23671 research outputs found
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Department of Dental Hygiene Class of 1979
No full textFirst row (left to right): M. Stevens, Director; M. Clark, Instructor; K. Cohenour, Instructor; D. Huntley, Instructor; M. McLure, Instructor; L. Wolf, Instructor, Dr. P. Bradley, Supervising Dentist; Dr. S. Moore, Supervising DentistSecond row (left to right): C. Bosiljevac, President; S. Cotton, Vice-president; D. Snyder, Sec. TreasurerThird row (left to right): S. Hiebert, President of JADHA; D. Farmer, Sec. Treasurer of JADHA; J. French, Social Chairman of JADHA; K. Anders, S. Arbuthnot, B. Bachrodt, M. Blair, T. Boese, N. BurrichterFourth row (left to right): C. Clumsky, L. Cole, R. Crowley, D. Crumley, K. Dickerson, S. Duncan, J. Esau, C. Ferguson, M. GlennFifth row (left to right): D. Harris, J. Hilmes, B. Johnson, V. LaClair, E. Lockwood, K. Mize, V. Rush, D. TheisDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Comparing the cognitive bias scale and cognitive bias scale of scales to other personality assessment inventory validity scales for detecting noncredible memory dysfunction in a clinical veteran sample
No full textClick on the DOI link to access this article at the publishers website (may not be free).Introduction: The Cognitive Bias Scale (CBS) and the three Cognitive Bias Scale of Scales (CB-SOS) were developed for the Personality Assessment Inventory (PAI) to assess for cognitive response bias in neuropsychological settings and populations. While cross-validation research to date has been supportive, the scales have yet to be validated in a clinically referred veteran sample. Method: Patients (N = 235) were clinically referred veterans who underwent neuropsychological evaluations. Individuals were classified into valid or invalid memory performance groups based on a criterion performance validity test. The CBS, the three CB-SOS, and multiple core and supplemental PAI symptom validity indices were examined. Results: Both the CBS and the three CB-SOS had large correlations with multiple over-report validity scales, and high concurrent elevation rates were observed across many of the over-report validity scales. The greatest area under the curve rates (i.e.70 or above) were seen for the CBS, two of the CB-SOS, and one psychiatrically focused validity index. When maintaining specificity at ≥90%, the CBS and two of the CB-SOS demonstrated the best sensitivity rates (i.e. 28–29%). Conclusions: While the CBS and the three CB-SOS have strong positive relationships with psychiatrically-focused over-report validity indices, the CBS and two of the CB-SOS demonstrated the best classification accuracy rates for identifying noncredible memory impairment. The cutoff scores and classification accuracy findings are in line with other published research results, suggesting good generalization to a clinically referred veteran sample. Additional conclusions regarding other findings are drawn and discussed. © 2025 Informa UK Limited, trading as Taylor & Francis Group
Activated backstepping with control barrier functions for the safe navigation of automated vehicles
No full textThis is an open access article under the CC BY license.This paper introduces a novel safety-critical control method through the synthesis of control barrier functions (CBFs) for systems with high-relative-degree safety constraints. By extending the procedure of CBF backstepping, we propose activated backstepping—a constructive method to synthesize valid CBFs. The novelty of our method is the incorporation of an activation function into the CBF, which offers less conservative safe sets in the state space than standard CBF backstepping. We demonstrate the proposed method on an inverted pendulum example, where we explain the underlying geometric meaning in the state space and provide comparisons with existing CBF synthesis techniques. Finally, we implement our method to achieve collision-free navigation for automated vehicles using a kinematic bicycle model in simulation. © 2017 IEEE
Innovations in powder materials for binder jetting
No full textPublished in SOAR: Shocker Open Access Repository by the Wichita State University Libraries Technical Services, October 2025.Additive manufacturing has evolved from a prototyping tool to a primary method for producing functional, end-use products. Binder jetting, in particular, has gained significant attention in the construction industry. This paper reviews innovative powder material mixes used in binder jetting techniques. These materials, combined with modifications to printing parameters such as layer thickness and binder saturation level, offer improvements in mechanical strength, dimensional accuracy, and the sustainability of 3D-printed objects. The review highlights recent experimental studies that investigate the effects of powder composition, binder saturation, and post-processing techniques. The findings provide insights into optimizing material behavior and mechanical performance for construction-grade applications
Department of Dental Hygiene Class of 2020
No full textFirst row (left to right): Lauren Paul, SADHA President; Mati Bickhard, SADHA Treasurer; Ally Tozier, SADHA Secretary; Baylee Pitts, SADHA Class Representative; Southima Viengluang, SADHA Class RepresentativeSecond row (left to right): Caressa Bartling, Brooklyn Bosch, Ashley Burtness, Alanis Do, Nicole Greene, Amy Hallberg, Allyssa Kirkham, Kathleen Lai, Lena LeThird row (left to right): Jimena Marrufo, Kendi Maxwell, Brittney McNown, Christine Morgan, Sydney Morris, Michelle Palmer, Karisma Pava, Megan Pedersen, Courtney RalstinFourth row (left to right): Aaliyah Reyes, Erin Robertson, Chelsea Schwartz, Mariah Seiler, Nadia Smith, Sara Stiles, Melissa Stout, Cassie Sullivan, Jenna TammerineDigitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only
Measurement of d2σ/d | q → |dEavail in charged current νμ -nucleus interactions at Eν=1.86 GeV using the NOvA Near Detector
No full textThis is an open access article under the CC BY license.Double- and single-differential cross sections for inclusive charged-current νμ-nucleus scattering are reported for the kinematic domain 0 to 2 GeV/c in three-momentum transfer and 0 to 2 GeV in available energy, at a mean νμ energy of 1.86 GeV. The measurements are based on an estimated 995,760 νμ charged-current (CC) interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole (2p2h) reactions is identified by the cross section excess relative to predictions for νμ-nucleus scattering that are constrained by a data control sample. Models for 2-particle-2-hole processes are rated by χ2 comparisons of the predicted-versus-measured νμ CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based València and SuSAv2 2p2h models. © 2025 authors. Published by the American Physical Society.University of Minnesota, UMN; National Science Foundation, NSF; Science and Technology Facilities Council, STFC; GA UK, Czech Republic; Department of Science and Technology, Ministry of Science and Technology, India, DST; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Russian Foundation for Basic Research, RFBR; U.S. Department of Energy, USDOE; Fundação de Amparo à Pesquisa do Estado de Goiás, FAPEG; Fermilab, FNAL; Office of Science, SC; Royal Society; European Research Council, ERC; Ministry of Science and Higher Education of the Russian Federation; Ministerstvo Školství, Mládeže a Tělovýchovy, MŠMT; Rochester Academy of Science, RAS; UK Research and Innovation, UKRI; Fermi Research Alliance, LLC, (DE-AC02-07CH11359)This document was prepared by the NOvA collaboration using the resources of the Fermi National Accelerator Laboratory (Fermilab), a U.S. Department of Energy, Office of Science, HEP User Facility. Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359. This work was supported by the U.S. Department of Energy; the U.S. National Science Foundation; the Department of Science and Technology, India; the European Research Council; the MSMT CR, GA UK, Czech Republic; the RAS, Ministry of Sciences and Higher Education (MSHE), and RFBR, Russia; CNPq and FAPEG, Brazil; UKRI, STFC and the Royal Society, United Kingdom; and the state and University of Minnesota. We are grateful for the contributions of the staffs of the University of Minnesota at the Ash River Laboratory, and of Fermilab
Advancing data privacy and security in space situational awareness with adversarial machine learning and diffusion techniques
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, School of ComputingSpace Situational Awareness (SSA) plays a critical role in monitoring space debris and ensuring safe satellite operations. This dissertation introduces TLE-SafeguardNet, a novel framework designed to protect the privacy of Two-Line Element (TLE) data, a key component for accurate SSA. The proposed method combines Singular Value Decomposition (SVD) with a forward diffusion noising process, which enhances the confidentiality of TLE data while preserving its utility for downstream tasks such as satellite classification.
To evaluate the effectiveness of this framework, we train a multilayer perceptron (MLP) network on the 2023 Q4 TLE dataset. Visualization techniques like t-distributed Stochastic Neighbor Embedding (t-SNE) reveal that the MLP successfully clusters payloads and debris based on eight dominant features extracted from the data. Moreover, SHapley Additive exPlanations (SHAP) plots provide interpretability by highlighting the most important features influencing the model’s decisions. The robustness of the method against noise is evaluated using a random forest classifier with 10-fold cross-validation, showing that noise can be introduced up to 401 time steps without significantly impacting data integrity.
While SSA data is crucial for satellite operators to avoid collisions, it is also vulnerable to exploitation by adversaries who can use it to launch both kinetic and non-kinetic attacks on satellites. Traditional methods, such as labeling a satellite as debris, aim to hide its true identity. However, adversaries can often deduce the satellite’s actual label based on orbit information. In furthering this work, we also propose a novel adversarial machine learning approach that conceals the true identity of sensitive satellites in SSA data by modifying both the satellite’s label and orbit information. Our experimental results show that this method reduces an adversary’s ability to correctly identify a concealed satellite’s true label to less than 50%, thus improving space data security and safeguarding satellite operations from potential adversarial threats
