The rolling problem: overview and challenges

In the present paper we give a historical account -ranging from classical to modern results- of the problem of rolling two Riemannian manifolds one on the other, with the restrictions that they cannot instantaneously slip or spin one with respect to the other. On the way we show how this problem has profited from the development of intrinsic Riemannian geometry, from geometric control theory and sub-Riemannian geometry. We also mention how other areas -such as robotics and interpolation theory- have employed the rolling model.Comment: 20 page

ПРОИЗВОДИТЕЛЬНОСТЬ ПРЯМОТОЧНОГО ВИБРОПНЕВМАТИЧЕСКОГО СЕПАРАТОРА ЗЕРНОВОЙ СМЕСИ

Crop yield greatly depends on quality and biological value of seeds. And biological value of seeds is characterized here not as much by geometric parameters as by their density, which is related to ripeness and nature of seed. Seeds with the greatest density have high germination energy, viability and, respectively, show high yield. The most efficient method for seed sorting by density is vibro-pneumatic sorting in a fluidized bed. Based on the studies carried out, the design and engineering layout of a direct-flow vibro-pneumatic separator with new engineering solutions has been scientifically substantiated and practically implemented. To study the process of seed sorting in a fluidized bed, a test rig was designed and manufactured, with the main element of developed direct-flow vibro-pneumatic separator allowing to significantly improve the efficiency of sorting the components of grain mixture into fractions that differ in density by 10–15 %. Based on the theoretical and experimental studies carried out, a mathematical model is obtained to determine the performance of vibro-pneumatic equipment, considering physical and mechanical properties of processed seeds and design features of the equipment. Analysis of mathematical equations allowed to determine the main directions for increasing the efficiency of vibro-pneumatic sorting of grain and seeds in a fluidized bed. The obtained mathematical dependencies can be used in substantiating rational mode and constructive parameters of vibro-pneumatic equipment operation for seed sorting by density. Implementation of research results will allow forming research and engineering basis for creation of high-performance machines for pre-seeding grain and seed preparation. Урожайность сельскохозяйственных культур во многом зависит от качества семян, их биологической ценности. При этом биологическую ценность семян характеризуют не столько геометрические параметры, сколько их плотность, которая связана со спелостью и натурой семени. Семена с наибольшей плотностью обладают высокой энергией прорастания, всхожестью и, соответственно, дают высокий урожай. Наиболее эффективным методом сортирования семян по плотности является вибропневматическое сортирование в псевдоожиженном слое. На основании проведенных исследований научно обоснована и практически реализована конструктивно-технологическая схема прямоточного вибропневматического сепаратора с новыми техническими решениями. Для изучения процесса сортирования семян в псевдоожиженном слое разработан и изготовлен экспериментальный стенд, главным элементом которого является разработанный прямоточный вибропневматический сепаратор, позволяющий значительно повысить эффективность сортирования компонентов зерновой смеси на фракции, отличающиеся между собой плотностью в пределах 10–15 %. На основании проведенных теоретических и экспериментальных исследований получена математическая модель для определения производительности вибропневматического оборудования, учитывающая физико-механические свойства обрабатываемых семян и конструктивные особенности оборудования. Анализ математических уравнений позволил определить основные направления повышения эффективности процесса вибропневматического сортирования зерна и семян в псевдоожиженном слое. Полученные математические зависимости могут быть использованы при обосновании рациональных режимно-конструктивных параметров работы вибропневматического оборудования для сортирования семян по плотности. Внедрение результатов исследований позволит создать научную и техническую основу создания высокопроизводительных машин для предпосевной под- готовки зерна и семян.

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

International audienceBackground: Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods

Mining clinical relationships from patient narratives

Background The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives hand-annotated with clinically important relationships. Over a class of seven relation types, the system achieves an average F1 score of 72%, only slightly behind an indicative measure of human inter annotator agreement on the same task. We investigate the effectiveness of different features for this task, how extraction performance varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships. Conclusion We have shown that it is possible to extract important clinical relationships from text, using supervised statistical ML techniques, at levels of accuracy approaching those of human annotators. Given the importance of relation extraction as an enabling technology for text mining and given also the ready adaptability of systems based on our supervised learning approach to other clinical relationship extraction tasks, this result has significance for clinical text mining more generally, though further work to confirm our encouraging results should be carried out on a larger sample of narratives and relationship types

Теорія та практика менеджменту безпеки

У збірнику подано тези доповідей та виступів учасників Міжнародної науково-практичної конференції, присвяченої питанням теорії менеджменту безпеки, безпеки особистості, прикладним аспектам забезпечення соціальної, екологічної, економічної безпеки підприємств, питанням механізму забезпечення соціоекологоекономічної безпеки регіону, проблемам забезпечення національної безпеки

Multifaceted highly targeted sequential multidrug treatment of early ambulatory high-risk SARS-CoV-2 infection (COVID-19)

The SARS-CoV-2 virus spreading across the world has led to surges of COVID-19 illness, hospitalizations, and death. The complex and multifaceted pathophysiology of life-threatening COVID-19 illness including viral mediated organ damage, cytokine storm, and thrombosis warrants early interventions to address all components of the devastating illness. In countries where therapeutic nihilism is prevalent, patients endure escalating symptoms and without early treatment can succumb to delayed in-hospital care and death. Prompt early initiation of sequenced multidrug therapy (SMDT) is a widely and currently available solution to stem the tide of hospitalizations and death. A multipronged therapeutic approach includes 1) adjuvant nutraceuticals, 2) combination intracellular anti-infective therapy, 3) inhaled/oral corticosteroids, 4) antiplatelet agents/anticoagulants, 5) supportive care including supplemental oxygen, monitoring, and telemedicine. Randomized trials of individual, novel oral therapies have not delivered tools for physicians to combat the pandemic in practice. No single therapeutic option thus far has been entirely effective and therefore a combination is required at this time. An urgent immediate pivot from single drug to SMDT regimens should be employed as a critical strategy to deal with the large numbers of acute COVID-19 patients with the aim of reducing the intensity and duration of symptoms and avoiding hospitalization and death