Energy Efficiency of Hybrid-Power HetNets: A Population-like Games Approach

In this paper, a distributed control scheme based on population games is proposed. The controller is in charge of dealing with the energy consumption problem in a Heterogeneous Cellular Network (HetNet) powered by hybrid energy sources (grid and renewable energy) while guaranteeing appropriate quality of service (QoS) level at the same time. Unlike the conventional approach in population games, it considers both atomicity and non-anonymity. Simulation results show that the proposed population-games approach reduces grid consumption by up to about 12% compared to the traditional best-signal level association policy.U.S. Air Force Office of Scientific Research FA9550-17-1-0259Ministerio de Cultura y Deporte DPI2016-76493-C3-3-RMinisterio de Economía y Empresa DPI2017-86918-

Atomicity and non-anonymity in population-like games for the energy efficiency of hybrid-power HetNets

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, the user–base station (BS) association problem is addressed to reduce grid consumption in heterogeneous cellular networks (HetNets) powered by hybrid energy sources (grid and renewable energy). The paper proposes a novel distributed control scheme inspired by population games and designed considering both atomicity and non-anonymity – i.e., describing the individual decisions of each agent. The controller performance is considered from an energy–efficiency perspective, which requires the guarantee of appropriate qualityof-service (QoS) levels according to renewable energy availability. The efficiency of the proposed scheme is compared with other heuristic and optimal alternatives in two simulation scenarios. Simulation results show that the proposed approach inspired by population games reduces grid consumption by 12% when compared to the traditional best-signal-level association policy.Peer ReviewedPostprint (author's final draft

Atomicity and non-anonymity in population-like games for the energy efficiency of hybrid-power HetNets

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, the user–base station (BS) association problem is addressed to reduce grid consumption in heterogeneous cellular networks (HetNets) powered by hybrid energy sources (grid and renewable energy). The paper proposes a novel distributed control scheme inspired by population games and designed considering both atomicity and non-anonymity – i.e., describing the individual decisions of each agent. The controller performance is considered from an energy–efficiency perspective, which requires the guarantee of appropriate qualityof-service (QoS) levels according to renewable energy availability. The efficiency of the proposed scheme is compared with other heuristic and optimal alternatives in two simulation scenarios. Simulation results show that the proposed approach inspired by population games reduces grid consumption by 12% when compared to the traditional best-signal-level association policy.Peer ReviewedPostprint (author's final draft

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans – anteaters, sloths, and armadillos – have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with 24 domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, ten anteaters, and six sloths. Our dataset includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data-paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the south of the USA, Mexico, and Caribbean countries at the northern portion of the Neotropics, to its austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n=5,941), and Cyclopes sp. has the fewest (n=240). The armadillo species with the most data is Dasypus novemcinctus (n=11,588), and the least recorded for Calyptophractus retusus (n=33). With regards to sloth species, Bradypus variegatus has the most records (n=962), and Bradypus pygmaeus has the fewest (n=12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other datasets of Neotropical Series which will become available very soon (i.e. Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans dataset

Optimization of the enzymatic treatment of olive oil pomace for lignocellulosic ethanol production

Measuring the activity of an enzyme is equivalent to describe its catalytic properties, that mainly they are influenced by the type of environment in which the enzyme is placed to operate. The objective of this work is to optimize the activity of the enzyme cellulase of Trichoderma Reesei to catalyze the reaction of saccharification of the contained cellulose on the Olive Pomace (final solid waster of the elaboration of the olives in the production process of olive oil). The saccharification reaction previews gradual deterioration of the cellulose in little parts until obtaining the monomer or dimers (cellobiose) of the glucose, final result of the reaction. A design of experiments is used to determine the optimal enzyme does for maximum ethanol production in a yeast fermentation process. Pomace pretreatment and quantity as well as enzyme concentration and hydrolysis temperature were varied selectively according to the design. In this way fewer experiments were needed to obtain the optimal formula for second generation ethanol production process

Hybrid modelling and self-learning system for dextrose crystallization process

This paper deals with the modelling of a continuous cooling column crystallizer. An accurate model of this system is needed for complex process control. The investigated system consists on dextrose monohydrate in aqueous solution. An adaptive hybrid model is presented. The model consists of two parts: the phenomenological model, expressed by a set of differential algebraic equations, and a neural network, based on historical data, developed by the fuzzy ARMAP technique. The empirical part of the hybrid model is aimed at eliminating the deviations of the prediction of the phenomenological model caused mainly by incrustations over the surface of the cooling coils located along the column crystallizer. The model is adaptive since neural network parameters are updated by a self-learning system (SLS) based on the acquired process data storage of the DCS of an industrial plant. Firstly, the hybrid model was implemented by using the data of a three-month campaign of the crystallizer, then the self-learning technique was checked on site in a subsequent campaign. © 2010 The Institution of Chemical Engineers