University of Wisconsin–Green Bay

ABSTRACT During lunge feeding, rorquals depress their lower jaw to fill the buccal pouch, which can produce a downward pitch on the body from a drag force below the center of mass. It is hypothesized that without flippers the raised upper jaw could produce a counteracting lift to trim the body. Upper jaws of the blue whale and humpback whale have triangular shapes that resemble delta wings. Such wings generate lift without stalling at high angles of attack. To test whether the upper jaw is acting like a delta wing, planform shapes of jaws were tested with multiaxis force transducer in a flow tank at angles of attack. These 2‐dimensional models performed like delta wings without stalling. A CFD model of an inflated buccal pouch indicated that pouch drag could be modeled as a flat plate. Comparison of flume and CFD data, on the other hand, showed pouch lift as more dependent on its degree of streamlining. Three‐dimensional models of whale bodies with the lower jaw angled down at 90 o and the upper jaw angled at 0 o , 15 o , and 30 o were tested in a flow tank. Without the flippers, increasing the angle of the raised upper jaw acted as a delta wing that produces most of the lift needed to negate downward pitching due to the drag of the depressed lower jaw and inflated buccal pouch.

Abstract Artificial Intelligence (AI) is reshaping the nature, value, and dynamics of knowledge assets in organizations. This chapter offers a foundational exploration of AI-driven knowledge management assets, emphasizing how emerging technologies create, enhance, and transform these assets across industries. We begin by classifying core types of knowledge assets, including human, structural, and relational assets, and then examine the emergence of AI-generated content, codified intelligence, and autonomous process insights as new forms of digital capital. Particular focus is given to AI’s role in business process automation, where algorithmic knowledge increasingly serves as a central asset. We also present a framework for integrating AI-generated knowledge into traditional enterprise systems and human workflows, emphasizing the balance between augmentation and automation. This chapter positions AI-enhanced knowledge assets not as replacements for human contribution but as catalysts that elevate decision-making, innovation, and organizational adaptability.

Naturally occurring Ca-bentonite and kaolinite clay minerals acted as low-cost adsorbents for removing phosphorus (P) from waste water and soil. We evaluated the potential of engineered clay pellets prepared with...

Accurate estimation of urine output is essential for assessing nutrient utilization, particularly for nutrients predominantly excreted through urine, such as minerals and protein-derived metabolites. An equation utilizing creatinine concentration and BW has been previously developed to estimate urinary volume (EUV) in lactating Holstein dairy cows, defined as EUV = [29 × BW (kg)]/[urinary creatinine (mg/L)]. Our objective was to evaluate whether this equation introduces bias when used for estimating urinary output in nonpregnant, nonlactating Holstein cows and to identify factors influencing its accuracy. For our study, we used 72 paired observations that included observed urinary volume (OUV) and urinary creatinine concentration. These observations were obtained from a prior research study that assessed the relative availability of various magnesium (Mg) sources using a duplicated 6 × 6 Latin square design, with cows (n = 12) grouped into squares based on lower (square 1) or higher (square 2) BW across 6 periods. Enrolled cows were of second parity (n = 8) and third or greater parity (n = 4), with BW ranging from 590 to 831 kg. To determine the agreement between EUV and OUV, we constructed 2 mixed-effects models. The first model evaluated slope bias (testing if the slope was significantly different from 1), whereas the second assessed mean bias (testing if the intercept was significantly different from 0) between EUV and mean-centered OUV. In our dataset, creatinine excretion per kilogram of BW ranged from 15.0 to 35.6 mg/kg BW with an average of 27.6 mg/kg BW. When assessing the agreement between OUV and EUV, we observed both slope and mean biases when applying the creatinine-based equation. Furthermore, there was a bias estimate across BW quartiles. Overall, 76.4% of observations fell within ±10% deviation range between EUV and OUV. These findings suggest that further research is needed to identify factors that can refine the creatinine and BW-based predictive equation specifically for nonpregnant, nonlactating Holstein dairy cows.

This study explores the impact of key factors on project management (PM) strategies within R&D units of SMEs. We present a case study of an SME engaged in the manufacturing of battery packs highlighting the critical need for adaptable PM methodologies to the specific characteristics of the company. Facing dynamic market conditions due to its growth rate, the SME sought a sustainable and flexible PM approach. The implementation of a hybrid agile–stage–gate methodology demonstrates the practical integration of identified parameters, offering valuable insights for SMEs navigating R&D challenges.

ABSTRACT The relationship between military spending and economic growth is debated, especially around how defense R&D creates technologies that benefit civilians. New innovations in artificial intelligence, quantum computing, and advanced materials have shifted this discussion beyond simple trade-offs. We model how perceived threats emerge from relative military capabilities and how defense R&D generates knowledge externalities enhancing civilian productivity. Our framework yields several notable insights: stronger dual-use spillovers increase equilibrium growth rates while potentially raising military expenditures; international knowledge diffusion generates asymmetric effects on defense spending incentives across technologically advanced and developing countries; and non-cooperative military spending decisions typically result in inefficient arms races. The equilibrium characterized by our model exhibits excessive military spending relative to the social optimum due to the negative security externality, highlighting potential welfare benefits from international coordination mechanisms. By incorporating realistic features including imperfect competition, non-constant returns, and heterogeneous spillovers, we provide a comprehensive theoretical foundation for understanding when military expenditures complement or constrain economic development. Our results contribute to debates on defense industrial policy, technology transfer regulations, and international security cooperation in an era of intensifying technological competition.

Orthogonal time frequency space and non-orthogonal multiple access (OTFS-NOMA) protocol have increasingly become well-suited candidates for dynamic vehicle-to-everything (V2X) communications. However, the stochastic characteristics cannot be fully addressed for existing UAV-assisted communication systems. To address this problem, we propose a deep reinforcement learning method, known as Twin Delayed Deep Deterministic Policy Gradient (TD3), for allocating communication resources in unmanned aerial vehicle (UAV)-Assisted OTFS-NOMA System. In particular, we implement the gate recurrent units (GRUs) to learn the deterministic optimal time-varying actions with imperfect channel state information (CSI). Simulation results demonstrate the proposed TD3-based approach achieves a substantial reduction in total transmission delay and communication latency, while maintaining effective convergence under dynamic channel conditions. These results validate the practicality of TD3 in enhancing reliability and resource utilization for UAV-assisted OTFS NOMA networks.

Introduction and Objective: The prevalence of DKA at diagnosis of T1D varies considerably by region, with rates increasing. U.S. studies have been limited to certain geographic locations and have not included Wisconsin youth. Here, we evaluate the prevalence of DKA at diagnosis with T1D in Wisconsin youth and determine which social determinants of health (SDoH) are independently correlated with DKA. Methods: A retrospective chart review was conducted of youth aged <18 years newly diagnosed with T1D through Children’s Wisconsin between 2021-2023. DKA at diagnosis was the dependent variable. Independent variables included age, race/ethnicity, insurance, HbA1c, and Area Deprivation Index (ADI), a composite measure of neighborhood socioeconomic disadvantage (determined by home address). Unadjusted and adjusted logistic regression models were used to identify independent correlates of DKA. Results: Of 426 youth newly diagnosed with T1D, 55% presented in DKA. Those with DKA versus those without DKA were 9.6 vs 9.3 years old; 56% vs 58% male; and 90% vs 71% had an initial HbA1c ≥ 10%, respectively. The DKA vs no-DKA groups were 64% vs 81% NHW; 16% vs 5% NHB; 10% vs 10% Hispanic. There was a mean national ADI of 55.7%ile vs 49%ile and 43% vs 24% had public insurance, in the DKA and no-DKA groups, respectively. Sixty-four (27%) of those presenting in DKA had seen a healthcare provider in the previous month and T1D was not diagnosed. Independent correlates of DKA included age, race/ethnicity, insurance type, and HbA1c. Older age was associated with lower odds of DKA at diagnosis (OR: 0.95; 95%CI: 0.90, 0.99). Increased odds of DKA were associated with NHB race/ethnicity (OR:2.58; 95%CI: 1.03,6.5), and public insurance (OR:1.79; 95%CI: 1.07, 3.00). An HbA1C ≥ 10% increased odds of DKA relative to HbA1c <8%. Conclusion: The prevalence of DKA at diagnosis of T1D in Wisconsin youth is 55%, with younger age, public insurance, and NHB race associated with DKA. ADI, a measure of SDoH, was not independently correlated with DKA. Disclosure L. von Zabern: None. I.M. Rizky: None. A.Z. Dawson: None. J.S. Williams: None. S.M. Cabrera: None.

ABSTRACTRecent research has acknowledged that a large and growing number of consumers worldwide have adopted a minimalistic philosophy and engaged in minimalistic consumption. However, extant research has generally grouped all minimalists together, thus limiting the theoretical and practical relevance of minimalism to the field of marketing. In delving deeper into prior work, it can be posited that minimalism, despite its overall focus on simplification, takes various forms driven by different motivations. We critically analyze prior conceptualizations of minimalism using a multi‐method approach to develop and validate a scale that captures different types of motivations driving minimalistic consumption. The scale enables researchers and practitioners to better understand minimalism, measure minimalistic consumption for each unique minimalistic consumption motivation, and tailor their approaches to appeal to different types of minimalistic consumers.