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Ammonia (NH 3), a significant precursor of particulate matter, affects not only biodiversity, ecosystems, and soil acidification but also climate and human health. In addition, its concentrations are constantly rising due to increasing feeding needs and the large use of fertilization and animal farming. Despite the significance of ammonia, its emissions are associated with large uncertainties, while its atmospheric abundance is difficult to measure. Nowadays, satellite products can effectively measure ammonia with low uncertainty and a global coverage. Here, we use satellite observations of column ammonia in combination with an inversion algorithm to derive ammonia emissions with a high resolution over Europe for the period 2013–2020. Ammonia emissions peak in northern Europe due to agricultural application and livestock management, in western Europe (industrial activity), and over Spain (pig farming). Emissions have decreased by - 26 % since 2013 (from 5431 Gg in 2013 to 3994 Gg in 2020), showing that the abatement strategies adopted by the European Union have been very efficient. The slight increase (+ 4.4 %) in 2015 is also reproduced here and is attributed to some European countries exceeding annual emission targets. Ammonia emissions are low in winter (286 Gg) and peak in summer (563 Gg) and are dominated by the temperature-dependent volatilization of ammonia from the soil. The largest emission decreases were observed in central and eastern Europe (- 38 %) and in western Europe (- 37 %), while smaller decreases were recorded in northern (- 17 %) and southern Europe (- 7.6 %). When complemented with ground observations, modelled concentrations using the posterior emissions showed improved statistics, also following the observed seasonal trends. The posterior emissions presented here also agree well with respective estimates reported in the literature and inferred from bottom-up and top-down methodologies. These results indicate that satellite measurements combined with inverse algorithms constitute a robust tool for emission estimates and can infer the evolution of ammonia emissions over large timescales. [ABSTRACT FROM AUTHOR]

The paper introduces the upcoming outputs of the Lexico-Semantic Database Project (LSD-Czech) supported by The Technology Agency of the Czech Republic (TA CR) [TL02000041]: a Czech online conceptual dictionary and a lexico-semantic database. It reviews the phases of the implementation of the project and its results to be made available by the end of 2022. It describes both the dictionary and the database (from which the dictionary is generated). While the dictionary is intended for the general user, the database will serve linguists and experts in natural language processing and language data application. The paper briefly discusses the field of onomasiological lexicography, focusing on what is an onomasiological dictionary, what dictionaries of this type exist for Czech, and introduces the two dictionaries on which the database is founded, Haller's CČesky slovník věcný a synonymický (1969--86) and Klégr's Tezaurus jazyka českého (2007). The focus of the paper is on the description of the functions and features of the dictionary, the steps whereby to search in it and the envisaged future improvements. Finally, it gives the specifications of the database and the next steps planned in its development. [ABSTRACT FROM AUTHOR]

Copyright of Bohemistyka is the property of Instytut Filologii Slowianskiej Uniwersytetu im. Adama Mickiewicza w Poznaniu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since at least the mid twentieth century, countability has been a lively topic in many fields of linguistics as well as an important subject in the field of teaching English as a second/foreign language. Yet the development of this category in the history of English has been little researched and never comprehensively described. This paper looks at the current state of the descriptions of the emergence of this category in the history of English. It notes a possible connection between its prominent status in the descriptions of Present-Day English and of English as a global language (studied by many non-native learners as well as linguists). It maps the history of the description of the category in grammars and dictionaries from the fourteenth until the early twentieth century, and prepares the ground for a follow-up corpus-based research of the development of countability in English. [ABSTRACT FROM AUTHOR]

Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard 1-week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA (Autonomous Monitor of Atmospheric Radioactive Aerosol) and CEGAM (carousel gamma spectrometry) real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters and allow for determining the moment when 106Ru arrived at the monitoring site within approx. 1 h and detecting activity concentrations as low as several mBq m -3 in 4 h intervals. These high-resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric transport model driven with meteorological data from the Global Forecast System (GFS), and we construct a source–receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. With Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared, and a better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237±107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA-reported (International Atomic Energy Agency) dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. In addition, we validated our findings also using the FLEXPART (FLEXible PARTicle dispersion) model coupled with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). [ABSTRACT FROM AUTHOR]

The paper describes the intermediate stage of a lexicographical project, whose aim is to digitize and align two Czech onomasiological dictionaries (Haller 1969-77; Klégr 2007) in order to create an integrated digital multi-purpose lexico-semantic database of Czech. The two dictionaries are based on different categorization systems (Hallig and von Wartburg; Roget) and use different formats. Their content only partially overlaps, making them largely complementary. Their linkage is planned to be achieved through their structural elements (categories of their hierarchies) rather than by matching individual headwords. The four phases of the project are digitization, encoding, programming and testing. The digitization of both dictionaries and the encoding of one of them have been completed, and the preliminary steps in programming the platform are underway. [ABSTRACT FROM AUTHOR]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [ABSTRACT FROM AUTHOR]

Abstract Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard one week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA and CEGAM real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters. These high resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hysplit atmospheric transport model driven with meteorological data from the global forecast system (GFS) and we construct a source-receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least-squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. On Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared and better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237 ± 107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA reported dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. [ABSTRACT FROM AUTHOR]

Copyright of Bohemistyka is the property of Instytut Filologii Slowianskiej Uniwersytetu im. Adama Mickiewicza w Poznaniu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Determination of a source term of an accidental release of radioactive material into the atmosphere is very important for evaluating emergency situations and their consequences. However, knowledge of the source term and its composition is typically vague and uncertain. One possible way to obtain the source term is inverse modeling in which an atmospheric transport model is combined with field measurements. The most accessible measurements are those from gamma dose rate (GDR) detectors. However, GDR measurements represent a sum of contribution from all nuclides from both plume and deposition which makes the problem particularly difficult. The same difficulty arises when the measurements can not distinguish contribution from another species in the release, such as nuclides attached to different particle sizes. We propose a Bayesian method for recovery of the source term from GDR measurements where a priori knowledge on ratios of different species is given in the form of bounds. This knowledge is incorporated into the model of covariance matrix of the source term. The Bayesian methodology allows to handle uncertain knowledge on the nuclide ratios as well as unknown temporal correlations of the source term. We evaluate and compare the proposed method with other state‐of‐the‐art methods on a twin experiment of a non‐stationary release of 16 nuclides from the Czech nuclear power plant Temelin being registered by the Austrian GDR monitoring network. Real‐world validation of the approach is performed on the latest measurements of concentration and deposition of caesium‐137 from the Chernobyl accident, where we estimate composition of the source term from different particle sizes (species). The estimated source term is in very good agreement with previously reported results and the calculated species ratios are supported by the available observations. We study the problem of multi‐specie source term estimation from gamma dose rate (GDR) measurements which is crucial in emergency situations after atmospheric releases of radioactive material. However, GDR measurements sum contributions of activities from all nuclides, making the source term estimation particularly difficult. We propose a novel hierarchical probabilistic model for multi‐specie source term estimation. Specifically, the model includes prior knowledge on specie ratios in the form of intervals. Validation is given on Chernobyl dataset. [ABSTRACT FROM AUTHOR]

The paper explores trends in spelling variation as reflected in Early English correspondence (15th-17th c.) on the material of the Corpus of Early English Correspondence (CEEC). Overall change in spelling variation has so far been commented on only in relatively general terms and never on quantitative grounds. There is, of course, no doubt about the general direction of the change (towards greater standardization, though not in a straightforward manner) and its basic characteristics, such as its slower pace in private documents compared to the spelling of professional publications, but the data to support the assertions as well as precise definitions of spelling variation or regularisation have not yet been, to our knowledge, provided. This paper introduces a novel methodology for the quantification of spelling variation and regularity, which allows a more objective assessment of its change and which also makes use of the metadata provided by the CEEC: such as gender, letter authenticity or relationship/kinship between the author and the recipient. The paper explores interactions of such variables from the diachronic perspective using quantified levels of spelling regularity. The measure introduced for this purpose is based on weighted information (Shannon) entropy, as a measure of predictability of spellings of individual functionally defined types, and its calculation is partly based on the morphological tagging of the parsed version of the Corpus. [ABSTRACT FROM AUTHOR]

In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model. [ABSTRACT FROM AUTHOR]

In the fall of 2011, iodine-131 (I-131) was detected at several radionuclide monitoring stations in Central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that I-131 was released from the Institute of Isotopes Ltd in Budapest, Hungary. It was reported that a total activity of 342 GBq of I-131 was emitted between September 8 and November 16, 2011. In this study, we use the ambient concentration measurements of I-131 to determine the location of the release as well as its magnitude and temporal variation. Although the location of the release became eventually known, its temporal variation is still uncertain and only partial information is available. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available I-131 measurements. Subsequently, we use the information about the source term for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor-sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, Flexpart and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) weather forecast model. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the I-131 emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform forward calculation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA, while our estimate for the total released activity (490 GBq) is higher than the reported one (342 GBq). Nevertheless, even using our larger source term, dose amounts were very low and never exceeded regulatory limits. [ABSTRACT FROM AUTHOR]

The paper describes the construction and testing of an electronic application for semi-automatic morphological analysis of Old English. It introduces the state of the art in the field of electronic analysis of Old English, provides a brief overview of Old English morphology and discusses the reasoning behind our theoretical framework. An account of the chosen methodology is offered and a specific description of its implementation is provided: from the acquisition and preparation of the lexical input data, through the programming of the forms generator to the testing of the results by analysing Old English text. The resulting recall of 95% is a success; however, the paper also hints at how it may be improved. It also discusses further use and development of the analyser, especially the disambiguation of its results. The paper makes a future semi-automatic morphological tagging of Old English texts a real possibility. [ABSTRACT FROM AUTHOR]

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g. concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation allows to estimate also all tuning parameters from the measurements. The estimation procedure is based on the Variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to estimate also all tuning parameters from the observations. The proposed algorithm is tested and compared with the state-of-the-art method on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download. [ABSTRACT FROM AUTHOR]

The primary research, which is brought by this paper, focuses on medium and large-sized enterprises in sectors of information and communication technologies (ICT). It investigates their attitude to the ICT that they use. Based on these ICT, the article is looking for trends set by the companies. Then, these trends could be considered as an inspiration for companies from other sectors of business. The research results have shown that the specially developed software belongs between the most often implemented ICT tools in the last seven years by the questionnaire companies. The research also showed that this ICT tool of all, which they have implemented, is the most beneficial at a tactical level. Another part of analysis, where the companies should chose benefits that arose from the implementation, pointed out that they are focused on two basic clusters of benefits. The first contains: improved teamwork and increased efficiency and effectiveness. The other cluster contains: reduced costs, improved service quality and improved integration of business functions. The research also showed that the surveyed companies consider the ICT only as a tool, which simplifies work. [ABSTRACT FROM AUTHOR]

The linear inverse problem is often formulated as y = Mx + e (Seibert, 2001), where y is thethe vector of observations, M is the source-receptor-sensitivity (SRS) matrix, x is theunknown source term, and e is the model residue. The key task is to estimate thesource term x. The measurements y are typically in the form of concentrations orgamma dose rates. The SRS matrix M is computed using an atmospheric transportmodel coupled with meteorological data. Since the inverse problem is ill-conditionedand subject to many errors, some form of regularization is required. A commonlyused regularization is the knowledge of the prior source term, also known as a firstguess. The result is thus influenced by this choice, which may be a drawback incases when very uncertain or limited information is available to construct suchprior. In this contribution, we are going to investigate the influence of the prior source term onthe posterior estimate of the source term. We considered the conventional optimizationapproach with loss function (Eckhardt et al., 2008) as well as probabilistic methodLS-APC with automatic selection of the tuning parameters (Tichý et al., 2016).To study the influence, it is necessary to have a dataset with ground truth sourceterm, therefore, we show the results on a twin simulation study as well as on realdata from the controlled tracer experiment ETEX. Specifically, we use the originalrelease as a prior source term, and compare results with those using inaccurate priorobtained by shifting, scaling, and blurring of the ground truth. Using this approach,stability and reliability of the estimated source term based on corrupted prior sourceterms and selected regularization parameters can be quantified and compared fordifferent methods. We show that the LS-APC method is more robust when littleinformation is available, but the conventional method can give good results when it is welltuned. Citations: Eckhardt, S., Prata, A. J., Seibert, P., Stebel, K., and Stohl, A.: Esti- mation of the verticalprofile of sulfur dioxide injection into the atmosphere by a volcanic eruption using satellitecolumn mea- surements and inverse transport modeling, Atmos. Chem. Phys., 8, 3881–3897,https://doi.org/10.5194/acp-8-3881-2008, 2008. Seibert, P.: Iverse modelling with a lagrangian particle disperion model: application topoint releases over limited time intervals, in: Air Pollution Modeling and its Application XIV,Springer, 381–389, 2001. Tichý, O., Šmídl, V., Hofman, R., and Stohl, A.: LS-APC v1.0: a tuning-free methodfor the linear inverse problem and its ap- plication to source-term determination,Geosci. Model Dev., 9, 4297–4311, https://doi.org/10.5194/gmd-9-4297-2016, 2016. [ABSTRACT FROM AUTHOR]