Unique ghost surface phonon polaritons in biaxially hyperbolic materials.

We predicted peculiar ghost surface phonon polaritons in biaxially hyperbolic materials, where the two hyperbolic principal axes lie in the plane of propagation. We took the biaxially-hyperbolic α-MoO3 as one example of the materials to numerically simulate the ghost surface phonon polaritons. We found three unique ghost surface polaritons to appear in three enclosed wavenumber-frequency regions, respectively. These ghost surface phonon polaritons have different features from the surface phonon polaritons found previously, i.e., they are some hybrid-polarization surface waves composed of two coherent evanescent branch-waves in the α-MoO3 crystal. The interference of branch-waves leads to that their Poynting vector and electromagnetic fields both exhibit the oscillation-attenuation behavior along the surface normal, or a series of rapidly attenuated fringes. We found that the in-plane hyperbolic anisotropy and low-symmetric geometry of surface are the two necessary conditions for the existence of these ghost surface polaritons.

Large spatial shifts of reflective beam at the surface of graphene/hBN metamaterials.

We theoretically studied the Goos-Hänchen (GH) and Imbert-Fedorov (IF) shifts of reflective beam at the surface of graphene/hBN metamaterials. The results show that the GH-shift is significantly enhanced and also possesses the large reflectivity when the light beam is incident at the critical angle near the Brewster angle. We found that the IF-shift is the largest when the reflective beam is a special polarized-beam or the reflective coefficients satisfy the conditions |rs | = |rp | and φs - φp = 2jπ (j is an integer). By changing the chemical potential, filling ratio and tilted angle, the position and width of frequency windows obtaining the maximum values of shifts can be effectively adjusted. The large and tunable GH- and IF-shifts with the higher reflectivity provide an alternative scheme to develop new nano-optical devices.

Polypharmacy, Gait Performance, and Falls in Community-Dwelling Older Adults. Results from the Gait and Brain Study.

BACKGROUND AND OBJECTIVES: Polypharmacy, defined as the use of five or more medications, has been repeatedly linked to fall incidence, and recently it was cross-sectionally associated with gait disturbances. Our objectives were to evaluate cross-sectional and longitudinal associations between polypharmacy and gait performance in a well-established clinic-based cohort study. We also assessed whether gait impairments could mediate associations between number of medications and fall incidence. DESIGN: Prospective cohort of community-dwelling older adults, with 5 years of follow-up. SETTING: Geriatric clinics in an academic hospital in London, ON, Canada. PARTICIPANTS: Community-dwelling older adults aged 65 and older (n = 249; 76.6 ± 8.6 y; 63% women). MEASUREMENTS: Number of medications, quantitative spatiotemporal gait parameters, and fall incidence during follow-up. RESULTS: The number of medications was cross-sectionally associated with poor gait performance (slow gait, speed p < .001; higher variability, p < .001; and higher stride, p < .001; step, p = .013, and double support times, p < .001). Prospectively, the number of medications was associated with overall gait decline (odds ratio = 1.23; 95% confidence interval [CI] = 1.13-1.33; p < .001), faster gait decline (hazard ratio = 4.62; 95%CI = 1.82-11.73; p < .001), and higher falls incidence (p = .006). These associations remained true after adjusting for age, sex, and accounting for "confounding by indication bias" by using a comorbidity propensity score adjustment. Each additional medication taken, significantly increased gait decline risk by 12% to 16% and fall incidence risk by 5% to 7%. Mediation analyses revealed that gait impairments in stride length, step length, and step width mediated the strength of the association between medications and fall incidence. CONCLUSION: Polypharmacy was cross-sectionally associated with poor gait performance and longitudinally associated with gait decline and fall incidence. Despite our use of propensity matching, confounding by indication could have influenced the results. Quantitative spatial gait parameters performance mediated the strength of the association between medications and falls, suggesting a role of gait disturbances in the medication-related falls pathway.

Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network.

For hospitals' admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.