University of Wisconsin–Whitewater

Abstract Barium stars are unusually enriched in barium ([Ba/Fe] ≥ 1.0 dex) and not predicted by current Galactic chemical evolution models. Previous observations of barium stars have found evidence that they form through mass transfer from a companion asymptotic giant branch (AGB) star or through radiative levitation. The chemical abundance and kinematic information of barium stars may help constrain AGB stellar nucleosynthesis, binary star evolution, and internal evolutionary processes that affect surface abundances. Using ∼450,000 stars from the Galactic Archaeology with Hermes (GALAH) survey, we identify nearly 3000 new barium-rich stars and separate them into hot ( T eff > 6000 K) and cool ( T eff < 6000 K) populations. Crossmatching with Gaia Data Release 3, we find that 47.7% of our barium stars within 1 kpc have an elevated renormalized unit weight error (RUWE; ≥1.4), compared to 16.3% of a comparable sample of the GALAH field, suggesting multiplicity plays an important role in the formation of both populations of barium stars. A subset of hot barium stars exhibit low RUWEs (RUWE < 1.2) and [ α /Fe] < −0.2, supporting radiative levitation as an origin as well. We determine Galactic memberships using both kinematics and chemistry and find that barium stars exist in the thin disk, thick disk, and halo though they are slightly more prevalent at lower metallicities. Overall, we show evidence for barium stars produced by mass transfer and for those produced by radiative levitation, with both formation mechanisms occurring ubiquitously across the Galaxy.

Coping self-efficacy is linked to adaptive coping skills and improved psychological well-being, yet few studies have examined coping self-efficacy in African American adolescents. This study examined change over time in African American adolescents' coping self-efficacy and the extent to which social-contextual factors (maternal warmth and community violence exposure) and gender are associated with coping self-efficacy. Participants were 160 African American adolescents who resided in a large Midwestern city. Ninth-grade adolescents were followed through 10th grade and reported on coping self-efficacy, maternal warmth, and community violence exposure at four 6-month time intervals. Latent growth modeling was used to examine change over time in coping self-efficacy, along with the effects of social-contextual factors and gender on coping self-efficacy. Latent growth modeling results suggest that, on average, coping self-efficacy linearly declined over the course of ninth and 10th grade. Maternal warmth was associated with higher coping self-efficacy over time, whereas community violence exposure was not associated with coping self-efficacy. Girls reported a lower initial level of coping self-efficacy than boys at the start of ninth grade, but there was no difference in the slope (rate of change) of coping self-efficacy between girls and boys. Implications of study results for research and school based, culturally and contextually relevant coping skills intervention for African American adolescents are discussed. Study limitations and future directions are also described. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

ABSTRACT The increasing volume of accounting research presents challenges in efficiently navigating and synthesizing information. We introduce AIRA—an artificial intelligence (AI)-based application developed to assist scholars in consolidating accounting research papers using generative AI models. Traditional literature review methods are time-consuming. AIRA streamlines this process with natural language prompting. To ensure the application’s reliability and usefulness, we use the design science methodology to validate it meets the designed objectives. Overall, faculty found AIRA achieves its objectives of being useful and easy to use, and they plan to continue using it in the future. A large sample of employees from the Securities and Exchange Commission (SEC) also believed the tool would be useful for their work. In examining the first 500 prompts that were entered by anonymous users, we find that it is used for literature search and retrieval, summarization of research findings, and literature review creation, among other tasks.

Urban trees provide essential environmental, health, social, and economic benefits. Consequently, researchers and stakeholders devote considerable effort to characterizing, mapping, and monitoring urban tree species. Traditional identification methods that rely on field surveys are labor-intensive and time-consuming. This study evaluated the potential of field hyperspectral spectroscopy to classify nine common urban tree species at the leaf level. Seven random forest classifiers, each using different combinations of spectral features, were compared for classification accuracy. The model that incorporated both first derivatives of spectral reflectance and vegetation indices achieved the highest overall accuracy (80.4%), whereas the model combining spectral reflectance and vegetation indices had the lowest predictive performance (70.1%). The most influential predictors were spectral bands and first derivatives in the red-edge and SWIR 1 regions; and the vegetation indices Red-edge Vegetation Stress Index (RVSI), Plant Senescence Reflectance Index (PSRI), and Blue Ratio (BR). These results support the use of hyperspectral remote sensing for identifying and classifying urban tree species.

Purpose E-commerce consumers value both forward and reverse logistics services and triple-bottom-line sustainability (cost, CSR and environmental performance). Unfortunately, these aspects of e-commerce can be at odds with one another. Therefore, a major challenge for e-commerce retailers lies in balancing the importance of forward and reverse logistics services with concerns about triple-bottom-line sustainability. To help e-commerce retailers better understand this challenge, our study delves into US consumers' tradeoff preferences related to delivery, returns and sustainability performances. Design/methodology/approach Policy capturing is a method employed by researchers to assess how individual decision-makers use available information when making evaluative tradeoffs between factors. We employ a policy-capturing method with 364 participants. Findings We find different consumers' tradeoff preferences in different groups in the USA. We observed that environmental concerns are paramount for younger respondents, but cost is more important for older respondents. Also, delivery performance is regarded as more significant than returns performance. Research limitations/implications Our findings imply that the demographic background of consumers plays a role in their purchasing behaviors. E-commerce retailers need to consider the psychological attitudes and age of the consumers. Practical implications Environmental performance seems to matter more to younger respondents relative to their older counterparts, so e-commerce retailers will need to adjust their strategies accordingly. We see some examples of e-commerce retailers trying to use this shift in attitudes to their advantage. Originality/value While literature has demonstrated that triple-bottom-line sustainability and logistics service are key consumer considerations in e-commerce, it has not yet been established how consumers evaluate these disparate and sometimes orthogonal factors in tandem. Our research aims to fill this gap.

Mathematics plays a critical role in shaping students’ academic success and future opportunities, yet many students perceive it as difficult and continue to underperform in the G.C.E. O/L Mathematics examination in Sri Lanka. This study applied machine learning techniques to identify the key factors influencing Grade 10 students’ mathematics performance in the Colombo Education Zone, Sri Lanka. Data were collected through systematic sampling of government school students using a structured questionnaire that captured demographic, cognitive, motivational, socioeconomic, and environmental variables. Several models, including regularized regression (Ridge, LASSO and ElasticNet), tree-based algorithms (Decision Tree, Random Forest, and XGBoost), Support Vector Regression (SVR), K-Nearest Neighbors (KNN) and neural networks (Multi-Layer Perceptron), were developed and evaluated. Among these, XGBoost emerged as the bestperforming model, achieving the highest predictive accuracy based on error metrics. The model revealed that high general knowledge, passing the Grade 5 Scholarship Examination, and a strong passion for the subject were the three most impactful variables influencing students’ mathematics performance. In addition, participation in school clubs and societies, higher IQ levels, perceiving mathematics as easier compared to other subjects, receiving additional support outside school, higher family income, supportive home learning environment, and positive peer competition were identified as the next most influential variables, respectively, whereas perceiving mathematics as difficult showed a significant negative effect. These findings reinforce the importance of improving cognitive abilities, strengthening early academic foundations, fostering motivation, and addressing socioeconomic disparities.

Abstract We introduce a consistent method for pricing panels of options through time with a single simulation. The approach is particularly useful for pricing options with early exercise features and allows calibrating flexible option pricing models to large panels of such options. We showcase this by calibrating option pricing models with time-varying volatility and asymmetric features to a sample of more than 25,000 American-style options. Our method leverages the homogeneity of option prices, relies only on polynomial approximations using simulated paths, and could be applied to other problems that require estimating optimal stopping times using dynamic programming.