26419 research outputs found
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Will They Be More Honest With an (External) Proctor? Evaluating UFC Match Performance Before and After the Adoption of the USADA Doping Program
No full textOver the eight years from 2015 to 2023, the Ultimate Fighting Championship (UFC) partnered with the United States Anti-Doping Agency (USADA) to institute comprehensive external oversight of doping controls. This study investigates the effects of transitioning from internally administered anti-doping protocols to exclusive external governance by comparing UFC performance metrics before and during the USADA partnership. Using a Difference-in-Differences (DiD) approach with data from 2012 to 2018, we find that match duration significantly increased, while the likelihood of extreme outcomes, knockouts (KO), technical knockouts (TKO), and submissions, decreased during the USADA period. These results suggest that performance-enhancing drugs (PEDs) could be more prevalent before USADA oversight and declined once stricter testing was introduced. The findings imply that UFC’s move away from USADA could lead to increased PED use among fighters
Integrating Design Thinking in a STEM Methods Course
No full textDesign thinking, a problem-solving approach, has been offered as a strategy to address the challenges of growing and complex teaching expectations. In this narrative, I describe an adapted model of design thinking that was implemented in a secondary STEM methods course for teacher candidates in a graduate program. A description of the key activities, including the fundamental role of empathizing, is shared. Design thinking, when implemented by teachers and teacher candidates, allows the teacher to design and improve a lesson or unit based on student feedback and engagement. This is especially important in today\u27s rapidly evolving educational landscape, where educators must be adaptable and responsive to the diverse needs of their students. Empirical studies examining the use of this strategy are important for establishing further credibility to this approach
Learning Design to Advance Human-AI Collaboration in K-12 Education
No full textThis chapter explores key components for designing effective Human-AI Collaboration (HAC) in K–12 education, addressing the current lack of theoretical and conceptual frameworks for structuring and implementing HAC in teaching and learning. It examines four essential areas: curriculum design, student and teacher–AI interaction, learning environments, and the evolution of HAC over time. The chapter introduces the concept of HAC in K–12 contexts, highlighting how humans and AI can leverage each other\u27s strengths through co-evolutionary processes that foster mutual learning and collaboration. It reviews current HAC practices in schools and discusses their contributions to both teaching and learning. Finally, it presents future directions for HAC research and practice, emphasizing implications for educational policy, AI system design, and instructional design to strengthen human-AI partnerships in education
Untrained Position-Encoded Multilayer Perceptron Network for Structured Illumination Microscopy Reconstruction
No full textStructured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image formation principles is then used to iteratively optimize reconstructions by minimizing the structural similarity loss between the generated images and the acquired SIM data. We demonstrate that PEM-SIM reconstructs both 2D and 3D SIM images with fewer input frames than conventional methods and successfully predicts missing axial planes in 3D stacks. The method shows robustness across varying signal-to-noise ratios and performs comparably to standard algorithms on both synthetic and experimental datasets. By eliminating the dependency on large datasets and enabling flexible, high-resolution reconstructions, PEM-SIM offers a data-efficient alternative for super-resolution imaging in microscopy
Radical-Based Oxidative Pretreatment Enhances Biofuel Production From Lignocellulosic Biomass Via Hydrothermal Liquefaction
No full textThe sustainable production of biofuels from lignocellulosic biomass is a central goal in the transition to low-carbon energy systems. However, hydrothermal liquefaction (HTL), a promising thermochemical conversion pathway, is constrained by the high oxygen content and complex aromatic structure of lignin, which lowers bio-oil quality. Here, we used a model system of brown-rot-degraded white oak (Quercus alba) to test whether radical-based oxidative pretreatment could enhance HTL performance by converting lignin into more aliphatic intermediates. Oxidation was performed under simulated Fenton conditions using fixed Fe(II) (60 ppm) and two hydrogen peroxide concentrations (3 and 8 M), resulting in extensive lignin depolymerization and structural transformation. Solid-state and gel-phase NMR analyses showed a pronounced loss of aromaticity and an increase in aliphatic carbon, including the formation of lipid-like structures. Elemental analysis revealed decreasing carbon content with increasing oxidative severity, accompanied by a higher hydrogen content and elevated H/C atomic ratios. These changes significantly improved the quality of the resulting bio-oils. Oils derived from oxidized samples exhibited high H/C ratios (1.92–2.00), low O/C ratios (0.05–0.07), and larger higher heating values (HHV) of up to 43 MJ/kg, approaching those of petroleum fuels. Gas chromatography demonstrated progressive enrichment in straight-chain n-alkanes (C₁₀–C₂₇), comprising up to 46% of the chromatographic area in the most oxidized sample (8M_HTL). Complementary NMR analysis confirmed the dominance of saturated hydrocarbons, highlighted by strong alkyl signals, with minor contributions from functionalized aliphatics, olefins, and aromatic species. Simulated distillation further indicated favorable boiling point distributions, centered in the kerosene and diesel ranges. Although this study used a simplified lignocellulosic substrate, it provides mechanistic insight into how radical oxidation transforms lignin and enhances HTL performance. These findings establish proof of concept for oxidative pretreatment as a strategy to selectively depolymerize lignin into aliphatic-rich intermediates, thereby improving bio-oil energy quality. This approach may be particularly valuable for valorizing lignin-rich waste streams such as kraft lignin and black liquor from the pulp and paper industry, which remain underutilized despite their potential as advanced biofuel feedstocks
Statistical Learning Methods for Predicting Ordinal and Count Outcomes in Genomics Data
No full textWith advancements in statistical modeling and the growing availability of high-dimensional genomics data, researchers have developed numerous methods to address the challenges and opportunities presented in such data. While many classification or regression techniques exist, their performance often depends heavily on the evaluation criteria used, and it is rarely clear a priori which technique will perform best for a given application. To address this uncertainty, we consider ensemble approaches that leverage bagging and rank aggregation techniques, applied separately to classification and regression problems. In the first part of this work, we introduce an ensemble classifier designed to optimize classification of ordinal outcomes in high-dimensional data. This classifier integrates several existing ordinal classification algorithms, improving predictive performance across multiple evaluation metrics. The second part focuses on ensemble prediction for count outcomes in high-dimensional settings. Similar to the classification framework, this regression ensemble combines multiple existing algorithms to optimize predictive performance across various evaluation criteria. Through extensive simulation studies and real-world genomic applications, we demonstrate that our ensemble methods consistently outperform or closely match the best-performing individual algorithms in both classification and regression contexts. These findings highlight that, when addressing the complexities of high-dimensional data, adopting an ensemble approach that integrates multiple models may provide superior and more reliable performance than relying on a single algorithm
Plastic Waste Imports & Coastal Litter: Evidence from Citizen Science Data
No full textPlastic waste is an internationally traded commodity, where importing countries recycle plastic waste into usable materials. However, there are concerns that the importation process creates plastic litter - a negative externality - in importing countries. While this concern has received much media and policy attention, quantifying the magnitude of this externality has been hindered by a lack of data on plastic litter across countries and over time. To this end, we use unconventional citizen science data on litter from Ocean Conservancy\u27s International Coastal Cleanup, together with the United Nations Comtrade Database, to estimate the correlation between traded plastic waste and coastal litter from 2003 to 2022. We find that a 10% increase in the amount of plastic waste a country imports is associated with a 0.6% increase in the amount of littered plastic collected. Heterogeneity analyses show this correlation is driven by countries with higher rates of waste mismanagement. Our results suggest that the country-and international-level regulatory policies implemented in the waste trade industry since 2018 may have contributed to mitigating plastic pollution
Threads of Empathy: An Integrative Model of Empathy in the Schools
No full textEmpathy has a distinctive and evolving history in education for cultivating human relationships and engendering mutual understanding. Within a school setting, empathic engagement is essential for the social and emotional development of children and adolescents. From an educational perspective, literature from multiple disciplines and specialties illustrate innovative practices to harness empathy in the classroom and beyond. The representative initiatives hold promise for developing an integrative model of empathy within a school or a school district through a program involving interprofessional collaboration