Development and Evaluation of an In Silico Dermal Absorption Model Relevant for Children

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment.

Infrared Absorption and Its Sources of CdZnTe at Cryogenic Temperature

To reveal the causes of infrared absorption in the wavelength region between electronic and lattice absorptions, we measured the temperature dependence of the absorption coefficient of p-type low-resistivity ($$\sim 10^2~ \Omega \mathrm{cm}$$ ∼ 10 2 Ω cm ) CdZnTe crystals. We measured the absorption coefficients of CdZnTe crystals in four wavelength bands ($$\lambda =6.45$$ λ = 6.45 , 10.6, 11.6, 15.1$$~\mu $$ μ m) over the temperature range of $$T=8.6$$ T = 8.6 -300 K with an originally developed system. The CdZnTe absorption coefficient was measured to be $$\alpha =0.3$$ α = 0.3 -0.5 $$\mathrm{cm}^{-1}$$ cm - 1 at $$T=300$$ T = 300 K and $$\alpha =0.4$$ α = 0.4 -0.9 $$\mathrm{cm}^{-1}$$ cm - 1 at $$T=8.6$$ T = 8.6 K in the investigated wavelength range. With an absorption model based on transitions of free holes and holes trapped at an acceptor level, we conclude that the absorption due to free holes at $$T=150$$ T = 150 -300 K and that due to trapped-holes at $$T<50$$ T < 50 K are dominant absorption causes in CdZnTe. We also discuss a method to predict the CdZnTe absorption coefficient at cryogenic temperature based on the room-temperature resistivity.

Semi-Reliability Probability Damage Assessment of GFRP Bars Embedded in Steam-Curing Concrete Beams Based on the Multiple Factors Related Moisture Absorption Model

GFRP bars will be damaged due to a series of irreversible hygroscopic chemical reactions under humid and hot curing environmental conditions. The multiple factors related to the moisture absorption model were established through the moisture absorption test of GFRP bars embedded in steam-curing concrete, which considered different curing temperatures, different thicknesses of the protective layer, and different diameters of GFRP bars. Semi-reliability probability damage assessment of GFRP bars embedded in steam-curing concrete was described by introducing the reliability and stochastic theory. Subsequently, the tensile test of GFRP bars was carried out to verify the feasibility of the damage assessment. The results showed that the moisture absorption curves of GFRP bars were basically in line with Fick’s law. In addition, the influences of the curing temperature, the thickness of the protective layer, and the diameter on moisture absorption performance were presented. The semi-reliability probability damage assessment model of GFRP bars embedded in steam-curing concrete beams adequately considered the multiple factors related to moisture absorption and the uncertainty and randomness of the influencing factors during the process of moisture absorption.

Low-frequency Flux Density Measurements and Pulsars with GHz-peaked Spectra

We have estimated flux densities of several pulsars from radio interferometric observations mainly at 325 MHz using the Giant Metrewave Radio Telescope. The new observations allowed us to update the spectral nature of the observed pulsars, and in six sources we identified relatively high frequency turnovers, which can be classified as new GHz-peaked spectrum (GPS) pulsars. For such objects the turnover in the spectrum is most likely caused by absorption in the immediate vicinity of the pulsar (or in the interstellar medium). We modeled the turnover spectra using the thermal free–free absorption model and the physical parameters obtained from the fits enabled us to identify the environments that could potentially be responsible for the observed absorption, such as pulsar wind nebulae, supernova remnant nebulae or H ii regions. The discovery of 6 new GPS pulsars brings the total number of such objects to 33 and we discuss the properties of the entire sample.

The uGMRT Observations of Three New Gigahertz-peaked Spectra Pulsars

Using the Giant Metrewave Radio Telescope, we report the detailed spectral measurements over a wide frequency range of three pulsars (J1741−3016, J1757−2223, and J1845−0743), which allow us to identify them as new gigahertz-peaked spectra pulsars. Our results indicate that their spectra show turnovers at the frequencies of 620 MHz, 640 MHz, and 650 MHz, respectively. Our analysis proves that wideband observations improve estimations of spectral nature using a free–free thermal absorption model, and thus allow for a more accurate approximation of the maximum energy in the spectrum. While there is no evidence as yet that these objects are associated with a supernova remnant or pulsar wind nebula, they will make good targets when looking for interesting environments in the future, or when conducting more sensitive sky surveys.

Modeling CO2, H2S, COS, and CH3SH Simultaneous Removal Using Aqueous Sulfolane–MDEA Solution

In this study, a rate-based absorption model coupled with an improved thermodynamic model was developed to characterize the removal of acid components (CO2 and H2S) and organic sulfur (COS and CH3SH) from natural gas with an aqueous sulfolane–MDEA solution. First, the accuracy of the thermodynamic model was validated by comparing the calculated partial pressure of CO2, H2S, and CH3SH with those of the experimental data reported in the literature. Then, the industrial test data were employed to validate the absorption model and the simulation results agreed well with the experimental data. The average relative errors of the removal rates of CO2, COS, and CH3SH are 3.3%, 3.0%, 4.1%, respectively. Based on the validated coupled model, the total mass transfer coefficient and mass transfer resistance of each solute component at different column positions were analyzed. The effects of the gas–liquid ratio, overflow weir height, and absorption pressure on the absorption performance of each component were studied, and the influence of the acid component concentration in the feed gas on the removal efficiency of methyl mercaptan (CH3SH) was also discussed. It is found that the improved absorption model can better characterize the absorption performance and be conducive to the optimal design of the absorber column.

Interference Phenomena in the Donetsk Regiolect

The article deals with the description of language interference as the main process in creating the regional identity of the Russian language in Donbass. The relevance of the work is determined by the fact that it is the first attempt to identify the role and forms of language interference in the formation of a regiolect, moreover, the object of research of the processes of language systems interaction is the Donetsk diffuse regiolect, which arose on the South Russian language base as a result of interference of closely related languages and regiolects. The stated subject involves the identification of types, zones, models and participants of interference interaction. The purpose of the article is to determine the sources and results of language interference that occurred during the formation of the Donetsk diffuse regiolect. The author uses general scientific and linguistic methods and techniques for analyzing regionalisms, in particular, comparative, descriptive methods, methods of collecting and processing sociolinguistic data, specialized methods for determining regionalisms. The use of basic methods of analysis and synthesis, as well as the observation method when considering Donetsk language phenomena, allowed the author to determine the Russian basis of the Donetsk regiolect, its diffuse nature, to justify the implementation in regional modifying the “interference absorption” model of external (different language and different regiolect) and internal (idiom) language phenomena, to establish the language zones of interference interaction, the main language systems that participated in the formation of the Donetsk regiolect, and the degree of their participation in this process. The obtained theoretical conclusions can be used in the study of the regiolects of the territories of late settlement, formed on the basis of closely related languages and regiolects, for example, Russian and Ukrainian, Russian and Belorussian, etc.

Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis

A specific model for drug absorption is necessarily assumed in pharmacokinetic (PK) analyses following extravascular dosing. Unfortunately, an inappropriate absorption model may force other model parameters to be poorly estimated. An added complexity arises in population PK analyses when different individuals appear to have different absorption patterns. The aim of this study is to demonstrate that a deep neural network (DNN) can be used to prescreen data and assign an individualized absorption model consistent with either a first-order, Erlang, or split-peak process. Ten thousand profiles were simulated for each of the three aforementioned shapes and used for training the DNN algorithm with a 30% hold-out validation set. During the training phase, a 99.7% accuracy was attained, with 99.4% accuracy during in the validation process. In testing the algorithm classification performance with external patient data, a 93.7% accuracy was reached. This algorithm was developed to prescreen individual data and assign a particular absorption model prior to a population PK analysis. We envision it being used as an efficient prescreening tool in other situations that involve a model component that appears to be variable across subjects. It has the potential to reduce the time needed to perform a manual visual assignment and eliminate inter-assessor variability and bias in assigning a sub-model.