Assessment criterion of structural resources of flight simulator motion system

На засадах врахування особливостей сприйняття пілотом інформації про рух і положення повітряного судна й особливостей просторового пілотування сформульований критерій оцінки використання конструктивних ресурсів динамічних стендів комплексних тренажерів повітряних суден. Розроблений критерій використовувався при розробці систем рухомості комплексних тренажерів літаків Іл-96-300 іТу-204 (Росія, Пензенське конструкторське бюро моделювання), Ан-74ТК-200 і Ан-140 (Україна, ДП «Антонов»). Це дало можливість оцінити конструктивні ресурси динамічних стендів і на підґрунті їхнього ефективного використання підвищити якість імітації акселераційних впливів

Языковая картина мира и оценка

Пономарёв Ю. М. Языковая картина мира и оценка / Ю. М. Пономарёв // Правове життя сучасної України : матеріали Міжнар. наук. конф. проф.-викл. та аспірант. складу (м. Одеса, 16-17 травня 2013 р.) / відп. за вип. В. М. Дрьомін ; НУ "ОЮА". Півд. регіон. центр НАПрН України. - Одеса : Фенікс, 2013. - Т. 1. - С. 670-672

Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events

[EN] Agri-food supply chains (AFSCs) are very vulnerable to high risks such as pandemics, causing economic and social impacts mainly on the most vulnerable population. Thus, it is a priority to implement resilient strategies that enable AFSCs to resist, respond and adapt to new market challenges. At the same time, implementing resilient strategies impact on the social, economic and environmental dimensions of sustainability. The objective of this paper is twofold: analyze resilient strategies on AFSCs in the literature and identify how these resilient strategies applied in the face of high risks affect the achievement of sustainability dimensions. The analysis of the articles is carried out in three points: consequences faced by agri-food supply chains due to high risks, strategies applicable in AFSCs, and relationship between resilient strategies and the achievement of sustainability dimensions.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCA-RISE-2015.Zavala-Alcívar, A.; Verdecho Sáez, MJ.; Alfaro Saiz, JJ. (2020). Resilient Strategies and Sustainability in Agri-Food Supply Chains in the Face of High-Risk Events. IFIP Advances in Information and Communication Technology. 598:560-570. https://doi.org/10.1007/978-3-030-62412-5_46S560570598Gray, R.: Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. 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Food Sci. 30, 43–48 (2019)Magalhães, A., Rossi, A., Zattar, I., Marques, M., Seleme, R.: Food traceability technologies and foodborne outbreak occurrences. Br. Food J. 121, 3362–3379 (2019)Denyer, D., Tranfield, D.: Producing a systematic review. In: Buchanan, D., Bryman, A. (eds.) The Sage Handbook of Organizational Research Methods, pp. 671–689. SAGE Publications Ltd., London (2009)Christopher, M., Peck, H.: Building the resilient supply chain. Int. J. Logist. Manag. 15, 1–14 (2004)Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018)Jüttner, U., Peck, H., Christopher, M.: Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. 6, 197–210 (2003)Behzadi, G., O’Sullivan, M., Olsen, T., Zhang, A.: Agribusiness supply chain risk management: a review of quantitative decision models. Omega (United Kingdom) 79, 21–42 (2018)Kleindorfer, P., Saad, G.: Managing disruption risks in supply chains. Pr. Op. Man. 14, 53–68 (2005)Vishnu, C., Sridharan, R., Gunasekaran, A., Ram Kumar, P.: Strategic capabilities for managing risks in supply chains: current state and research futurities. J. Adv. Manag. Res. 17(2), 173–211 (2019)Deaton, B., Deaton, B.: Food security and Canada’s agricultural system challenged by COVID-19. Can. J. Agric. Econ. 68(2), 143–149 (2020)Richards, T., Rickard, B.: COVID-19 impact on fruit and vegetable markets. C. J. Ag. Ec. 68(2), 189–194 (2020)Larue, B.: Labor issues and COVID-19. Can. J. Agric. Econ. Can. d’agroeconomie (2020). https://doi.org/10.1111/cjag.12233Hollnagel, E.: Epilogue: RAG: the resilience analysis grid. In: Hollnagel, E., Paries, J., Woods, D., Wreathall, J. (eds.) Resilience Engineering in Practice: A Guidebook. Ashgate Pr., pp. 275–296 (2011)Ponomarov, S., Holcomb, M.: Understanding the concept of supply chain resilience. Int. J. 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Design of agile supply chains including analysing the trade-off between number of partners and reliability

The reliability of supply partners is particularly vital in agile supply chains as it is vulnerable to the inability of a supply partner to meet its high responsiveness and flexibility requirements resulting in the disruption of the whole network. Disruption can have expensive and extensive results for the entire agile supply chain. To mitigate the risk of disruption and improve the reliability of the whole agile supply chain, decision-makers need to pay more attention to supply chain design and construction, whilst simultaneously taking into account the sourcing strategy decisions. This paper proposes a series of models for the design of agile supply chains using dynamic programming modelling. These provide decision-makers with a systematic way of analysing one of the key decisions of sourcing strategy, namely the trade-off between the number of supply partners and reliability. The efficacy of the models is demonstrated through their application to a Chinese bus and coach manufacturer by way of an empirical illustration. The results show that this approach is effective for this application and it can be applied in other related decision-making scenarios. The methods offered in this paper provide managers with a practical tool to design their agile supply chains while considering the trade-offs between the number of partners and the reliability of the entire agile supply chain

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

A global reference for human genetic variation

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.We thank the many people who were generous with contributing their samples to the project: the African Caribbean in Barbados; Bengali in Bangladesh; British in England and Scotland; Chinese Dai in Xishuangbanna, China; Colombians in Medellin, Colombia; Esan in Nigeria; Finnish in Finland; Gambian in Western Division – Mandinka; Gujarati Indians in Houston, Texas, USA; Han Chinese in Beijing, China; Iberian populations in Spain; Indian Telugu in the UK; Japanese in Tokyo, Japan; Kinh in Ho Chi Minh City, Vietnam; Luhya in Webuye, Kenya; Mende in Sierra Leone; people with African ancestry in the southwest USA; people with Mexican ancestry in Los Angeles, California, USA; Peruvians in Lima, Peru; Puerto Ricans in Puerto Rico; Punjabi in Lahore, Pakistan; southern Han Chinese; Sri Lankan Tamil in the UK; Toscani in Italia; Utah residents (CEPH) with northern and western European ancestry; and Yoruba in Ibadan, Nigeria. Many thanks to the people who contributed to this project: P. Maul, T. Maul, and C. Foster; Z. Chong, X. Fan, W. Zhou, and T. Chen; N. Sengamalay, S. Ott, L. Sadzewicz, J. Liu, and L. Tallon; L. Merson; O. Folarin, D. Asogun, O. Ikpwonmosa, E. Philomena, G. Akpede, S. Okhobgenin, and O. Omoniwa; the staff of the Institute of Lassa Fever Research and Control (ILFRC), Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria; A. Schlattl and T. Zichner; S. Lewis, E. Appelbaum, and L. Fulton; A. Yurovsky and I. Padioleau; N. Kaelin and F. Laplace; E. Drury and H. Arbery; A. Naranjo, M. Victoria Parra, and C. Duque; S. Däkel, B. Lenz, and S. Schrinner; S. Bumpstead; and C. Fletcher-Hoppe. Funding for this work was from the Wellcome Trust Core Award 090532/Z/09/Z and Senior Investigator Award 095552/Z/11/Z (P.D.), and grants WT098051 (R.D.), WT095908 and WT109497 (P.F.), WT086084/Z/08/Z and WT100956/Z/13/Z (G.M.), WT097307 (W.K.), WT0855322/Z/08/Z (R.L.), WT090770/Z/09/Z (D.K.), the Wellcome Trust Major Overseas program in Vietnam grant 089276/Z.09/Z (S.D.), the Medical Research Council UK grant G0801823 (J.L.M.), the UK Biotechnology and Biological Sciences Research Council grants BB/I02593X/1 (G.M.) and BB/I021213/1 (A.R.L.), the British Heart Foundation (C.A.A.), the Monument Trust (J.H.), the European Molecular Biology Laboratory (P.F.), the European Research Council grant 617306 (J.L.M.), the Chinese 863 Program 2012AA02A201, the National Basic Research program of China 973 program no. 2011CB809201, 2011CB809202 and 2011CB809203, Natural Science Foundation of China 31161130357, the Shenzhen Municipal Government of China grant ZYC201105170397A (J.W.), the Canadian Institutes of Health Research Operating grant 136855 and Canada Research Chair (S.G.), Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research (M.K.D.), a Le Fonds de Recherche duQuébec-Santé (FRQS) research fellowship (A.H.), Genome Quebec (P.A.), the Ontario Ministry of Research and Innovation – Ontario Institute for Cancer Research Investigator Award (P.A., J.S.), the Quebec Ministry of Economic Development, Innovation, and Exports grant PSR-SIIRI-195 (P.A.), the German Federal Ministry of Education and Research (BMBF) grants 0315428A and 01GS08201 (R.H.), the Max Planck Society (H.L., G.M., R.S.), BMBF-EPITREAT grant 0316190A (R.H., M.L.), the German Research Foundation (Deutsche Forschungsgemeinschaft) Emmy Noether Grant KO4037/1-1 (J.O.K.), the Beatriu de Pinos Program grants 2006 BP-A 10144 and 2009 BP-B 00274 (M.V.), the Spanish National Institute for Health Research grant PRB2 IPT13/0001-ISCIII-SGEFI/FEDER (A.O.), Ewha Womans University (C.L.), the Japan Society for the Promotion of Science Fellowship number PE13075 (N.P.), the Louis Jeantet Foundation (E.T.D.), the Marie Curie Actions Career Integration grant 303772 (C.A.), the Swiss National Science Foundation 31003A_130342 and NCCR “Frontiers in Genetics” (E.T.D.), the University of Geneva (E.T.D., T.L., G.M.), the US National Institutes of Health National Center for Biotechnology Information (S.S.) and grants U54HG3067 (E.S.L.), U54HG3273 and U01HG5211 (R.A.G.), U54HG3079 (R.K.W., E.R.M.), R01HG2898 (S.E.D.), R01HG2385 (E.E.E.), RC2HG5552 and U01HG6513 (G.T.M., G.R.A.), U01HG5214 (A.C.), U01HG5715 (C.D.B.), U01HG5718 (M.G.), U01HG5728 (Y.X.F.), U41HG7635 (R.K.W., E.E.E., P.H.S.), U41HG7497 (C.L., M.A.B., K.C., L.D., E.E.E., M.G., J.O.K., G.T.M., S.A.M., R.E.M., J.L.S., K.Y.), R01HG4960 and R01HG5701 (B.L.B.), R01HG5214 (G.A.), R01HG6855 (S.M.), R01HG7068 (R.E.M.), R01HG7644 (R.D.H.), DP2OD6514 (P.S.), DP5OD9154 (J.K.), R01CA166661 (S.E.D.), R01CA172652 (K.C.), P01GM99568 (S.R.B.), R01GM59290 (L.B.J., M.A.B.), R01GM104390 (L.B.J., M.Y.Y.), T32GM7790 (C.D.B., A.R.M.), P01GM99568 (S.R.B.), R01HL87699 and R01HL104608 (K.C.B.), T32HL94284 (J.L.R.F.), and contracts HHSN268201100040C (A.M.R.) and HHSN272201000025C (P.S.), Harvard Medical School Eleanor and Miles Shore Fellowship (K.L.), Lundbeck Foundation Grant R170-2014-1039 (K.L.), NIJ Grant 2014-DN-BX-K089 (Y.E.), the Mary Beryl Patch Turnbull Scholar Program (K.C.B.), NSF Graduate Research Fellowship DGE-1147470 (G.D.P.), the Simons Foundation SFARI award SF51 (M.W.), and a Sloan Foundation Fellowship (R.D.H.). E.E.E. is an investigator of the Howard Hughes Medical Institute