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Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

History matching is an important reservoir engineering process whereby the values of uncertain attributes of a reservoir model are changed to find models that have a better chance of reproducing the performance of an actual reservoir. As a typical inverse and ill-posed problem, different combinations of reservoir uncertain attributes lead to equally well-matched models and the success of a history-matching approach is usually measured in terms of its ability to efficiently find multiple history-matched models inside the search space defined by the parameterization of the problem (multiple-matched models have a higher chance of better representing the reservoir performance forecast). While studies on history-matching approaches have produced remarkable progress over the last two decades, given the uniqueness of each reservoir's history-matching problem, no strategy is proven effective for all cases, and finding alternative, efficient, and effective history-matching methodologies is still a research challenge. In this work, we introduce a learning-from-data approach with path relinking and soft clustering to the history-matching problem. The proposed algorithm is designed to learn the patterns of input attributes that are associated with good matching quality from the set of available solutions, and has two stages that handle different types of reservoir uncertain attributes. In each stage, the algorithm evaluates the data of all-available solutions continuously and, based on the acquired information, dynamically decides what needs to be changed, where the changes shall take place, and how such changes will occur in order to generate new (and hopefully better) solutions. We validate our approach using the UNISIM-I-H benchmark, a complex synthetic case constructed with real data from the Namorado Field, Campos Basin, Brazil. Experimental results indicate the potential of the proposed approach in finding models with significantly better history-matching quality. Considering a global misfit quality metric, the final best solutions found by our approach are up to 77% better than the corresponding initial best solutions in the datasets used in the experiments. Moreover, compared with previous work for the same benchmark, the proposed learning-from-data approach is competitive regarding the quality of solutions found and, above all, it offers a significant reduction (up to 30 × less) in the number of simulations. [ABSTRACT FROM AUTHOR]

This paper initially proposes a heuristic algorithm for the p-median problem designed for large weighted graphs. The problem is approached through the construction of p trees whose shapes are progressively modified according to successive tests over the stability of their roots and vertices. The algorithm seems promising because: (i) on a regular PC it can handle problems of the order of 500 vertices, while the mainframe version goes indefinitely further, (ii) contrary to what normally would be expected, execution times seem to be inversely proportional to p, and even for large problems, they may be reasonable, especially if p is large relative to the number of vertices, and (iii) it produces solutions of good quality and in most of the cases studied, it outperforms the traditional heuristic of Teitz and Bart. A real application of the algorithm embedded in a methodology to evaluate the location of 85 public schools, among 389 possible vertices, in the metropolitan area of Rio de Janeiro is reported. Results confirmed the conjecture of poor location and the procedure was able to identify several micro-regions simply void of schools. The methodology is being well received by the education authorities and its extension to the whole metropolitan area is being considered. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the present study was to examine the prevalence and factors associated with sarcopenia in older residents in São Paulo, Brazil. Design: Cross-sectional study. Setting: São Paulo, Brazil. Participants: 1,149 older individuals from the second wave of the Saúde, Bem-Estar e Envelhecimento (SABE) study from 2006. Measurements: The definition of sarcopenia was based on the consensus of the European Working Group on Sarcopenia in Older People (EWGSOP), which include three components: low muscle mass, assessed by a skeletal muscle mass index of ≤8.90kg/m for men and ≤6.37kg/m for women; low muscle strength, assessed by handgrip strength <30kg for men and <20kg for women; and low physical performance, assessed by gait speed <0.8m/s. Diagnosis of sarcopenia required presence of low muscle mass plus low muscle strength or low physical performance. Socio-demographic and behavioral characteristics, medical conditions and nutritional status were considered as independent variables to determine the associated factors using a logistic regression model. Results: The prevalence of sarcopenia was 16.1% in women and 14.4% in men. Advanced age with a dose response effect, cognitive impairment, lower income, smoking, undernutrition and risk for undernutrition (p<0.05) were factors associated with sarcopenia. Conclusions: The EWGSOP algorithm is useful to define sarcopenia. The prevalence of sarcopenia in the Brazilian elderly population is high and several associated factors show that this syndrome is affected by multiple domains. No differences were observed by gender in any age groups. [ABSTRACT FROM AUTHOR]

Given a non-convex two-dimensional area and identical rectangular stands, we consider the problem of placing the maximum number of stands in the area, by satisfying a number of operational constraints. We present linear programming models and show the total unimodularity of the matrices associated with their constraint sets. We then give computational results obtained by applying the models to the real-world case of the Beira Mar handcraft fair of Fortaleza (Brazil). [ABSTRACT FROM AUTHOR]

Simulation of granular particles is an important tool in many fields. However, simulation of particles of complex shapes remains largely out of reach even in two-dimension. One of the major hurdles is the difficulty in representing particles in an efficient, flexible, and accurate manner. By representing particles as convex polyhedrons which are themselves the intersection of a set of half spaces, we develop a method that allows one to efficiently carry out key operations, including particle–particle and particle–container wall overlapping detection, precise identification of the overlapping region, particle shifting, particle rotation, and others. The simulation of packing 1,000 particles into a container takes only a few minutes with this approach. We further demonstrate the potential of this approach with a simulation that re-generates the “Brazil nut” phenomenon by mixing and shaking particles of two different sizes. [ABSTRACT FROM AUTHOR]