Greensboro College

Abstract This study investigates the association between Dark Triad traits (DTT), narcissism, Machiavellianism, and psychopathy, and employee review generation and consumption on Glassdoor. Using 533,007 reviews of S&P 500 companies, we applied the Linguistic Inquiry and Word Count method to infer DTT-linked language markers. Results show small but statistically reliable negative associations between narcissism and psychopathy and both review rating and perceived helpfulness. In contrast, Machiavellianism shows a small negative link to review ratings but a positive link to helpfulness. Confidence intervals and incremental fit statistics confirm the modest, context-dependent nature of these effects. Theoretically, the findings link trait-based organizational psychology with communication perspectives on online disinhibition and cue-reduced contexts, showing how antagonistic tendencies can surface in discursive evaluations outside the workplace. The study also advances a behavioral–linguistic approach to measuring personality at scale, complementing traditional self-report methods. Managerially, the results suggest that personality-linked patterns in employee reviews exist but operate alongside situational and platform factors, emphasizing the importance of context when interpreting online employer reputation signals.

The in vitro transformation of plants, or the delivery of foreign genetic material that is incorporated into their genomes, represents a powerful tool both for elucidating genotype-phenotype relationships and for generating plant cultivars which have desirable traits. However, outside of a few model species, the processes involved in transformation are often inefficient, taking months to perform for many plant species, with several bottlenecks at the different stages of calli induction, genetic transfection, and plant regeneration. While duckweeds – aquatic monocots that are the smallest and fastest-growing flowering plants – have distinguished themselves with several emerging biotechnological applications, they too are the subject of conflicting reports regarding their transformation potential. Here, we synthesized and optimized the protocols for in vitro transformation of duckweed Spirodela polyrhiza (Greater Duckweed) from start-to-finish: achieving > 90% − 100% efficiencies for each of calli induction; transient and stable genetic transformation; visual marker-free selection of transformants; and regeneration of genetically modified plants with stable transgene expression for over 100 generations – and which in S. polyrhiza can be achieved over the course of weeks instead of months. These approaches overcome many bottlenecks and help to pave the way for functional genomics studies and synthetic biology applications in this biotechnologically important species. An optimized transformation protocol achieves nearly 100% efficiency in Spirodelapolyrhiza, establishing duckweed as a robust, reliable platform for genetic studies andadvancing plant biotechnology applications.

Randomized response technique (RRT) surveys are designed to secure honest answers to sensitive questions. In this study, we consider the important issue of measurement error (ME). While non-response, a common culprit for survey inaccuracy, is a lesser issue in RRT studies because they are conducted through face-to-face interviews, measurement error is of particular significance. RRT models are generally more complex than other survey methods, sometimes requiring that respondents follow ordered instructions, draw cards from decks, and/or perform simple mathematical calculations. All of these steps can result in measurement errors, and when such error is high, estimation efficiency will suffer. In this study, we consider the impact of measurement error on a Mixture Optional Enhanced Trust (MOET) RRT model proposed in 2024, and we propose new estimators for this model that take measurement error into account. We also study the extent to which measurement error can be tolerated before it is so large that it overwhelms and undermines the benefit that RRT was implemented to yield in the first place (the reduction in or elimination of social desirability bias-related untruthfulness). We also draw attention to a surprising finding—that the presence of measurement error inadvertently serves to provide additional scrambling, thereby leading to an increase in privacy.

The aim was to describe employers' perceptions of what nursing programs do well to prepare students for competent practice as new graduate nurses (NGNs) and areas for improvement. There is a gap in knowledge regarding educators' and employers' expectations and evaluation of competence in NGNs. A qualitative descriptive study of nurse orientation leaders and nurse preceptors was conducted. Major themes were as follows: We Expect Them to Know the Basics, Nursing Schools Do a Pretty Good Job, and Teach Them to Understand the Nurse's Role. Four subthemes included the following: Expose Students to the Reality of Nursing, Focus on Communication Skills, Teach Students to See the Big Picture, and Strategies for Practice Readiness. Nurse educators can help students to integrate knowledge and experience in ways that mimic the actual practice of nursing, thus producing graduates who are better prepared to enter today's complex health care arena.

Effective data representation in machine learning and deep learning is paramount. For an algorithm or neural network to capture patterns in data and be able to make reliable predictions, the data must appropriately describe the problem domain. Although there exists much literature on data preprocessing for machine learning and data science applications, novel data representation methods for enhancing machine learning model performance remain highly absent within the literature. This dataset is a compilation of convolutional neural network model performance trained and tested on a wide range of numerical base representations of the MNIST and MNIST-C datasets. This performance data can be further analysed by the research community to uncover trends in model performance against the numerical base of its data. This dataset can be used to produce more research of the same nature, testing cross-base data encoding on machine learning training and testing data for a wide range of real-world applications.