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Resumo Objetivo: Esta pesquisa tem como objetivo estruturar um modelo hierárquico que integre os conceitos da indústria 4.0 (I4.0) e padronizar conceitos com base na literatura. Originalidade/valor: Kamble et al. (2018) apontam para a falta de uma arquitetura para representar os conceitos I4.0. Este artigo traz uma abordagem para a relação entre esses conceitos de I4.0. Ele expande os es- tudos de Ghobakhloo (2018) e Liao et al. (2017) e homogeneíza termos presentes na literatura. Design/metodologia/abordagem: A partir de uma revisão sistemática da literatura nas bases de dados Scopus e ScienceDirect, de 2011 a 2019, foram revisados 91 artigos, dos quais 58 foram analisados. Resultados: A partir da literatura, os termos relacionados com I4.0 foram agrupados em três categorias: tecnologias, princípios e dimensões. Os clusters de tecnologia representam ferramentas utilizadas para promover mudanças e transformações nos processos, aqui chamados de princípios. Essas mudanças e transformações criam novos padrões da indústria, permitindo a integração de processos para a solução de problemas, contribuindo para a implementação do gerenciamento inteligente. A relação entre essas categorias resulta em um modelo hierárquico. / Abstract Purpose: This research aims to structure a hierarchical model that integrates the industry 4.0 (I4.0) concepts and standardizes concepts based on the literature. Originality/value: Kamble et al. (2018) point out the lack of architecture to represent I4.0 concepts. This paper brings an approach to the relationship between these concepts of I4.0. It expands the studies by Ghobakhloo (2018) and Liao et al. (2017) and homogenizes terms present in the literature. Design/methodology/approach: From a systematic review of the literature in the Scopus and ScienceDirect databases, from 2011 to 2019, 91 articles were reviewed, of which 58 articles were analyzed. Findings: From the literature, the terms related to I4.0 were grouped into three categories: technologies, principles, and dimensions. Technology clusters represent tools used to promote changes and transformations in the processes, here called principles. These changes and transformations create new industry standards, enabling process integration for problem-solving, and contributing to implementing intelligent management. The relationship between these categories results in a hierarchical model for I4.0 concepts. This hierarchical model can be used to identify opportunities for future research, demonstrating associations between categories that have not yet been explored. It opens possibilities for organizations to enter the fourth industrial revolution. The results help practitioners and researchers to understand this new process in detail and facilitate the construction of a valid and operational intelligent manufacturing platform.

Creation of reliable and efficient flying ad-hoc networks (FANET) requires detailed development of the model of the physical layer of data transmission, determined by the conditions of operation of the networks. The problems of well-known software simulators of communication networks are the simplified nature of the physical layer, as well as the inability to obtain specific analytical solutions in the process of simulation. The hierarchical model of formation of information signals which allows to represent various types of communication channels and the channel-forming equipment, for providing their analytical description and the further analysis is developed. The model allows to describe communication channels between UAVs and (or) ground control centers taking into account the effects of attenuation, intersymbol interference, multipath propagation of signals; schemes of terminal and intermediate network equipment with linear and nonlinear signal conversion; circuits with forward regulation, backward regulation and combined regulation; circuits with multi-channel signal generation and processing, as well as cross-links between channels. Analytical expressions of the transfer function of the generalized hierarchical model for an arbitrary number of disclosed levels of hierarchy are obtained. An example of the presentation and study of the UAV transmitter circuit on the basis of a hierarchical model of signal formation is considered.

When endeavoring to make informed decisions, conservation biologists must frequently contend with disparate sources of data and competing hypotheses about the likely impacts of proposed decisions on the resource status. Frequently, statistical analyses, modeling (e.g., for population projection) and optimization or simulation are conducted as separate exercises. For example, a population model might be constructed, whose parameters are then estimated from data (e.g., ringing studies, population surveys). This model might then be used to predict future population states, from current population estimates, under a particular management regime. Finally, the parameterized model might also be used to evaluate alternative candidate management decisions, via simulation, optimization, or both. This approach, while effective, does not take full advantage of the integration of data and model components for prediction and updating; we propose a hierarchical Bayesian context for this integration. In the case of American black ducks (Anas rubripes), managers are simultaneously faced with trying to extract a sustainable harvest from the species, while maintaining individual stocks above acceptable thresholds. The problem is complicated by spatial heterogeneity in the growth rates and carrying capacity of black ducks stocks, movement between stocks, regional differences in the intensity of harvest pressure, and heterogeneity in the degree of competition from a close congener, mallards (Anas platyrynchos) among stocks. We have constructed a population life cycle model that takes these components into account and simultaneously performs parameter estimation and population prediction in a Bayesian framework. Ringing data are used to develop posterior predictive distributions for harvest mortality rates, given as input decisions about harvest regulations. Population surveys of black ducks and mallards are used to obtain stock-specific estimates of population size for both species, for inputs into the population life-cycle model. These estimates are combined with the posterior distributions for harvest mortality, to obtain posterior predictive distributions of future population status for candidate sets of regional harvest regulations, under alternative biological hypotheses for black duck population dynamics. These distributions might then be used for both the exploration of optimal harvest policies and for sequential updating of model posteriors, via comparison of predictive distributions to future survey estimates of stock-specific abundance. Our approach illustrates advantages of MCMC for integrating disparate data sources into a common predictive framework, for use in conservation decision making.