Application of a continuous distribution model for proton binding by humic acids extracted from acidic lake sediments

1987 ◽  
Vol 10 (3) ◽  
pp. 169-173 ◽  
Author(s):  
James R. Rhea ◽  
Thomas C. Young
2011 ◽  
Vol 110 (10) ◽  
pp. 102216 ◽  
Author(s):  
Prabhanshu Shekhar ◽  
Hirsh Nanda ◽  
Mathias Lösche ◽  
Frank Heinrich

2019 ◽  
Vol 116 (10) ◽  
pp. 4058-4063 ◽  
Author(s):  
Johannes Niskanen ◽  
Mattis Fondell ◽  
Christoph J. Sahle ◽  
Sebastian Eckert ◽  
Raphael M. Jay ◽  
...  

The phase diagram of water harbors controversial views on underlying structural properties of its constituting molecular moieties, its fluctuating hydrogen-bonding network, as well as pair-correlation functions. In this work, long energy-range detection of the X-ray absorption allows us to unambiguously calibrate the spectra for water gas, liquid, and ice by the experimental atomic ionization cross-section. In liquid water, we extract the mean value of 1.74 ± 2.1% donated and accepted hydrogen bonds per molecule, pointing to a continuous-distribution model. In addition, resonant inelastic X-ray scattering with unprecedented energy resolution also supports continuous distribution of molecular neighborhoods within liquid water, as do X-ray emission spectra once the femtosecond scattering duration and proton dynamics in resonant X-ray–matter interaction are taken into account. Thus, X-ray spectra of liquid water in ambient conditions can be understood without a two-structure model, whereas the occurrence of nanoscale-length correlations within the continuous distribution remains open.


1992 ◽  
Vol 43 (1) ◽  
pp. 127-131 ◽  
Author(s):  
B. Manunza ◽  
C. Gessa ◽  
S. Deiana ◽  
R. Rausa

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 555
Author(s):  
Chénangnon Frédéric Tovissodé ◽  
Sèwanou Hermann Honfo ◽  
Jonas Têlé Doumatè ◽  
Romain Glèlè Kakaï

Most existing flexible count distributions allow only approximate inference when used in a regression context. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under-, and over-dispersion). The new method, referred to as “balanced discretization”, consists of discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation (probabilistic rounding) in terms of the continuous distribution. For illustrative purposes, we develop the family of balanced discrete gamma distributions that can model equi-, under-, and over-dispersed count data. This family of count distributions is appropriate for building flexible count regression models because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we show that under the equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersions, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data are Poisson distributed.


2021 ◽  
Vol 42 (03) ◽  
pp. 346-356
Author(s):  
Dennis M. Lyu ◽  
Rebecca R. Goff ◽  
Kevin M. Chan

AbstractLung transplantation in the United States, under oversight by the Organ Procurement Transplantation Network (OPTN) in the 1990s, operated under a system of allocation based on location within geographic donor service areas, wait time of potential recipients, and ABO compatibility. On May 4, 2005, the lung allocation score (LAS) was implemented by the OPTN Thoracic Organ Transplantation Committee to prioritize patients on the wait list based on a balance of wait list mortality and posttransplant survival, thus eliminating time on the wait list as a factor of prioritization. Patients were categorized into four main disease categories labeled group A (obstructive lung disease), B (pulmonary hypertension), C (cystic fibrosis), and D (restrictive lung disease/interstitial lung disease) with variables within each group impacting the calculation of the LAS. Implementation of the LAS led to a decrease in the number of wait list deaths without an increase in 1-year posttransplant survival. LAS adjustments through the addition, modification or elimination of covariates to improve the estimates of patient severity of illness, have since been made in addition to establishing criteria for LAS value exceptions for pulmonary hypertension patients. Despite the success of the LAS, concerns about the prioritization, and transplantation of older, sicker individuals have made some aspects of the LAS controversial. Future changes in US lung allocation are anticipated with the current development of a continuous distribution model that incorporates the LAS, geographic distribution, and unaccounted aspects of organ allocation into an integrated score.


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