Shape memory alloy based smart landing gear for an airship

The design and development of a shape memory alloy based smart landing gear for aerospace vehicles is based on a13; novel design approach. The smart landing gear comprises a landing beam, an arch, and a superelastic nickeltitanium shape memory alloy element. This design is of a generic nature and is applicable to a certain class of light13; aerospace vehicles. In this paper a specixFB01;c case of the shape memory alloy based smart landing gear design and13; development applicable to a radio controlled semirigid airship (radio controlled blimp) of 320 m3 volume is13; presented.Ajudicious combination of carbon xFB01;ber reinforced plastic for the landing beam, cane (naturally occurring13; plant product) wrapped with carbon xFB01;ber reinforced plastic for the arch, and superelastic shape memory alloy is13; used in the development. An appropriate sizing of the arch and landing beam is arrived at to meet the dual requirement of low weight and high-energy dissipation while ndergoing x201C;large elasticx201D; (large nonlinear recoverable13; elastic strain) deformations to ensure soft landings when the airship impacts the ground. The soft landing is required13; to ensure that shock and vibration are minimized (to protect the sensitive payload). The inherently large energydissipating character of the superelastic shape memory alloy element in the tensile mode of deformation and the superior elastic bounce back features of the landing gear provide the ideal solution.Anonlinear analysis based on the classical and xFB01;nite element method approach is followed to analyze the structure. Necessary experiments and tests have been conducted to check the veracity of the design. Good correlation has been found between the analyses and testing. This exercise is intended to provide an alternate method of developing an efxFB01;cient landing gear with satisfactory geometry for a x201C;certain class of light aerospace vehiclesx201D; such as airships, rotorcraft, and other light unmanned air vehicles

Effects of Arginine on the Kinetics of Bovine Insulin Aggregation Studied by Dynamic Light Scattering

In the fields of protein science and medicine, understanding the kinetics of protein aggregation are significant in the research and treatment of certain amyloid diseases such as Alzheimer’s. Previous studies have suggested that arginine can increase the solubility of certain proteins, suppress protein aggregation, and assist in the refolding of aggregated proteins; however, the molecular mechanisms by which arginine can influence protein aggregation are still unclear. Bovine insulin was employed as a model system for further understanding the effects of arginine on protein aggregation. Using Dynamic Light Scattering (DLS), we studied the concentration-dependent and temperature-dependent suppression of aggregation in insulin by means of arginine. Arginine concentrations from 10mM to 500mM were shown to have produced a concentration-dependent increase in the lag time of the aggregation, which is the period preceding protein aggregation. DLS measurements of insulin in the presence of arginine from 60°C to 85°C showed a significant increase in the aggregation delay for samples with arginine compared to control samples without arginine. Arginine samples were shown to have delayed aggregation by up to a factor of 7.5. From Arrhenius analysis, we also found that the activation energy of 1mM insulin was 17 ± 5 kcal/mol while the energy of the insulin samples with 500mM arginine was higher (26 ± 3 kcal/mol). These energy values are in accordance with the energy associated with β-sheet formation, which is about 0.5 kcal/mol/residue (or ~25 kcal/mol for monomeric insulin). The 9 kcal/mol difference may quantify the barrier effect of arginine on insulin aggregation

Improving HPV Vaccination Initiation and Completion Rates: A QI Project

Human papillomavirus (HPV) is a common sexually transmitted infection in the United States and is the cause of most cervical cancers in women, as well as anogenital cancers, oropharyngeal cancer, and genital warts in both men and women (Centers for Disease Control [CDC], 2014b). The HPV vaccine is recommended for preteen girls and boys to protect against HPV infections (CDC, 2011). In line with the goal of Healthy People 2020, the purpose of this evidence-based quality improvement (QI) project was to implement a text message reminder system, and calculate the completion rate of the 3-dose HPV vaccination series among adolescents in a pediatric clinic in a small city in Texas. The results were compared to the vaccination rates from a previously used reminder system. The project was of a single-group. pre-test–post-test design. Participants were adolescents aged 9–18 years who had begun, or were set to begin, the 3-dose HPV vaccine series during the project implementation period (October 2015–April 2016). Data from a random review of 100 charts of adolescents reminded using the prior system (prior to implementation) for the same vaccine appointments were used for comparison with the new reminder system data. Enrollees received up to 3 text message reminders for each appointment. They were surveyed to evaluate their preference of a reminder system. Using data from the prior system (n = 100), 11% of adolescents completed the three-dose HPV vaccine series compared to 46.9% from the text messaging system group (n = 49). Both groups showed a better completion rate among females. Based on surveys, 24 (80%) were in favor of text message reminders. This project shows that text message reminders offer an effective method of sending appointment reminders to increase the initiation and completion rates of the HPV vaccine series

Nonparametric Transient Classification using Adaptive Wavelets

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classifier against the well-tested Supernova Photometric Classification Challenge dataset in which the challenge is to correctly classify light curves as Type Ia or non-Ia supernovae. We train our ranked probability classifier on the spectroscopically-confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of 82.4% yielding a highly competitive score of 0.49 whilst implementing a truly "model-blind" approach to supernova classification. Consequently this approach may be particularly suitable for the classification of astronomical transients in the era of large synoptic sky surveys.Comment: 14 pages, 8 figures. Published in MNRA

Parameter estimation with Bayesian estimation applied to multiple species in the presence of biases and correlations

The original formulation of Bayesian estimation applied to multiple species (BEAMS) showed how to use a data set contaminated by points of multiple underlying types to perform unbiased parameter estimation. An example is cosmological parameter estimation from a photometric supernova sample contaminated by unknown Type Ibc and II supernovae. Where other methods require data cuts to increase purity, BEAMS uses all of the data points in conjunction with their probabilities of being each type. Here we extend the BEAMS formalism to allow for correlations between the data and the type probabilities of the objects as can occur in realistic cases. We show with simple simulations that this extension can be crucial, providing a 50 per cent reduction in parameter estimation variance when such correlations do exist. We then go on to perform tests to quantify the importance of the type probabilities, one of which illustrates the effect of biasing the probabilities in various ways. Finally, a general presentation of the selection bias problem is given, and discussed in the context of future photometric supernova surveys and BEAMS, which lead to specific recommendations for future supernova survey

Inhibitory Effects of Arginine on the Aggregation of Bovine Insulin

Static and dynamic light scattering were used to investigate the effects of L-arginine, commonly used to inhibit protein aggregation, on the initial aggregation kinetics of solutions of bovine insulin in 20% acetic acid and 0.1 M NaCl as a model system for amyloidosis. Measurements were made as a function of insulin concentration (0.5-2.0 mM), quench temperature (60-85 • C), and arginine concentration (10-500 mM). Aggregation kinetics under all conditions had a lag phase, whose duration decreased with increasing temperature and with increasing insulin concentration but which increased by up to a factor of 8 with increasing added arginine. Further, the initial growth rate after the lag phase also slowed by up to a factor of about 20 in the presence of increasing concentrations of arginine. From the temperature dependence of the lag phase duration, we find that the nucleation activation energy doubles from 17 ± 5 to 36 ± 3 kcal/mol in the presence of 500 mM arginine

Clinical Guidance for the Management of Patients with Urothelial Cancers During the COVID-19 Pandemic - Rapid Review.

The current COVID-19 pandemic presents a substantial obstacle to cancer patient care. Data from China as well as risk models suppose that cancer patients, particularly those on active, immunosuppressive therapies are at higher risks of severe infection from the illness. In addition, staff illness and restructuring of services to deal with the crisis will inevitably place treatment capacities under significant strain. These guidelines aim to expand on those provided by NHS England regarding cancer care during the coronavirus pandemic by examining the known literature and provide guidance in managing patients with urothelial and rarer urinary tract cancers. In particular, they address the estimated risk and benefits of standard treatments and consider the alternatives in the current situation. As a result, it is recommended that this guidance will help form a framework for shared decision making with patients. Moreover, they do not advise a one-size-fits-all approach but recommend continual assessment of the situation with discussion within and between centres

Statistical classification techniques for photometric supernova typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non-representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of