Energy Efficiency through Thermal Energy Storage - Evaluation of the Possibilities for the Swedish Building Stock, Phase 1

As a first step in assessing the potential of thermal energy storage in Swedish buildings, the current situation of the Swedish building stock and different storage methods are discussed in this paper. Overall, many buildings are from the 1960’s or earlier having a relatively high energy demand, creating opportunities for large energy savings. The major means of heating are electricity for detached houses and district heating for multi dwelling houses and premises. Cooling needs are relatively low but steadily increasing, emphasizing the need to consider energy storage for both heat and cold. The thermal mass of a building is important for passive storage of thermal energy but this has not been considered much when constructing buildings in Sweden. Instead, common ways of storing thermal energy in Swedish buildings today is in water storage tanks or in the ground using boreholes, while latent thermal energy storage is still very uncommon.

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Identificação fuzzy-multimodelos para sistemas não lineares

This paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Administration of single-dose GnRH agonist in the luteal phase in ICSI cycles: a meta-analysis

Background: The effects of gonadotrophin-releasing hormone agonist (GnRH-a) administered in the luteal phase remains controversial. This meta-analysis aimed to evaluate the effect of the administration of a single-dose of GnRH-a in the luteal phase on ICSI clinical outcomes.Methods: The research strategy included the online search of databases. Only randomized studies were included. The outcomes analyzed were implantation rate, clinical pregnancy rate (CPR) per transfer and ongoing pregnancy rate. The fixed effects model was used for odds ratio. In all trials, a single dose of GnRH-a was administered at day 5/6 after ICSI procedures.Results: All cycles presented statistically significantly higher rates of implantation (P < 0.0001), CPR per transfer (P = 0.006) and ongoing pregnancy (P = 0.02) in the group that received luteal-phase GnRH-a administration than in the control group (without luteal-phase-GnRH-a administration). When meta-analysis was carried out only in trials that had used long GnRH-a ovarian stimulation protocol, CPR per transfer (P = 0.06) and ongoing pregnancy (P = 0.23) rates were not significantly different between the groups, but implantation rate was significant higher (P = 0.02) in the group that received luteal-phase-GnRH-a administration. on the other hand, the results from trials that had used GnRH antagonist multi-dose ovarian stimulation protocol showed statistically significantly higher implantation (P = 0.0002), CPR per transfer (P = 0.04) and ongoing pregnancy rate (P = 0.04) in the luteal-phaseGnRH- a administration group. The majority of the results presented heterogeneity.Conclusions: These findings demonstrate that the luteal-phase single-dose GnRH-a administration can increase implantation rate in all cycles and CPR per transfer and ongoing pregnancy rate in cycles with GnRH antagonist ovarian stimulation protocol. Nevertheless, by considering the heterogeneity between the trials, it seems premature to recommend the use of GnRH-a in the luteal phase. Additional randomized controlled trials are necessary before evidence-based recommendations can be provided.

Phenylalanine ammonia-lyase activity in soybean roots extract measured by reverse-phase high performance liquid chromatography

The high performance liquid chromatography (HPLC) technique was applied to measure phenylalanine ammonia-lyase (PAL, EC 4.3.1.5) activity in soybean (Glycine max L. Merril cv. BR16) roots. t-Cinnamate, the catalytic product of the PAL reaction was quantified at 275 nm by isocratic elution with methanol:water through an ODS(M) column. Comparative experiments were carried out with 1.0 mM ferulic acid, an inducer of PAL activity. The results suggest that liquid chromatography is a rapid and sensitive method to analyze PAL activity in non-purified extract.

Complementarity of eastern and western hemisphere long-baseline neutrino oscillation experiments

We present a general formalism for extracting information on the fundamental parameters associated with neutrino masses and mixings from two or more long baseline neutrino oscillation experiments. This formalism is then applied to the current most likely experiments using neutrino beams from the Japan Hadron Facility (JHF) and Fermilab's NuMI beamline. Different combinations of muon neutrino or muon anti-neutrino running are considered. The type of neutrino mass hierarchy is extracted using the effects of matter on neutrino propogation. Contrary to naive expectation, we find that both beams using neutrinos is more suitable for determining the hierarchy provided that the neutrino energy divided by baseline (E/L) for NuMI is smaller than or equal to that of JHF, whereas to determine the small mixing angle, theta(13), and the CP or T violating phase delta, one neutrino and the other anti-neutrino are most suitable. We make extensive use of bi-probability diagrams for both understanding and extracting the physics involved in such comparisons.

Method for determination of vertical bar U(e)3 vertical bar in neutrino oscillation appearance experiments

We point out that determination of the MNS matrix element \U-e3\ = s(13) in long-baseline nu(mu) --> nu(e) neutrino oscillation experiments suffers from large intrinsic uncertainty due to the unknown CP violating phase delta and sign of Deltam(13)(2). We propose a new strategy for accurate determination of theta(13); tune the beam energy at the oscillation maximum and do the measurement both in neutrino and antineutrino channels. We show that it automatically resolves the problem of parameter ambiguities which involves delta, theta(13), and the sign of Deltam(13)(2). (C) 2002 Elsevier B.V. B.V. All rights reserved.

Spin-orbit coupling in the superconducting phase and DDW states of high-T-c cuprates

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Triangular-lattice anisotropic dimerized Heisenberg antiferromagnet: Stability and excitations of the quantum paramagnetic phase

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic Classification of Three-Phase Flow Patterns of Heavy Oil in a Horizontal Pipe Using Support Vector Machines

The pipe flow of a viscous-oil-gas-water mixture such as that involved in heavy oil production is a rather complex thereto-fluid dynamical problem. Considering the complexity of three-phase flow, it is of fundamental importance the introduction of a flow pattern classification tool to obtain useful information about the flow structure. Flow patterns are important because they indicate the degree of mixing during flow and the spatial distribution of phases. In particular, the pressure drop and temperature evolution along the pipe is highly dependent on the spatial configuration of the phases. In this work we investigate the three-phase water-assisted flow patterns, i.e. those configurations where water is injected in order to reduce friction caused by the viscous oil. Phase flow rates and pressure drop data from previous laboratory experiments in a horizontal pipe are used for flow pattern identification by means of the 'support vector machine' technique (SVM).

Multi-spectroscopic monitoring of cisplatin-derived species delivery from ureasil polyether hybrid matrix

Driven by the challenges involved in the development of new advanced materials with unusual drug delivery profiles capable of improving the therapeutic and toxicological properties of existing cancer chemotherapy, the one-pot sol-gel synthesis of flexible, transparent and insoluble urea-cross-linked polyether-siloxane hybrids has been recently developed. In this one-pot synthesis, the strong interaction between the antitumor cisplatin (CisPt) molecules and the ureasil-poly(propylene oxide) (PPO) hybrid matrix gives rise to the incorporation and release of an unknown CisPt-derived species, hindering the quantitative determination of the drug release pattern from the conventional UV-Vis absorption technique. In this article, we report the use of an original synchrotron radiation calibration method based on the combination of XAS and UV-Vis for the quantitative determination of the amount of Pt-based molecules released in water. Thanks to the combination of UV-Vis, XAS and Raman techniques, we demonstrated that both the CisPt molecules and the CisPt-derived species are loaded into an ureasil-PPO/ureasil-poly(ethylene oxide) (PEO) hybrid blend matrix. The experimentally determined molar extinction coefficient of the CisPt-derived species loaded into ureasil-PPO hybrid matrix enabled the simultaneous time-resolved monitoring of each Pt species released from this hybrid blend matrix.

Coordination polymer adsorbent for matrix solid-phase dispersion extraction of pesticides during analysis of dehydrated Hyptis pectinata medicinal plant by GC/MS

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multi-functional properties of CaCu3Ti4O12 thin films

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transfer of radon and parent nuclides U-238 and U-234 from soils of the Mendip Hills area, England, to the water phase

Laboratory time scale experiments were conducted on soils from the Mendip Hills area, England, with the purpose of evaluating the release of Rn-222 and their parent nuclides U-238 and U-234 to the water phase and to determine the influence of parameters that can affect the geochemical behaviour of these nuclides in natural systems. The specific surface area of the samples ranged from 43.8 to 52.5 cm(2) g(-1), where the particle size for all soil horizons is lognormally distributed, with modal values of the particle radius undersize ranging from 107 up to 203 mu m. The values for the released radon were between 26 and 194 pCi, which allowed to estimate emanation coefficients for these materials between 0.1 and 0.2, within the context of other values reported elsewhere. Soils derived from Carboniferous limestone and characterized by higher pH, exchangeable calcium, and the presence of U, but with a lower U-231/U-238 activity ratio, yielded the highest values for released Rn; however, this trend was not observed for dissolved U and its respective U-234/U-238 activity ratio, when considering the less aggressive etchant. Uranium is mobilized from rock matrix to A and B horizons in the analysed soil profiles, where its enrichment is about 10 times higher in soils derived from Carboniferous limestone. These data also permitted an evaluation of a theoretical model for the generation of Rn in soils and its transfer to water, in order to interpret the radioactivity due to this gas in groundwaters from the Mendip Hills district, England. (C) 1999 Elsevier B.V. B.V. All lights reserved.