Cydalima perspectalis in Poland—8 Years of Invasion against the Background of Three Other Invasive Species

The box tree moth (Cydalima perspectalis) origins from East Asia. In Europe, it was recorded for the first time in 2007, and in Poland in 2012. By the end of 2020, it was found all over Poland. There are no published data on the range of C. perspectalis occurrence in Poland because it is not a quarantine pest in the European Union and is not subject to official monitoring. Data collected in 2018–2020 via a website dedicated to monitoring, for the first time, illustrate the current range and its largest concentrations in southern and central Poland. The monitoring confirmed that the main directions of the invasion are related to the main communication routes of Poland (south-north) and are of a long-distance character. The dispersal pattern corresponds to the model developed for Cameraria ohridella: a stratified dispersal model that considers long-distance road/rail transport. The second important factor contributing to the invasion of C. perspectalis are large human communities enabling rapid local dispersion (a diffusion model). Comparing its invasion with the monitoring data from 2007–2013 of two other invasive pests of Poland: Ostrinia nubilalis and Diabrotica virgifera, shows that a diffusion model best describes the spatial spread of these pests only to uninhabited neighboring areas.

NEURAL NETWORK CALCULATION OF CARBON DIOXIDE CONCENTRATIONS

Снижение углеродного следа в настоящее время является одной из приоритетных задач мировой экономики. Для достижения этой цели необходимо с одной стороны снижать выбросы парниковых газов, с другой стороны развивать методы мониторинга парниковых газов в атмосферном воздухе для обеспечения контроля эффективности принимаемых решений.Учитывая сложность процессов рассеивания газов в атмосферном воздухе, значительными преимуществами в вопросах определения концентраций атмосферных примесей обладают нейросетевые методы моделирования. В данной статье представлен метод расчета концентраций углекислого газа в атмосферном воздухе с помощью спроектированной и обученной каскадной нейросетевой модели, позволяющей при расчете концентраций учитывать сложное влияние метеорологических факторов и локальных условий рассеивания. Первым уровнем модели является расчет концентрации оксида углерода по известным параметрам источников выбросов этого вещества с использованием регламентированной методики расчета рассеивания примесей в атмосфере в Унифицированной программе расчета рассеивания «Эколог-Город». Вторым уровнем является нейронная сеть, которая корректирует рассчитанную на первом шаге концентрацию по заданным метеорологическим параметрам для увеличения точности моделирования. Третьим уровнем является нейронная сеть, позволяющая по полученной на предыдущем шаге концентрации оксида углерода, а также измеренным значениям коэффициента химической трансформации и концентрации атмосферного озона производить расчет концентрации углекислого газа.Полученная каскадная модель апробирована на территории г. Нижнекамск. Достигнутая точность расчета концентрации углекислого составила более 95%. Таким образом, представленная технология позволяет расширить возможности локальной системы мониторинга в условиях недостаточного количества измерений диоксида углерода. Reducing the carbon footprint is currently one of the priorities for the world economy. To do this, it is necessary to reduce greenhouse gas emissions, as well as to develop methods for monitoring greenhouse gases in the atmospheric air to ensure control over the effectiveness of decisions taken.Considering the complexity of the processes of dispersion of gases in the atmospheric air, neural network modeling methods have significant advantages in determining the concentrations of atmospheric impurities. This article presents a method for calculating the concentration of carbon dioxide in the atmospheric air using a designed and trained cascade neural network model, which makes it possible to take into account the complex influence of meteorological factors and local dispersion conditions when calculating concentrations. The first level of the model is the calculation of the concentration of carbon monoxide according to the known parameters of the emission sources of this substance using the regulated method for calculating the dispersion of impurities in the atmosphere in the Unified program for calculating dispersion "Ecolog-City". The second level is a neural network, which corrects the concentration calculated at the first step according to the specified meteorological parameters to increase the modeling accuracy. The third level is a neural network that allows calculating the concentration of carbon dioxide based on the concentration of carbon monoxide obtained at the previous step, as well as the measured values of the coefficient of chemical transformation and concentration of atmospheric ozone.The resulting cascade model was tested on the territory of Nizhnekamsk. The achieved accuracy of calculating the concentration of carbon dioxide was more than 95%. Thus, the presented technology makes it possible to expand the capabilities of the local monitoring system in conditions of an insufficient number of measurements of carbon dioxide.

Calculation of crosswind integrated concentration by using different dispersion schemes

The Gaussian model is the most extensively used model for local dispersion. The Gaussian formula for a continuous release from a point source (GPM) is integrated to get crosswind integrated concentration. Different schemes such as Irwin, power law, Briggs, Standard method, and split sigma theta method can be used to obtain integrated concentration. Also downwind speed in power law, plume rise and Statistical measures are used in the model to know which is the best scheme agrees with the observed concentration data obtained from Copenhagen, Denmark.

Relative dispersion with finite inertial ranges

Relative dispersion experiments are often analysed using theoretical predictions from two- and three-dimensional turbulence. These apply to infinite inertial ranges, assuming the same dispersive behaviour over all scales. With finite inertial ranges, the metrics are less conclusive. We examine this using pair separation probability density functions (PDFs), obtained by integrating a Fokker–Planck equation with different diffusivity profiles. We consider time-based metrics, such as the relative dispersion, and separation-based metrics, such as the finite scale Lyapunov exponent (FSLE). As the latter cannot be calculated from a PDF, we introduce a new measure, the cumulative inverse separation time (CIST), which can. This behaves like the FSLE, but advantageously has analytical solutions in the inertial ranges. This allows the establishment of consistency between the time- and space-based metrics, something which has been lacking previously. We focus on three dispersion regimes: non-local spreading (as in a two-dimensional enstrophy inertial range), Richardson dispersion (as in an energy inertial range) and diffusion (for uncorrelated pair motion). The time-based metrics are more successful with non-local dispersion, as the corresponding PDF applies from the initial time. Richardson dispersion is barely observed, because the self-similar PDF applies only asymptotically in time. In contrast, the separation-based CIST correctly captures the dependencies, even with a short (one decade) inertial range, and is superior to the traditional FSLE at large scales. Nevertheless, it is advantageous to use all measures together, to seek consistent indications of the dispersion.

Theoretical study of the adsorption of BMSF-BENZ drug for osteoporosis disease treatment on Al-doped carbon nanotubes (Al-CNT) as a drug delivery vehicle

The adsorption energy of the BMSF-BENZ adsorbed complexes was investigated to understand the non-local dispersion interactions, with many other chemical parameters related to this subject like HOMO and LUMO, energy gap, and the time needed for the BMSF-BENZ to be desorbed from the nanotube (recovery time). Our study reveals that Al-CNT is a promising adsorbent for this drug as Eads of BMSF-BENZ/Al-CNT complexes are -22.09, -38.68, -12.89, -31.01, -27.31, -21.90, and -21.42 kcal/mol in the gas phase on the active atoms of the BMSF BENZ (Br, N8, N9, N58, O35, O41, and S), respectively. In addition, the spontaneous and favorable interaction between the BMSF BENZ and all nanoparticles was confirmed by investigating Gibbs free energy and quantum theory of atoms in molecule analysis (QTAIM) so that it can be used as an electrochemical sensor or biosensor. Furthermore, to more visualize the nature of intermolecular bonding and the strength of interaction between the BMSF-BENZ drug molecule and the nanotube, QTAIM has been widely studied in the case of drug delivery purposes. Al-CNT (4,0) can be extended as a drug delivery system and the work function type sensor.

Linear dispersion relation of geodesic acoustic modes driven by trapped and circulating energetic particles

We derive the local dispersion relation of energetic-particle-induced geodesic acoustic modes (EGAMs) for both trapped and circulating ion beams with single pitch angle slowing-down and Maxwellian distributions, as well as a bump-on-tail distribution in tokamak plasmas. For slowing-down and Maxwellian particles, the solutions of the local dispersion relation give the spectrum, growth rate and thresholds of excitation as functions of the pitch angle, beam density and frequency of the energetic particles bounce motion. For circulating ions there is only one unstable branch with frequency below the GAM continuum and a threshold of excitation in the pitch angle, for both the slowing-down and single pitch Maxwellian distributions. Trapped particles cause no excitation of a mode for neither slowing-down nor Maxwellian ion beams, but they can excite a mode with a bump-on-tail distribution when the mean velocity of the beam is larger than the threshold and the energetic particle bounce frequency is high enough.

Dispersive effects and focused biodistribution of recombinant human hyaluronidase PH20: A locally acting and transiently active permeation enhancer

Background Recombinant human hyaluronidase PH20 (rHuPH20) facilitates the dispersion and absorption of subcutaneously administered therapeutic agents. This study aimed to characterize the transient, local action of rHuPH20 in the subcutaneous (SC) space using focused biodistribution and dye dispersion studies conducted in mice. Materials and methods To evaluate the biodistribution of rHuPH20, mice were intradermally administered rHuPH20 (80 U). The enzymatic activity of rHuPH20 was analyzed in the skin, lymph nodes, and plasma. Animal model sensitivity was determined by intravenous administration of rHuPH20 (80 U) to the tail vein. To evaluate local dispersion, mice received an intradermal injection of rHuPH20 followed by an intradermal injection of Trypan Blue dye at a contralateral site 45 minutes later. Dye dispersion was measured using a digital caliper. Results After intradermal rHuPH20 injection, enzymatic activity was detected within the skin near the injection site with levels decreasing rapidly after 15 minutes. There was no clear evidence of systemic exposure after administration of rHuPH20, and no discernible rHuPH20 activity was observed in lymph or plasma as a function of time after dosing. In the dye dispersion study, delivery of rHuPH20 at one site did not impact dye dispersion at a distal skin site. Conclusion These observations support the classification of rHuPH20 as a transiently active and locally acting permeation enhancer.