Comparison of airborne measurements of NO, NO₂, HONO, NO_y and CO during FIREX-AQ

We present a comparison of fast-response instruments installed onboard the NASA DC-8 aircraft that measured nitrogen oxides (NO and NO2), nitrous acid (HONO), total reactive odd nitrogen (measured both as the total (NOy) and from the sum of individually measured species (SNOy)) and carbon monoxide (CO) in the troposphere during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign. By targeting smoke from summertime wildfires, prescribed fires and agricultural burns across the continental United States, FIREX-AQ provided a unique opportunity to investigate measurement accuracy in concentrated plumes where hundreds of species coexist. Here, we compare NO measurements by chemiluminescence (CL) and laser induced fluorescence (LIF); NO2 measurements by CL, LIF and cavity enhanced spectroscopy (CES); HONO measurements by CES and iodide-adduct chemical ionization mass spectrometry (CIMS); and CO measurements by tunable diode laser absorption spectrometry (TDLAS) and integrated cavity output spectroscopy (ICOS). Additionally, total NOy measurements using the CL instrument were compared with SNOy (= NO + NO2 + HONO + nitric acid (HNO3) + acyl peroxy nitrates (APNs) + submicron particulate nitrate (pNO3)). The aircraft instrument intercomparisons demonstrate the following: 1) NO measurements by CL and LIF agreed well within instrument uncertainties, but with potentially reduced time response for the CL instrument; 2) NO2 measurements by LIF and CES agreed well within instrument uncertainties, but CL NO2 was on average 10 % higher; 3) CES and CIMS HONO measurements were highly correlated in each fire plume transect, but the correlation slope of CES vs. CIMS for all 1 Hz data during FIREX-AQ was 1.8, which we attribute to a reduction in the CIMS sensitivity to HONO in high temperature environments; 4) NOy budget closure was demonstrated for all flights within the combined instrument uncertainties of 25 %. However, we used a fluid dynamic flow model to estimate that average pNO3 sampling fraction through the NOy inlet in smoke was variable from one flight to another and ranged between 0.36 and 0.99, meaning that approximately 0–24 % on average of the total measured NOy in smoke may have been unaccounted for and may be due to unmeasured species such as organic nitrates; 5) CO measurements by ICOS and TDLAS agreed well within combined instrument uncertainties, but with a systematic offset that averaged 2.87 ppbv; and 6) integrating smoke plumes followed by fitting the integrated values of each plume improved the correlation between independent measurements.

Pelaksanaan Discharge Planning pada Pasien Hipertensi di RSUD Panembahan Senopati Bantul

<p>Hipertensi merupakan penyakit tidak menular yang masih menjadi masalah dibidang kesehatan. Upaya penanganan hipertensi pada dasarnya sudah dijalankan dengan berbagai cara termasuk kegiatan untuk meningkatkan pengetahuan penderita hipertensi. Pengetahuan dapat ditingkatkan melalui komunikasi informasi edukasi. Salah satu yang biasanya dilakukan petugas kesehatan ditatanan pelayanan kesehatan adalah pelaksanaan discharge planing. discharge planning adalah perencanaan pulang, sejak pasien masuk rumah sakit sampai pasien pulang yang dilakukan oleh petugas Kesehatan. Penelitian ini merupakan penelitian deskriptif dengan pendekatan retrospektif. Populasi penelitian ini adalah rekam medis pasien hipertensi yang mendapatkan rawat inap dan dinyatakan pulang dari bulan Agustus sampai dengan bulan Oktober 2017. Besarnya sampel penelitian sebanyak 145 rekam medis. Besarnya sampel tiap bangsal menggunakan rumus sampling fraction cluster. Instrumen penelitian menggunakan lembar checklist pemberian Pendidikan kesehatan. Analisa data menggunakan deskriptif statistik. Hasil dan Kesimpulan: Hasil penelitian menunjukan program pendidikan kesehatan secara keseluruhan 145 (100%) dilaksanakan, sebagian besar menggunakan metode diskusi 145 (100%), respon pasien atau keluarga pasien hipertensi setelah diberi pendidikan kesehatan sebagian besar paham tapi tidak bisa menjelaskan sendiri 113 (77,93%), pemberi pendidikan kesehatan sebagian besar dilaksanakan oleh dokter 135 (93,10%), dan penerima pendidikan kesehatan terbanyak diterima oleh keluarga pasien hipertensi 122 (84,14%).</p><p>Kata Kunci: Discharge Planning, Hipertensi</p>

The Continuous Sampling Plan for Two Production Lines: The CSP-2L

This paper presents the CSP-2L continuous sampling plan, which is designed for product inspectionon two independent production lines at the same time. The purpose of the CSP-2L is to improve the CSP-1-2Lin order to reduce the number of defective products that have passed without being inspected during thetemporary inspection stop, so the quality of the products is better. Therefore, if the manufacturer who uses theCSP-2L plan to inspect the production lines then resulting in higher quality of the products than the CSP-1-2L.This presentation includes the product inspection procedures and the formulas for performance measures suchas average total fraction inspected (ATFI), average total outgoing quality (ATOQ), and average total outgoingquality limit (ATOQL) which are carried out using a Markov chain. The formulas for performance measures ofthe CSP-2L have been tested to be accurate. When defined, the probability of a unit produced by the processbeing nonconforming of line 1 and line 2 are equal (p1 = p2 = p = 0.005, 0.015 and 0.035), the clearance numberof line 1 and line 2 are equal (i1 = i2 = 10, 20, 30, 40 and 50), the sampling fraction of line 1 and line 2 are equal(f1 = f2 =12and13) and the number of units to be found when inspection of line 1 and line 2 are in the phaseof sampling inspection at the same time (m = i1 and 2i1). Moreover, the ATOQ values from the CSP-2L and theCSP-1-2L plans were compared. The results showed that, the formulas for performance measures are accurateand in the case of p, the levels are low and moderate, the ATOQ of the CSP-2L are less than those of theCSP-1-2L in all cases. But in the case of p is at a high level, the ATOQ of the CSP-2L is less than those of theCSP-1-2L for some cases of i.

The Effects of Protective Factors on Community Resilience in Dealing Flood

Gempolsari Village, Sidoarjo (East Java, Indonesia) is one area that is always flooded annually; thus, people in the region have a high level of resilience. The level of resilience is fundamental for victims to be able to live normally as before the disaster came, which is influenced by several factors, one of which is a protective factor. This study aims to determine the relationship between protective factors and community resilience in dealing the flood disaster. This research was conducted in Gempolsari Village, Sidoarjo (East Java, Indonesia). This study used quantitative descriptive as the research design with cross sectional approach. This study used Probability Sampling as sampling technique in the form of Sampling Fraction Per Cluster. The sample used was 91 respondents. This study used two variables: protective factors as independent variables and community resilience to face flood as the dependent variable. The data collection process was carried out using a questionnaire, then data was analyzed using the Spearman Rho test (α ≤ 0,05). The results showed that a significant relationship (p = 0.05) between protective factors and the level of community resilience in dealing with flood was found, where the protective factor that has the strongest relationship was impulse control (r = 0.527). A high protective factor could increase the level of community resilience to face flood disasters, because the level of resilience was influenced by the level of protective factors owned by the community.

Determination of Optimum Sampling Fraction for Bootstrap Resampling

Lately, there has been much discussion on the bootstrap resampling method, both as a way of estimating standard error and as a way of improving estimations with access to only one sample. However, little is found in literature discussing the size the bootstrap sample should take. This study aims to determine the existence of an optimum sampling fraction for resampling, analysing different estimators and number of resamples. An optimum fraction exists if, and only if, for every estimator and every amount of resamples, a fraction (or region) performs better in every population. Ten random populations were created by adding together different normal, Poisson and exponential distributions such that their means and variances are diverse. A Monte Carlo simulation with ten thousand iterations was done, taking random systematic samples from the populations and from these, bootstrap samples to estimate the mean, variance and respective standard errors. Results show the inexistence of a single optimum fraction. However, it does point to an optimum region for standard error estimation above 37.5%.

A Metric Fractionator for Estimation of Total Cell Number in Paraffin-Embedded Flat or Hollow Organs

The physical fractionator is a convenient and practical solution for estimation of total cell number in a regulatory toxicology setting because it is insensitive to shrinkage allowing for paraffin processing/embedding and does not require measurement of the reference or organ volume. The principle involves sampling a known fraction of an organ in one or more steps and counting the total number of cells present in the final sample, physical disector section pairs. The total cell number in the organ is estimated by multiplying the cell count in the final fraction by the inverse of the sampling fraction(s). The key feature of the design is that tissue shrinkage due to paraffin processing occurs before the organ is uniformly sampled. Another requirement is that thermal expansion or contraction is avoided during the preparation of disector sections from the individual embedded subsamples, which ensures that the disector sections represent a known constant fraction. This vertical physical fractionator with subsampling is a simple and fast estimator to obtain precise and robust estimates of total cell number in large flat or hollow organs that do not prolong routine necropsy procedures. It is compatible with paraffin processing, avoids exhaustive sectioning, and allows for the collection of routine histopathology sections.

Estimating Diversification Rates on Incompletely Sampled Phylogenies: Theoretical Concerns and Practical Solutions

Molecular phylogenies are a key source of information about the tempo and mode of species diversification. However, most empirical phylogenies do not contain representatives of all species, such that diversification rates are typically estimated from incompletely sampled data. Most researchers recognize that incomplete sampling can lead to biased rate estimates, but the statistical properties of methods for accommodating incomplete sampling remain poorly known. In this point of view, we demonstrate theoretical concerns with the widespread use of analytical sampling corrections for sparsely sampled phylogenies of higher taxonomic groups. In particular, corrections based on “sampling fractions” can lead to low statistical power to infer rate variation when it is present, depending on the likelihood function used for inference. In the extreme, the sampling fraction correction can lead to spurious patterns of diversification that are driven solely by unbalanced sampling across the tree in concert with low overall power to infer shifts. Stochastic polytomy resolution provides an alternative to sampling fraction approaches that avoids some of these biases. We show that stochastic polytomy resolvers can greatly improve the power of common analyses to estimate shifts in diversification rates. We introduce a new stochastic polytomy resolution method (Taxonomic Addition for Complete Trees [TACT]) that uses birth–death-sampling estimators across an ultrametric phylogeny to estimate branching times for unsampled taxa, with taxonomic information to compatibly place new taxa onto a backbone phylogeny. We close with practical recommendations for diversification inference under several common scenarios of incomplete sampling. [Birth–death process; diversification; incomplete sampling; phylogenetic uncertainty; rate heterogeneity; rate shifts; stochastic polytomy resolution.]

Sampling-based Markov regression model for multistate disease progression: Applications to population-based cancer screening program

To develop personalized screening and surveillance strategies, the information required to superimpose state-specific covariates into the multi-step progression of disease natural history often relies on the entire population-based screening data, which are costly and infeasible particularly when a new biomarker is proposed. Following Prentice’s case-cohort concept, a non-standard case-cohort design from a previous study has been adapted for constructing multistate disease natural history with two-stage sampling. Nonetheless, the use of data only from first screens may invoke length-bias and fail to consider the test sensitivity. Therefore, a new sampling-based Markov regression model and its variants are proposed to accommodate additional subsequent follow-up data on various detection modes to construct state-specific covariate-based multistate disease natural history with accuracy and efficiency. Computer simulation algorithms for determining the required sample size and the sampling fraction of each detection mode were developed either through power function or the capacity of screening program. The former is illustrated with breast cancer screening data from which the effect size and the required sample size regarding the effect of BRCA on multistate outcome of breast cancer were estimated. The latter is applied to population-based colorectal cancer screening data to identify the optimal sampling fraction of each detection mode.

Equal probability sampling

This chapter presents a formal quantitative treatment of material covered conceptually in Chapter 2, all with respect to equal probability with replacement (SWR) and without replacement selection simple random sampling, (SRS) of samples of size n from a finite population of size N. Small sample space examples are used to illustrate unbiasedness of mean-per-unit estimators of the mean, total and proportion of the target variable, y, for SWR and SRS. Explicit formulas for sampling variance indicate how estimator uncertainty depends on finite population variance, sample size and sampling fraction. Measures of the relative performance of alternative sampling strategies (relative precision, relative efficiency, net relative efficiency) are introduced and applied to mean-per-unit estimators used for the SWR and SRS selection methods. Normality of the sampling distribution of the SRS mean-per-unit estimator depends on sample size but also on the shape of the distribution of the target variable, y, values over the finite population units. Normality of the sampling distribution is required to justify construction of valid 95% confidence intervals that may be constructed around sample estimates based on unbiased estimates of sampling variance. Methods to calculate sample size to achieve accuracy objectives are presented. Additional topics include Bernoulli sampling (a without replacement selection scheme for which sample size is a random variable), the Rao–Blackwell theorem (which allows improvement of estimators that are based on selection methods which may result in repeated selection of the same units), oversampling and nonresponse.