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In the United States, academic advising is among the highest turnover professions in the university system. Academic advisors, who work at the intersection of academics and student life, bear the brunt of increased pressures and decreased resources. Yet, primary-role advisors often do not experience high salaries or opportunities for advancement. Despite a high turnover in advising, some advisors have intentionally chosen to remain in advising. Framed by the social cognitive model of career self-management, this phenomenological study examined the workplace career management of female primary-role advisors at public institutions in the midwestern region of the United States. Through holistic coding, three primary factors emerged from the data: students, supportive environments, and balance and benefits. We offer implications for practice.

Metal hexafluorides hydrolyze at ambient temperature to deposit compounds having fluorine-to-oxygen ratios that depend upon the identity of the metal. Uranium-hexafluoride hydrolysis, for example, deposits uranyl fluoride (UO2F2), whereas molybdenum hexafluoride (MoF6) and tungsten hexafluoride deposit trioxides. Here, we pursue general strategies enabling the prediction of depositing compounds resulting from multi-step gas-phase reactions. To compare among the three metal-hexafluoride hydrolyses, we first investigate the mechanism of MoF6 hydrolysis using hybrid density functional theory (DFT). Intermediates are then validated by performing anharmonic vibrational simulations and comparing with infrared spectra [McNamara et al., Phys. Chem. Chem. Phys. 25, 2990 (2023)]. Conceptual DFT, which is leveraged here to quantitatively evaluate site-specific electrophilicity and nucleophilicity metrics, is found to reliably predict qualitative deposition propensities for each intermediate. In addition to the nucleophilic potential of the oxygen ligands, several other contributing characteristics are discussed, including amphoterism, polyvalency, fluxionality, steric hindrance, dipolar strength, and solubility. To investigate the structure and composition of pre-nucleation clusters, an automated workflow is presented for the simulation of particle growth. The workflow entails a conformer search at the density functional tight-binding level, structural refinement at the hybrid DFT level, and computation of a composite free-energy profile. Such profiles can be used to estimate particle nucleation kinetics. Droplet formation is also considered, which helps to rationalize the different UO2F2 particle morphologies observed under varying levels of humidity. Development of predictive methods for simulating physical and chemical deposition processes is important for the advancement of material manufacturing involving coatings and thin films. [ABSTRACT FROM AUTHOR]

Metal hexafluorides hydrolyze at ambient temperature to deposit compounds having fluorine-to-oxygen ratios that depend upon the identity of the metal. Uranium-hexafluoride hydrolysis, for example, deposits uranyl fluoride (UO2F2), whereas molybdenum hexafluoride (MoF6) and tungsten hexafluoride deposit trioxides. Here, we pursue general strategies enabling the prediction of depositing compounds resulting from multi-step gas-phase reactions. To compare among the three metal-hexafluoride hydrolyses, we first investigate the mechanism of MoF6 hydrolysis using hybrid density functional theory (DFT). Intermediates are then validated by performing anharmonic vibrational simulations and comparing with infrared spectra [McNamara et al., Phys. Chem. Chem. Phys. 25, 2990 (2023)]. Conceptual DFT, which is leveraged here to quantitatively evaluate site-specific electrophilicity and nucleophilicity metrics, is found to reliably predict qualitative deposition propensities for each intermediate. In addition to the nucleophilic potential of the oxygen ligands, several other contributing characteristics are discussed, including amphoterism, polyvalency, fluxionality, steric hindrance, dipolar strength, and solubility. To investigate the structure and composition of pre-nucleation clusters, an automated workflow is presented for the simulation of particle growth. The workflow entails a conformer search at the density functional tight-binding level, structural refinement at the hybrid DFT level, and computation of a composite free-energy profile. Such profiles can be used to estimate particle nucleation kinetics. Droplet formation is also considered, which helps to rationalize the different UO2F2 particle morphologies observed under varying levels of humidity. Development of predictive methods for simulating physical and chemical deposition processes is important for the advancement of material manufacturing involving coatings and thin films. [ABSTRACT FROM AUTHOR]

Developing accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS‐NG imaging spectrometer (or "hyperspectral") data from NASA's 2021 Delta‐X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880–1,000 and 1,080–1,200 nm near‐infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave‐one‐out‐cross‐validation as the PLSR models (R2 = 0.43, mean absolute error = 257.3 g/m2) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems. Plain Language Summary: Developing accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding the sustainability of coastal deltaic systems, as plant biomass aids in a wetland maintaining its elevation in the face of relative sea level rise. Here we use imaging spectroscopy, or "hyperspectral" remote sensing, to map wetland biomass across seasons in coastal Louisiana. In doing so, we correct for the noise caused by high atmospheric water vapor content in important infrared bands, thereby improving our biomass models. We test empirical and machine learning models for estimating wetland biomass, determining that a machine learning regression model using our corrected data delivers the best model metrics and map quality. We thus map AGB in April and August 2021, across the Atchafalaya and Terrebonne Basins. Our maps show various seasonal growth patterns, with the Atchafalaya and Terrebonne Basins' herbaceous AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. Key Points: We developed an interpolation algorithm to correct noise in near‐infrared bands that improved wetland aboveground biomass (AGB) estimatesWe assessed model approaches and mapped wetland AGB in coastal Louisiana with a machine learning algorithm for March and August 2021The active Atchafalaya and inactive Terrebonne Basins' AGB increased from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively [ABSTRACT FROM AUTHOR]

The purpose of active learning is to solicit participation by students beyond the passive mode of traditional classroom lectures. Reading, writing, participating in discussions, hands-on activities, engaging in active problem solving, and collaborative learning can all be involved. The skills acquired during active learning tend to go above and beyond basic comprehension of information covered during a lecture. In fact, the goal of active learning is to not only enable student comprehension, but also to assist the student in cultivating valuable aptitudes for synthesizing, analyzing, and evaluating ideas and their learning potential. This captures a significantly larger portion of the Bloom's Taxonomy than would be available in a lecture-only situation. One model for active learning takes the form of tutorials, or more accurately described as active learning modules (ALMs), aimed at improving student learning in historically difficult subject areas in engineering through the application of finite element analysis. The tutorial set developed here includes learning modules for various subject areas in Mechanical, Electrical, and Biomedical Engineering courses. The aim of this study is to determine if ALMs of this type are, in fact, effective active learning tools. In each participating course, after the student completes their traditional lecture series, they are introduced to a computer-based ALM. In order to perform a baseline study, students are administered content quizzes before and after the completion of the module. These quiz results are statistically analyzed to determine if subject aptitude, including comprehension, is improved. The incorporation of a novel assessment methodology reinforces the project goals as we are able to judge if these modules afford all students with an equal active learning process experience. The ALMs are shown to be a successful step towards improving aptitude and comprehension of challenging engineering content in an active learning environment.

Abstract Background Since the advent of microarray technology, numerous methods have been devised to infer gene regulatory relationships from gene expression data. Many approaches that infer entire regulatory networks. This produces results that are rich in information and yet so complex that they are often of limited usefulness for researchers. One alternative unit of regulatory interactions is a linear path between genes. Linear paths are more comprehensible than networks and still contain important information. Such paths can be extracted from inferred regulatory networks or inferred directly. Since criteria for inferring networks generally differs from criteria for inferring paths, indirect and direct inference of paths may achieve different results. Results This paper explores a strategy to infer linear pathways by converting the path inference problem into a shortest-path problem. The edge weights used are the negative log-transformed probabilities of directness derived from the posterior joint distributions of pairwise mutual information between gene expression levels. Directness is inferred using the data processing inequality. The method was designed with two goals. One is to achieve better accuracy in path inference than extraction of paths from inferred networks. The other is to facilitate priorization of interactions for laboratory validation. A method is proposed for achieving this by ranking paths according to the joint probability of directness of each path's edges. The algorithm is evaluated using simulated expression data and is compared to extraction of shortest paths from networks inferred by two alternative methods, ARACNe and a minimum spanning tree algorithm. Conclusions Direct path inference appears to achieve accuracy competitive with that obtained by extracting paths from networks inferred by the other methods. Preliminary exploration of the use of joint edge probabilities to rank paths is largely inconclusive. Suggestions for a better framework for such comparisons are discussed.

We report detailed observations of K‐leaders and the activity between them with the three‐dimensional Broadband Interferometric Mapping and Polarization system (BIMAP‐3D) at Los Alamos National Laboratory. It is found that K‐leaders have a general propagation trend of initial acceleration and then gradual deceleration, and the corresponding very high frequency (VHF) radiation power is exponentially correlated with the leader speed. Based on the 3D development and simultaneous electric field change measurement, some simple K‐leaders can be modeled with time‐evolving point charges at the propagating leader tip and at the stationary origin. We found that the charge magnitude increases during the initial acceleration stage and stays relatively constant for the rest of the development. K‐leaders are observed to interact with other branches; the branches affect the leader's propagation speed, and may affect the charge transfer. After the occurrence of a K‐leader, VHF emissions are quenched for several milliseconds. VHF sources then reappear in an impulsive and scattered manner as "twinkling," and these sources are found not uniquely on the so‐called needles, but also on the main channel. These twinkling sources start near the apparent positive leader tip, and extend back toward the direction of the flash origin at about 105 m/s, while the apparent positive tip continues to extend forward at about 104 m/s. The twinkling extending toward the direction of flash origin appears to initiate the following K‐leader, although it may be interrupted by a K‐leader along a different branch, or simply die out without more K‐leader activity. Plain Language Summary: A K‐leader is a discharge process that occurs at the later stage of a lightning flash. It retraces the path established by earlier discharges and propagates at a high speed of 106–107 m/s. Recently we developed a new system called BIMAP‐3D that can map lightning radio sources in 3D at a spatial resolution of 10 m and at a time resolution of a fraction of a microsecond. We found that K‐leaders commonly speed up from 106 to 107 m/s at the initial stage and then gradually slow down to a stop at their later stage, with associated radio power positively correlated with the traveling speed. Other branches in the lightning flash are found to affect the K‐leader speed as it approaches and passes the branch junctions due to charge redistribution caused by the earlier processes. After the occurrence of a K‐leader radio emissions are shut off for a few milliseconds due to the increased conductivity of the leader. After that, scattered radio sources reappear in an expanding region, both extending the branch and expanding back toward the starting point of the lightning. These apparent twinkling radio sources lead to the start of the next K‐leader. Key Points: K‐leader propagation often exhibits an initial acceleration followed by a gradual decelerationK‐leaders often sharply decelerate and then sharply re‐accelerate as they approach and pass branch junctions in the flash structureA bidirectionally extending region of "twinkling" very high frequency sources leads to the initiation of the next K‐leader [ABSTRACT FROM AUTHOR]

Stroke survivors struggle to meet clinical recommendations for physical exercise duration and intensity. During the past two decades, emerging evidence has shown effectiveness of music interventions for several motor tasks in stroke rehabilitation. Additionally, music has been found effective for increasing exercise performance in athletes and clinical populations. It is postulated that the therapeutic effects of music in physical exercise are modulated by preference and task-specificity of the music. We tested this hypothesis in a pilot study using a three-arm randomized cross-over design comprising the following auditory conditions during cycle ergometry cardiorespiratory exercise sessions: (a) a group-tailored music playlist, (b) radio music, and (c) a non-music control condition. Participants (n = 19) were inpatient stroke survivors undergoing rehabilitation between 2 and 12 weeks post infarct. Our results demonstrate that clinical characteristics are an important determinant for identifying patients who can benefit from music. Specifically, participants with a higher level of gait functioning experienced no benefit, whereas participants with a low level of gait functioning showed an increase in both exercise duration (M = 4.46 minutes) and time spent in the recommended heart rate intensity (M = 6.39 and M = 2.49 minutes for the playlist and radio condition, respectively). The findings suggest a beneficial role of music in rehabilitation of stroke patients with low gait functioning. Future studies should ultimately disentangle which musical parameters are more likely to induce the putative ergogenic effects. Trial registry number: NCT05398575. [ABSTRACT FROM AUTHOR]