''One Passion, One Vision and One World’’- Reflections on the 9th Council of International Neonatal Nurses Conference (COINN) 2016

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Neonatal Nursing following peer review. Subject to 12 months embargo period, embargo end date: 20 October 2017. The version of record [Journal of Neonatal Nursing (December 2016) Vol 22 (6): 258-262. First published online October 20, 2016] is available online at doi: http://dx.doi.org/10.1016/j.jnn.2016.09.007This report presents our reflections and feedback of the 9th Council of International Neonatal Nurses (COINN) conference 2016 that took place at the Westin hotel, Vancouver, British Columbia, Canada from August 13th to 17th 2016. This sophisticated venue overlooking a beautiful harbour captured the welcoming atmosphere and positivity that was to embrace the conference experience for the 4 day duration of ‘COINN 2016’. As you read this short article which describes the highlights and key messages from the conference, we invite the readers to hear the actual words from some of the first-time participants who stated that they will place the next ‘COINN 2019’ on their calendar with high anticipation.Peer reviewedFinal Accepted Versio

Ultralow noise performance of an 8.4-GHz maser-feedhorn system

A total system noise temperature of 6.6 K was demonstrated with an 8.4-GHz traveling wave maser and feedhorn operating in a cryogenic environment. Both the maser and feedhorn were inserted in the helium cryostat, with the maser operating in the 1.6-K liquid bath and the feedhorn cooled in the helium gas, with a temperature gradient along the horn ranging from the liquid bath temperature at its lower end to room temperature at its top. The ruby maser exhibited 43 dB of gain with a bandwidth of 76 MHz(-3 dB) centered at 8400 MHz. Discussions of the maser, cooled feedhorn, and cryostat designs are presented along with a discussion of the noise temperature measurements

Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate From Microwave Observations

This paper presents a new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI) over the ocean, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes Theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance our understanding of theoretical benefits of the Bayesian approach, we have conducted sensitivity analyses based on two synthetic datasets for which the true conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak, due to saturation effects. It is also suggested that the choice of the estimators and the prior information are both crucial to the retrieval. In addition, the performance of our Bayesian algorithm is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate

Study of perturbed periodic systems of differential equations - The Stroboscopic method

Stroboscopic method for solving perturbed periodic systems of differential equation

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate

Optical Spectroscopic Survey of High-latitude WISE-selected Sources

We report on the results of an optical spectroscopic survey at high Galactic latitude (|b| ≥ 30°) of a sample of WISE-selected targets, grouped by WISE W1 (λ_eff = 3.4 μm) flux, which we use to characterize the sources WISE detected. We observed 762 targets in 10 disjoint fields centered on ultraluminous infrared galaxy candidates using DEIMOS on Keck II. We find 0.30 ± 0.02 galaxies arcmin–2 with a median redshift of z = 0.33 ± 0.01 for the sample with W1 ≥ 120 μJy. The foreground stellar densities in our survey range from 0.23 ± 0.07 arcmin–2 to 1.1 ± 0.1 arcmin–2 for the same sample. We obtained spectra that produced science grade redshifts for ≥90% of our targets for sources with W1 flux ≥120 μJy that also had an i-band flux gsim 18 μJy. We used this for targeting very preliminary data reductions available to the team in 2010 August. Our results therefore present a conservative estimate of what is possible to achieve using WISE's Preliminary Data Release for the study of field galaxies

Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: improved method and uncertainties

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors

A COMPARATIVE RISK ANALYSIS OF FUELWOOD USE IN THE PACIFIC NORTHWEST

Abstract-A risk impact analysis is performed for residential space heating derived from fuelwood in the Pacific Northwest. Direct risks associated with the collection and use of fuelwood in homes are characterized and computed per 1012Btu of fuelwood harvested. The hazards identified are then compared to similar risks associated with the generation of electricity using four different fuels: coal, oil, natural gas, and uranium. The resulting comparison indicates that the acquisition and use of fuelwood in residences is several times more costly in terms of loss of life and injury incurred, than electricity derived from the conventional fuels compared