Chemical composition determination of impurities and effect on the toxicity degree of solar panel components

By 2050, according to the conclusion of the European Commission, the amount of solar panels waste will reach 78 million tons. 85% of all solar panels produced today belong to polycrystalline solar panels. The subject of this paper is the polymer components of polycrystalline solar panels EVA (ethyl vinyl acetate) and Tedlar® (polyvinyl fluoride). The paper reflects studies to determine the chemical composition of impurities of the solar panel components, and the degree of impurities influence on the toxicity of polymer components

Relationship between the criminal proceduer setting and the objectives of public prosecution

Objective on the basis of the doctrine legislation and practice to make conclusions about the degree of efficiency of such participants in the criminal proceedings as the detective investigator Prosecutor judge and to analyze the observance and implementation of such important principles as adversary equality of the parties and presumption of innocence from the point of view of the thorough study of their practical application. Methods dialectical method analysis synthesis deduction and induction and specific scientific methods of scientific cognition. Results the actual position was determined of the subjects of criminal proceedings from the point of view of feasibility of basic principles of criminal proceedings not in legislative but in practical aspect. Scientific novelty Often the position of criminal proceeding subjects their authority role and status are analyzed from the point of view of legislative norms. It also true for criminal proceedings. In the article an attempt is made to analyze with the new position the relationship of the criminal process setting and the objectives of public prosecution in the modern period of development of criminal procedural science. Practical value the criminal proceedings is an essential element in the aspect of the citizensrsquo rights protection thus it is obvious that the position of the criminal proceedings participants should objective and transparent and the criminal proceedings principles should be implemented. The article shows the problems and proposes was of their solution which are of objective interest. The research results can be applied in practice and taken into account when making changes in the legislation

X-ray quality control of fruits and seeds

Development of the X-ray technologies provides an opportunity to get images of small non-thick objects, to use modern X-ray equipment and to introduce its use into the practice of seeds and fruits quality control (reproductive diasporas). This non-destructive method of X-ray control allows to determine filled seeds from empty and infected seeds and fruits. Such a way of control is important to be introduced in a variety of botanical institutions, including botanical gardens, and is important for finding low-quality seeds and fruits from the introduced plants and those, coming from the Inter-Botanical exchange

<i>Abies semenovii</i> B. Fedtsch. at the Peter the Great Botanical Garden

Abies semenovii B. Fedtsch. (Pinaceae) is an extremely rare flora species of the Central Asia (Kirghizia); it has been cultivated at the Peter the Great Botanical Garden of the Komarov Botanical Institute of the Russian Academy of Sciences (RAS) since 1949, where it was first introduced into general cultivation. Since 2000, upon reaching the age of 43 years, the seed reproduction of the plants is being marked. An X-ray test proved seeds, collected in 2014, to be filled and full. In spring 2015, first time in the 67 years of cultivating this specie in St. Petersburg area, first young crops were received. Abies semenovii – a cold hard and decorative tree – has to be introduced into the gardening of St. Petersburg and shall be promoted into the Karelia and further to the northern regions of the European part of the Russian Federation

Adverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users

Mapping the pharmaceutically significant entities on natural language to standardized terms/concepts is a key task in the development of the systems for pharmacovigilance, marketing, and using drugs out of the application scope. This work estimates the accuracy of mapping adverse reaction mentions to the concepts from the Medical Dictionary of Regulatory Activity (MedDRA) in the case of adverse reactions extracted from the reviews on the use of pharmaceutical products by Russian-speaking Internet users (normalization task). The solution we propose is based on a neural network approach using two neural network models: the first one for encoding concepts, and the second one for encoding mentions. Both models are pre-trained language models, but the second one is additionally tuned for the normalization task using both the Russian Drug Reviews (RDRS) corpus and a set of open English-language corpora automatically translated into Russian. Additional tuning of the model during the proposed procedure increases the accuracy of mentions of adverse drug reactions by 3% on the RDRS corpus. The resulting accuracy for the adverse reaction mentions mapping to the preferred terms of MedDRA in RDRS is 70.9% F1-micro. The paper analyzes the factors that affect the accuracy of solving the task based on a comparison of the RDRS and the CSIRO Adverse Drug Event Corpus (CADEC) corpora. It is shown that the composition of the concepts of the MedDRA and the number of examples for each concept play a key role in the task solution. The proposed model shows a comparable accuracy of 87.5% F1-micro on a subsample of RDRS and CADEC datasets with the same set of MedDRA preferred terms

Investigation of Microstructure and Corrosion Resistance of Ti-Al-V Titanium Alloys Obtained by Spark Plasma Sintering

The research results of the microstructure and corrosion resistance of Ti and Ti-Al-V Russian industrial titanium alloys obtained by spark plasma sintering (SPS) are described. Investigations of the microstructure, phase composition, hardness, tensile strength, electrochemical corrosion resistance and hot salt corrosion of Ti-Al-V titanium alloy specimens were carried out. It was shown that the alloy specimens have a uniform highly dense microstructure and high hardness values. The studied alloys also have high resistance to electrochemical corrosion during tests in acidic aqueous solution causing the intergranular corrosion as well as high resistance to the hot salt corrosion. The assumption that the high hardness of the alloys as well as the differences in the corrosion resistance of the central and lateral parts of the specimens are due to the diffusion of carbon from the graphite mold into the specimen surface was suggested

Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models

The paper presents the full-size Russian corpus of Internet users&rsquo; reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them&mdash;medication and disease&mdash;include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora

Accuracy Analysis of the End-to-End Extraction of Related Named Entities from Russian Drug Review Texts by Modern Approaches Validated on English Biomedical Corpora

An extraction of significant information from Internet sources is an important task of pharmacovigilance due to the need for post-clinical drugs monitoring. This research considers the task of end-to-end recognition of pharmaceutically significant named entities and their relations in texts in natural language. The meaning of “end-to-end” is that both of the tasks are performed within a single process on the “raw” text without annotation. The study is based on the current version of the Russian Drug Review Corpus—a dataset of 3800 review texts from the Russian segment of the Internet. Currently, this is the only corpus in the Russian language appropriate for research of the mentioned type. We estimated the accuracy of the recognition of the pharmaceutically significant entities and their relations in two approaches based on neural-network language models. The first core approach is to sequentially solve tasks of named-entities recognition and relation extraction (the sequential approach). The second one solves both tasks simultaneously with a single neural network (the joint approach). The study includes a comparison of both approaches, along with the hyperparameters selection to maximize resulting accuracy. It is shown that both approaches solve the target task at the same level of accuracy: 52–53% macro-averaged F1-score, which is the current level of accuracy for “end-to-end” tasks on the Russian language. Additionally, the paper presents the results for English open datasets ADE and DDI based on the joint approach, and hyperparameter selection for the modern domain-specific language models. The result is that the achieved accuracies of 84.2% (ADE) and 73.3% (DDI) are comparable or better than other published results for the datasets

Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models

The paper presents the full-size Russian corpus of Internet users’ reviews on medicines with complex named entity recognition (NER) labeling of pharmaceutically relevant entities. We evaluate the accuracy levels reached on this corpus by a set of advanced deep learning neural networks for extracting mentions of these entities. The corpus markup includes mentions of the following entities: medication (33,005 mentions), adverse drug reaction (1778), disease (17,403), and note (4490). Two of them—medication and disease—include a set of attributes. A part of the corpus has a coreference annotation with 1560 coreference chains in 300 documents. A multi-label model based on a language model and a set of features has been developed for recognizing entities of the presented corpus. We analyze how the choice of different model components affects the entity recognition accuracy. Those components include methods for vector representation of words, types of language models pre-trained for the Russian language, ways of text normalization, and other pre-processing methods. The sufficient size of our corpus allows us to study the effects of particularities of annotation and entity balancing. We compare our corpus to existing ones by the occurrences of entities of different types and show that balancing the corpus by the number of texts with and without adverse drug event (ADR) mentions improves the ADR recognition accuracy with no notable decline in the accuracy of detecting entities of other types. As a result, the state of the art for the pharmacological entity extraction task for the Russian language is established on a full-size labeled corpus. For the ADR entity type, the accuracy achieved is 61.1% by the F1-exact metric, which is on par with the accuracy level for other language corpora with similar characteristics and ADR representativeness. The accuracy of the coreference relation extraction evaluated on our corpus is 71%, which is higher than the results achieved on the other Russian-language corpora