significant effectiveness in improving functional mobility among older adults, as evidenced by paired T-test analyses. The 1.32-second mean improvement in TUG performance represents a clinically meaningful change.
Conclusion: These findings provide strong evidence for the integration of culturally-relevant physical activity interventions with structured fall prevention education as an effective approach to improving functional mobility in older adults.https://scholarworks.utep.edu/otcapstones/1017/thumbnail.jp
All We (and LLMs) Need Is Fuzzy: An Argument
No full textLarge Language Models (LLMs) like ChatGPT have spectacular successes -- but they also have surprising failures that an average person with common sense could easily avoid. It is therefore desirable to incorporate the imprecise ( fuzzy ) common sense into LLMs. A natural question is: to what extent will this help? This way, we may avoid a few simple mistakes, but will it significantly improve the LLMs\u27 performance? What portion of the gap between current LLMs and ideal perfect AI-based agents can be, in principle, covered by using fuzzy techniques? Judging by the fact that few researchers working on LLMs (and on deep learning in general) try fuzzy methods shows that most these researchers do not believe that the use of fuzzy techniques will significantly improve LLMs\u27 performance. Contrary to this pessimistic viewpoint, our analysis shows that potentially, fuzzy techniques can cover all the above gap -- or at least a significant portion of this gap. In this sense, indeed, all LLMs need to become perfect is fuzzy techniques
Polymorphisms of delta-aminolevulinic acid dehydratase (ALAD) and peptide transporter 2 (PEPT2) genes in children with low-level lead exposure
No full textLow-level lead exposure during early childhood has long been associated with altered neurocognitive development and diminished cognitive functions. Over nine thousand U.S. industrial facilities annually emit significant amounts of lead, creating exposure risk particularly for minority children. The mechanisms by which low-level lead exerts neurotoxic effects are poorly understood. Once absorbed, the only intervention is source removal, thus primary prevention is key. Genetic biomarkers could provide an efficient means of identifying children at greatest risk. Common functional variants of genes that alter lead’s neurotoxic potential have been identified and include delta-aminolevulinic acid dehydratase (ALAD2) and peptide transporter 2 (PEPT2*2). These polymorphisms have not been examined previously in Hispanic minority samples, or with regard to lowest level lead exposure. In 116 children of Mexican-American/Hispanic descent residing in zip codes previously designated as “high risk” for lead exposure (mean age = 8.1, SD = 1.9), blood lead level was measured at three time points over a three month period and averaged. DNA extraction was completed using buccal swab samples. The frequencies of the ALAD2 and PEPT2*2 polymorphisms observed in this sample closely approximated those previously reported for Anglo, European and Asian samples. As compared to children heterozygous for the PEPT2*2 polymorphism, and without the PEPT2*2 polymorphism, the geometric mean blood lead level of children homozygous for the PEPT2*2 polymorphism was significantly higher. In contrast to past studies, mean blood lead level of children heterozygous and homozygous for the ALAD2 polymorphism in this sample did not differ from that of children without the ALAD2 polymorphism. Higher blood lead burden in children with the PEPT2*2 mutation may suggest that this common genetic variant is a biomarker of increased vulnerability to the neurotoxic effects of lowest level lead exposure
Shapley Value under Interval Uncertainty and Partial Information
No full textIn the 1950s, the future Nobelist Lloyd Shapley solved the problem of how to fairly divide the common gain. Namely, he showed that some reasonable requirements determine a unique division -- which is now known as the Shapley value. The main limitation of Shapley\u27s solution is that it assumes that for each subgroup of the original group of participants, we know exactly how much this group could gain if it acted by itself, without involving others. In practice, we rarely know these exact values. At best, we know the bounds on each such value -- i.e., in other words, an interval that contains this value -- or even have no information about some of these values at all. In this paper, we show that a natural modification of Shapley\u27s conditions enables us to extend Shapley\u27s formulas to this realistic case, when we have interval uncertainty and partial information
To Which Interdisciplinary Research Collaborations Should We Pay More Attention?
No full textInterdisciplinary research is very important in modern science. However, such a research is not easy, it often needs support and help. Resources that can be used for such a support are limited, so we need to decide which of many possible collaborations we should support. In this paper, we provide a natural simple model of collaboration effectiveness. Based on this model, we conclude that we should support collaborations for which the vector product of the participants\u27 knowledge vectors attains the largest values
Conjuring the Moon
No full textConjuring The Moon wrestles with the question of why we as women still submit to norms created by men who can\u27t possibly understand our reality. Why should we support ideologies that claim to represent us while actively working against us? Why should we conform to a system that positions us as inessential Other? The speaker of this book aspires to liberate herself from such burdens. Conjuring the Moon encapsulates one woman\u27s search for the feminine divine within herself, her religion, and her environment; but as empowering as this search may be, it remains inextricably connected to her social and historical role as inessential Other. Narrated primarily from a first-person point-of-view, a consistent, evolving I, inspired by myself, the poems in this book present a woman longing for feminine divinity in a world that reduces her status to inessential Other
Predictive Understanding of Lake Water Temperature and Dissolved Oxygen Profiles Across the Red River Basin Through Interpretable Machine Learning
Get PDFAccurately predicting lake water temperature (LWT) and dissolved oxygen (DO) is crucial for determining threshold values of fish survivability under warmer global conditions, with recreational fishing in reservoirs significantly contributing to regional economies, such as 1,891 million annually to the economies of Oklahoma and Texas, respectively. Current mathematical models for temperature and oxygen profiles, which incorporate multi-layer and turbulent mixing equations, are complex and challenging to parameterize, particularly due to uncertainties in acquiring sufficient data for training and validation. Leveraging the flexibility and information extraction power of machine learning (ML) methods, this master thesis aimed to set up and test ML and deep learning (DL) models to predict LWT and DO across 12 lakes within the Red River Basin of the South in the United States, using historical spatially distributed measurements. Five ML approaches, including Random Forest (RF), Gradient Boosting Extreme (XGBoost), Tree-Boosting with Gaussian Process and Mixed Effects Model (GPBoost), Support Vector Machine (SVM), and Deep Learning (DL), were assessed using numerical k-fold cross-validation metrics. The results highlight GPBoost as the most effective method for predicting LWT and DO, which is attributed to their incorporation of interpretable physical variables. Notably, GPBoost exhibited robust performance under various lake conditions, while RF, XGBoost, and SVR showed signs of overfitting. Comparisons with traditional 1-D numerical approaches underscore the potential of ML algorithms for faster and more precise results, offering valuable insights into the dynamics of lake ecosystems and emphasizing the need for alternative methods to capture their complexities effectively
Physics-Guided Scan Paths Optimization For Controlled Microstructure In Laser Powder Bed Fusion
Get PDFLaser Powder Bed Fusion (L-PBF) is a renowned additive manufacturing technique, celebrated for its capability to construct intricate metal components with remarkable precision. However, one of the main challenges with L-PBF is the formation of complex microstructures, which can significantly impact the final material properties. To address this issue, our study proposes a physics-guided and machine-learning-aided approach to optimize scan paths for achieving desired microstructure outcomes, such as the generation of equiaxed grains that enhance material properties. By using phase-field modeling, a physics-based computational method, we gain insights into microstructure evolution. To reduce computational costs, we train a surrogate machine-learning model using a 3D U-Net convolutional neural network and single-track phase-field simulations as dataset. This enables the machine learning model to predict crystalline grain orientations accurately based on the initial microstructure and thermal history. As a preliminary approach, we investigate three primary scanning strategies; vertical serpentine, spiral serpentine and diagonal scanning at various hatch spacings to identify the most effective paths for achieving the desired microstructure. This lays the foundation for a comprehensive examination of how different scan paths and parameters affect the resulting microstructure. By combining this strategic analysis with our advanced modeling techniques, we provide insights into how scan path influences the attainment of optimal crystalline grain structure in L-PBF processes. This approach not only enhances our ability to predict microstructural outcomes but also advances the precision manufacturing capabilities of L-PBF, merging theoretical knowledge with practical application to guide future advancements in additive manufacturing. Importantly, our methodology achieves a computational time reduction by approximately three orders of magnitude, underscoring the efficiency of our ML approach in accelerating the design process