Impact of Nurse Manager Peer Mentorship Program on Job Satisfaction and Intention to Stay

Nurse managers (NMs) play a vital role in patient outcomes by providing a stable work environment for teams. Numerous factors influence a NM’s job satisfaction and intent to remain in a job. The purpose of this project was to develop an evidence-based formal mentorship program for NMs in an effort to impact retention rates. A secondary purpose was to evaluate the impact that a formal mentorship program has on NMs’ job satisfaction and intent to stay. Across two hospitals in the Pacific Northwest, 15 NMs participated in a 6-month mentorship program. The program was guided by both the mentorship enactment theory and Kouzes and Posner’s exemplary leadership framework. Using the Leadership Practices Inventory and the Nurse Manager Practice Environment scale, job satisfaction, intent to stay in a job, and transformational leadership behaviors were measured before and after the program, Results were analyzed using a paired-samples t test. There were statistically significant differences between the preprogram Leadership Practices Inventory scores (M = 212.27, SD = 37.8) and postprogram scores (M = 232.47, SD = 25.28); t(14) = –2.83, p = .013. There were also statistically significant differences between the preprogram Nurse Manager Practice Environment Scale–Culture of Generativity subscale (M = 23.20, SD = 4.65) and post-program scores (M = 26.20, SD = 4.74); t(14) = –2.40, p = .032. The results demonstrated a significant increase in job satisfaction, intent to stay, and transformational leadership behaviors. Implementation of this pilot program supported positive social change through reduced NM turnover, resulting in a reduction of healthcare spending on replacement costs in addition to improved patient outcome

Machine learning -- based diffractive imaging with subwavelength resolution

Far-field characterization of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new imaging technique capable of fast and accurate characterization of two-dimensional structures with at least wavelength/25 resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180-nm-scale features with 532-nm laser light. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new characterization tools with high spatial resolution, fast data acquisition, and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscop

Disentanglement of Correlated Factors via Hausdorff Factorized Support

A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a models representations with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumption: that factors are statistically independent. In reality, factors (like object color and shape) are correlated. To address this limitation, we propose a relaxed disentanglement criterion - the Hausdorff Factorized Support (HFS) criterion - that encourages a factorized support, rather than a factorial distribution, by minimizing a Hausdorff distance. This allows for arbitrary distributions of the factors over their support, including correlations between them. We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over +60% in relative improvement over existing disentanglement methods. In addition, we find that leveraging HFS for representation learning can even facilitate transfer to downstream tasks such as classification under distribution shifts. We hope our original approach and positive empirical results inspire further progress on the open problem of robust generalization

Dynamic dielectric metasurfaces via control of surface lattice resonances in non-homogeneous environment

Dynamic control of metamaterials and metasurfaces is crucial for many photonic technologies, such as flat lenses, displays, augmented reality devices, and beam steering, to name a few. The dynamic response is typically achieved by controlling the phase and/or amplitude of individual meta-atom resonances using electro-optic, phase-change or nonlinear effects. Here, we propose and demonstrate a new practical strategy for the dynamic control of the resonant interaction of light with dielectric metasurfaces, exploiting the dependence of the interaction between meta-atoms in the array on the inhomogeneity of the surrounding medium. The revealed tuning mechanisms are based on the concept of the surface lattice resonance (SLR), the development of which strongly depends on the difference between permittivities of superstrate and substrate materials. We experimentally demonstrate surface lattice resonances in dielectric (Si) metasurfaces, and reveal two tuning mechanisms corresponding to shifting or damping of the SLR in optofluidic environment. The demonstrated dynamic tuning effect with the observed vivid colour changes may provide a dynamic metasurface approach with high spectral selectivity and enhanced sensitivity for sensors, as well as high-resolution for small pixel size displays.Comment: Main text: 10 pages, 4 figures. Supplementary information: 18 pages, 14 figure

Population genetic diversity and fitness in multiple environments

<p>Abstract</p> <p>Background</p> <p>When a large number of alleles are lost from a population, increases in individual homozygosity may reduce individual fitness through inbreeding depression. Modest losses of allelic diversity may also negatively impact long-term population viability by reducing the capacity of populations to adapt to altered environments. However, it is not clear how much genetic diversity within populations may be lost before populations are put at significant risk. Development of tools to evaluate this relationship would be a valuable contribution to conservation biology. To address these issues, we have created an experimental system that uses laboratory populations of an estuarine crustacean, <it>Americamysis bahia </it>with experimentally manipulated levels of genetic diversity. We created replicate cultures with five distinct levels of genetic diversity and monitored them for 16 weeks in both permissive (ambient seawater) and stressful conditions (diluted seawater). The relationship between molecular genetic diversity at presumptive neutral loci and population vulnerability was assessed by AFLP analysis.</p> <p>Results</p> <p>Populations with very low genetic diversity demonstrated reduced fitness relative to high diversity populations even under permissive conditions. Population performance decreased in the stressful environment for all levels of genetic diversity relative to performance in the permissive environment. Twenty percent of the lowest diversity populations went extinct before the end of the study in permissive conditions, whereas 73% of the low diversity lines went extinct in the stressful environment. All high genetic diversity populations persisted for the duration of the study, although population sizes and reproduction were reduced under stressful environmental conditions. Levels of fitness varied more among replicate low diversity populations than among replicate populations with high genetic diversity. There was a significant correlation between AFLP diversity and population fitness overall; however, AFLP markers performed poorly at detecting modest but consequential losses of genetic diversity. High diversity lines in the stressful environment showed some evidence of relative improvement as the experiment progressed while the low diversity lines did not.</p> <p>Conclusions</p> <p>The combined effects of reduced average fitness and increased variability contributed to increased extinction rates for very low diversity populations. More modest losses of genetic diversity resulted in measurable decreases in population fitness; AFLP markers did not always detect these losses. However when AFLP markers indicated lost genetic diversity, these losses were associated with reduced population fitness.</p

Influence of Corn Stover Harvest on Soil Quality Assessments at Multiple Locations Across the U.S.

Corn (Zea mays L.) stover has been identified as a biofuel feedstock due to its abundance and a perception that the residues are unused trash material. However, corn stover and other plant residues play a role in maintaining soil quality (health) and enhancing productivity, thus use of this abundant material as feedstock must be balanced with the need to protect the vital soil resource. Plant residues provide physical protection against erosion by wind and water, contribute to soil structure, nutrient cycling, and help sustain the soil microbiota. Replicated plots were established on productive soils at several locations (IA, IN, MN, NE, PA, SD, and SC) and a multi-year study was carried out to determine the amount of corn stover that can be removed while maintaining the current level of soil quality for each soil. These sites represented a range of soil types and climatic conditions, and have been ongoing for and least five years with some much longer studies. All sites had at least three levels of stover harvest: grain only (control), maximum removal (90-100%) and a mid-range removal rate (~50%). Data from 4 sites are presented (IA, IN, MN, and NE). The Soil Management Assessment Framework (SMAF) was used to score and assess changes in selected soil quality indicators. Data shows that removal at the highest rates resulted in some loss in soil quality with respect to soil organic carbon and bulk density. These sites were converted to no-till when the experiments were initiated, thus SOC accrual because of the shift in tillage management appeared to balance any losses due to feedstock harvest

Qualitative assessment of Tongue Drive System by people with high-level spinal cord injury

The Tongue Drive System (TDS) is a minimally invasive, wireless, and wearable assistive technology (AT) that enables people with severe disabilities to control their environments using tongue motion. TDS translates specific tongue gestures into commands by sensing the magnetic field created by a small magnetic tracer applied to the user’s tongue. We have previously quantitatively evaluated the TDS for accessing computers and powered wheelchairs, demonstrating its usability. In this study, we focused on its qualitative evaluation by people with high-level spinal cord injury who each received a magnetic tongue piercing and used the TDS for 6 wk. We used two questionnaires, an after-scenario and a poststudy, designed to evaluate the tongue-piercing experience and the TDS usability compared with that of the sip-and-puff and the users’ current ATs. After study completion, 73% of the participants were positive about keeping the magnetic tongue-barbell in order to use the TDS. All were satisfied with the TDS performance and most said that they were able to do more things using TDS than their current ATs (4.22/5)