Prevalencia de neumoconiosis y su relación con medidas de higiene y seguridad industrial y niveles ambientales de carbón en minería subterránea en el departamento de Cundinamarca - Colombia, 2015

Introducción: La exposición en minas subterráneas a altos niveles de polvo de carbón está relacionada con patologías pulmonares. Objetivo: Determinar la prevalencia de neumoconiosis, medidas de higiene y seguridad industrial y su relación con niveles ambientales de carbón en trabajadores de minas de socavón en Cundinamarca. Materiales y Métodos: Estudio de corte transversal, en 215 trabajadores seleccionados mediante muestreo probabilístico estratificado con asignación proporcional. Se realizaron monitoreos ambientales, radiografías de tórax y encuestas con variables sociodemográficas y laborales. Se emplearon medidas de tendencia central y dispersión y la prueba de independencia ji-cuadrado de Pearson o pruebas exactas, con el fin de establecer las asociaciones. Resultados: El 99,5% de la población perteneció al género masculino, el 36,7% tenía entre 41-50 años, con un promedio de años de trabajo de 21,70 ± 9,99. La prevalencia de neumoconiosis fue de 42,3% y la mediana de la concentración de polvo de carbón bituminoso fue de 2,329670 mg/m3. El índice de riesgo de polvo de carbón presentó diferencias significativas en las categorías de bajo (p=0,0001) y medio (p=0,0186) con la prevalencia de neumoconiosis. El 84,2% reporto no usar mascarilla. No se presentan diferencias entre los niveles de carbón (p=0,194) con la prevalencia de neumoconiosis. Conclusiones: Se encontró una prevalencia de neumoconiosis de 42,3% en Cundinamarca. Se requiere contar con medidas de higiene y seguridad industrial efectivas para controlar el riesgo al que están expuestos los mineros de carbón por la inhalación de polvo de carbón.

Las implicaciones políticas de la relación China-América Latina: el Consenso Asiático como red de poder global y el caso brasileño (Tema Central)

This paper analyzes the People’s Republic of China economic and political ascendance in the 21st century, focusing on the evolution of its sui géneris economic development model and its significance for the relationship between China and the developing countries of the peripheral ‘Global South.’ The objective of this article is to analyze the relationship between China and Latin America in the 21st century, characterizing it as a new Center-periphery global power network based on trade and investment, which is often referred to as the ‘Asian Consensus’. The article will give special attention to the Brazilian case.

Modeling the effects of node heterogeneity on the performance of a real grid application

The performance benefit when using Grid systems comes from different strategies, among which partitioning the applications into parallel tasks is the most important. However, in most cases the enhancement coming from partitioning is smoothed by the effect of the synchronization overhead, mainly due to the high variability of completion times of the different tasks, which, in turn, is due to the large heterogeneity of Grid nodes. For this reason, it is important to have models which capture the performance of such systems. In this paper we describe a queueing-network-based performance model able to accurately analyze Grid architectures, and we use the model to study a real parallel application executed in a Grid. The proposed model improves the classical modelling techniques and highlights the impact of resource heterogeneity and network latency on the application performance.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Generating virtual environments to allow increased access to the built environment

The problems encountered by individuals with disabilities when accessing large public buildings is described and a solution based on the generation of virtual models of the built environment is proposed. These models are superimposed on a control network infrastructure, currently utilised in intelligent building applications such as lighting, heating and access control. The use of control network architectures facilitates the creation of distributed models that closely mirror both the physical and control properties of the environment. The model of the environment is kept local to the installation which allows the virtual representation of a large building to be decomposed into an interconnecting series of smaller models. This paper describes two methods of interacting with the virtual model, firstly a two dimensional aural representation that can be used as the basis of a portable navigational device. Secondly an augmented reality called DAMOCLES that overlays additional information on a user’s normal field of view. The provision of virtual environments offers new possibilities in the man-machine interface so that intuitive access to network based services and control functions can be given to a user.

Single-carrier frequency domain equalisation for hammerstein communication systems using complex-valued neural networks

Single-carrier (SC) block transmission with frequency-domain equalisation (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high bandwidthefficiency and high power-efficiency systems, the channel can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, We model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

Weighted alpha-rate dominating sets in social networks

We are looking into variants of a domination set problem in social networks. While randomised algorithms for solving the minimum weighted domination set problem and the minimum alpha and alpha-rate domination problem on simple graphs are already present in the literature, we propose here a randomised algorithm for the minimum weighted alpha-rate domination set problem which is, to the best of our knowledge, the first such algorithm. A theoretical approximation bound based on a simple randomised rounding technique is given. The algorithm is implemented in Python and applied to a UK Twitter mentions networks using a measure of individuals’ influence (klout) as weights. We argue that the weights of vertices could be interpreted as the costs of getting those individuals on board for a campaign or a behaviour change intervention. The minimum weighted alpha-rate dominating set problem can therefore be seen as finding a set that minimises the total cost and each individual in a network has at least alpha percentage of its neighbours in the chosen set. We also test our algorithm on generated graphs with several thousand vertices and edges. Our results on this real-life Twitter networks and generated graphs show that the implementation is reasonably efficient and thus can be used for real-life applications when creating social network based interventions, designing social media campaigns and potentially improving users’ social media experience.

The nested assembly of individual-resource networks

P>1. Much of the current understanding of ecological systems is based on theory that does not explicitly take into account individual variation within natural populations. However, individuals may show substantial variation in resource use. This variation in turn may be translated into topological properties of networks that depict interactions among individuals and the food resources they consume (individual-resource networks). 2. Different models derived from optimal diet theory (ODT) predict highly distinct patterns of trophic interactions at the individual level that should translate into distinct network topologies. As a consequence, individual-resource networks can be useful tools in revealing the incidence of different patterns of resource use by individuals and suggesting their mechanistic basis. 3. In the present study, using data from several dietary studies, we assembled individual-resource networks of 10 vertebrate species, previously reported to show interindividual diet variation, and used a network-based approach to investigate their structure. 4. We found significant nestedness, but no modularity, in all empirical networks, indicating that (i) these populations are composed of both opportunistic and selective individuals and (ii) the diets of the latter are ordered as predictable subsets of the diets of the more opportunistic individuals. 5. Nested patterns are a common feature of species networks, and our results extend its generality to trophic interactions at the individual level. This pattern is consistent with a recently proposed ODT model, in which individuals show similar rank preferences but differ in their acceptance rate for alternative resources. Our findings therefore suggest a common mechanism underlying interindividual variation in resource use in disparate taxa.

The web of connections between tourism companies: Structure and dynamics

Tourism destination networks are amongst the most complex dynamical systems, involving a myriad of human-made and natural resources. In this work we report a complex network-based systematic analysis of the Elba (Italy) tourism destination network, including the characterization of its structure in terms of several traditional measurements, the investigation of its modularity, as well as its comprehensive study in terms of the recently reported superedges approach. In particular, structural (the number of paths of distinct lengths between pairs of nodes, as well as the number of reachable companies) and dynamical features (transition probabilities and the inward/outward activations and accessibilities) are measured and analyzed, leading to a series of important findings related to the interactions between tourism companies. Among the several reported results, it is shown that the type and size of the Companies influence strongly their respective activations and accessibilities, while their geographical position does not seem to matter. It is also shown that the Elba tourism network is largely fragmented and heterogeneous, so that it could benefit from increased integration. (C) 2009 Elsevier B.V. All rights reserved.

Modularity and robustness of bone networks

Cortical bones, essential for mechanical support and structure in many animals, involve a large number of canals organized in intricate fashion. By using state-of-the art image analysis and computer graphics, the 3D reconstruction of a whole bone (phalange) of a young chicken was obtained and represented in terms of a complex network where each canal was associated to an edge and every confluence of three or more canals yielded a respective node. The representation of the bone canal structure as a complex network has allowed several methods to be applied in order to characterize and analyze the canal system organization and the robustness. First, the distribution of the node degrees (i.e. the number of canals connected to each node) confirmed previous indications that bone canal networks follow a power law, and therefore present some highly connected nodes (hubs). The bone network was also found to be partitioned into communities or modules, i.e. groups of nodes which are more intensely connected to one another than with the rest of the network. We verified that each community exhibited distinct topological properties that are possibly linked with their specific function. In order to better understand the organization of the bone network, its resilience to two types of failures (random attack and cascaded failures) was also quantified comparatively to randomized and regular counterparts. The results indicate that the modular structure improves the robustness of the bone network when compared to a regular network with the same average degree and number of nodes. The effects of disease processes (e. g., osteoporosis) and mutations in genes (e.g., BMP4) that occur at the molecular level can now be investigated at the mesoscopic level by using network based approaches.

A scenario-driven decision support system for serious crime investigation

Consideration of a wide range of plausible crime scenarios during any crime investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious crime investigation.

Precificação em orquestradores de informação: maximizando redes estáveis

Em redes de inovação baseadas em trocas de informação, o agente orquestrador se apropria das informações dos atores periféricos, gera inovação e distribui em forma de valor agregado. É sua função promover a estabilidade na rede fazendo com que a mesma tenha taxas não negativas de crescimento. Nos mercados de análise de crédito e fraude, por exemplo, ou bureaus funcionam como agentes orquestradores, concentrando as informações históricas da população que são provenientes de seus clientes e fornecendo produtos que auxiliam na tomada de decisão. Assumindo todas as empresas do ecossistema como agentes racionais, a teoria dos jogos se torna uma ferramenta apropriada para o estudo da precificação dos produtos como mecanismo de promoção da estabilidade da rede. Este trabalho busca identificar a relação de diferentes estruturas de precificação promovidas pelo agente orquestrador com a estabilidade e eficiência da rede de inovação. Uma vez que o poder da rede se dá pela força conjunta de seus membros, a inovação por esta gerada varia de acordo com a decisão isolada de cada agente periférico de contratar o agente orquestrador ao preço por ele estipulado. Através da definição de um jogo teórico simplificado onde diferentes agentes decidem conectar-se ou não à rede nas diferentes estruturas de preços estipuladas pelo agente orquestrador, o estudo analisa as condições de equilíbrio conclui que o equilíbrio de Nash implica em um cenário de estabilidade da rede. Uma conclusão é que, para maximizar o poder de inovação da rede, o preço a ser pago por cada agente para fazer uso da rede deve ser diretamente proporcional ao benefício financeiro auferido pela inovação gerada pela mesma. O estudo apresenta ainda uma simulação computacional de um mercado fictício para demonstração numérica dos efeitos observados. Através das conclusões obtidas, o trabalho cobre uma lacuna da literatura de redes de inovação com agentes orquestradores monopolistas em termos de precificação do uso da rede, servindo de subsídio de tomadores de decisão quando da oferta ou demanda dos serviços da rede.

Rede de controle em ambiente hospitalar: um protocolo multiciclos para automação hospitalar sobre IEEE 802.3 com IGMP Snooping

VALENTIM, R. A. M. ; MORAIS, A. H. F. ; SOUZA, V. S. V ; ARAUJO JUNIOR, H. B. ; BRANDAO, G. B. ; GUERREIRO, A. M. G. . Rede de Controle em Ambiente Hospitalar: um protocolo multiciclos para automação hospitalar sobre IEEE 802.3 com IGMP Snooping. Revista Ciência e Tecnologia, v. 11, p. 19, 2009

Desenvolvimento de sistemas embarcados para redes de sensores e atuadores sem fio aplicadas em unidades de elevação de petróleo do tipo Plunger-Lift

The Wireless Sensor Networks (WSN) methods applied to the lifting of oil present as an area with growing demand technical and scientific in view of the optimizations that can be carried forward with existing processes. This dissertation has as main objective to present the development of embedded systems dedicated to a wireless sensor network based on IEEE 802.15.4, which applies the ZigBee protocol, between sensors, actuators and the PLC (Programmable Logic Controller), aiming to solve the present problems in the deployment and maintenance of the physical communication of current elevation oil units based on the method Plunger-Lift. Embedded systems developed for this application will be responsible for acquiring information from sensors and control actuators of the devices present at the well, and also, using the Modbus protocol to make this network becomes transparent to the PLC responsible for controlling the production and delivery information for supervisory SISAL

Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development