Thermodynamic Stabillity of Hydrogen-Bonded Systems in Polar and Nonpolar Environments

The thermodynamic properties of a selected set of benchmark hydrogen-bonded systems (acetic acid dimer and the complexes of acetic acid with acetamide and methanol) was studied with the goal of obtaining detailed information on solvent effects on the hydrogen-bonded interactions using water, chloroform, and n-heptane as representatives for a wide range in the dielectric constant. Solvent effects were investigated using both explicit and implicit solvation models. For the explicit description of the solvent, molecular dynamics and Monte Carlo simulations in the isothermal isobaric (NpT) ensemble combined with the free energy perturbation technique were performed to determine solvation free energies. Within the implicit solvation approach, the polarizable continuum model and the conductor-like screening model were applied. Combination of gas phase results with the results obtained from the different solvation models through an appropriate thermodynamic cycle allows estimation of complexation free energies, enthalpies, and the respective entropic contributions in solution. Owing to the strong solvation effects of water the cyclic acetic acid dimer is not stable in aqueous solution. In less polar solvents the double hydrogen bond structure of the acetic acid dimer remains stable. This finding is in agreement with previous theoretical and experimental results. A similar trend as for the acetic acid dimer is also observed for the acetamide complex. The methanol complex was found to be thermodynamically unstable in gas phase as well as in any of the three solvents. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31: 2046-2055, 2010

Heat losses and thermal performance of commercial combined solar and pellet heating systems

Various pellet heating systems are marketed in Sweden, some of them in combination with a solar heating system. Several types of pellet heating units are available and can be used for a combined system. This article compares four typical combined solar and pellet heating systems: System 1 and 2 two with a pellet stove, system 3 with a store integrated pellet burner and system 4 with a pellet boiler. The lower efficiency of pellet heaters compared to oil or gas heaters increases the primary energy demand. Consequently heat losses of the various systems have been studied. The systems have been modeled in TRNSYS and simulated with parameters identified from measurements. For almost all systems the flue gas losses are the main heat losses except for system 3 where store heat losses prevail. Relevant are also the heat losses of the burner and the boiler to the ambient. Significant leakage losses are noticed for system 3 and 4. For buildings with an open internal design system 1 is the most efficient solution. Other buildings should preferably apply system 3. The right choice of the system depends also on whether the heater is placed inside or outside of the heated are. A large potential for system optimization exist for all studied systems, which when applied could alter the relative merits of the different system types.

PV-Wind Hybrid Systems for Swedish Locations

PV-Wind-Hybrid systems for stand-alone applications have the potential to be more cost efficient compared to PV-alone systems. The two energy sources can, to some extent, compensate each others minima. The combination of solar and wind should be especially favorable for locations at high latitudes such as Sweden with a very uneven distribution of solar radiation during the year. In this article PV-Wind-Hybrid systems have been studied for 11 locations in Sweden. These systems supply the household electricity for single family houses. The aim was to evaluate the system costs, the cost of energy generated by the PV-Wind-Hybrid systems, the effect of the load size and to what extent the combination of these two energy sources can reduce the costs compared to a PV-alone system. The study has been performed with the simulation tool HOMER developed by the National Renewable Energy Laboratory (NREL) for techno-economical feasibility studies of hybrid systems. The results from HOMER show that the net present costs (NPC) for a hybrid system designed for an annual load of 6000 kWh with a capacity shortage of 10% will vary between $48,000 and $87,000. Sizing the system for a load of 1800 kWh/year will give a NPC of $17,000 for the best and $33,000 for the worst location. PV-Wind-Hybrid systems are for all locations more cost effective compared to PV-alone systems. Using a Hybrid system is reducing the NPC for Borlänge by 36% and for Lund by 64%. The cost per kWh electricity varies between $1.4 for the worst location and $0.9 for the best location if a PV-Wind-Hybrid system is used.

Dynamic whole system testing of combined renewable heating systems : the current state of the art

Objective: For the evaluation of the energetic performance of combined renewable heating systems that supply space heat and domestic hot water for single family houses, dynamic behaviour, component interactions, and control of the system play a crucial role and should be included in test methods. Methods: New dynamic whole system test methods were developed based on “hardware in the loop” concepts. Three similar approaches are described and their differences are discussed. The methods were applied for testing solar thermal systems in combination with fossil fuel boilers (heating oil and natural gas), biomass boilers, and/or heat pumps. Results: All three methods were able to show the performance of combined heating systems under transient operating conditions. The methods often detected unexpected behaviour of the tested system that cannot be detected based on steady state performance tests that are usually applied to single components. Conclusion: Further work will be needed to harmonize the different test methods in order to reach comparable results between the different laboratories. Practice implications: A harmonized approach for whole system tests may lead to new test standards and improve the accuracy of performance prediction as well as reduce the need for field tests.

Testing of combined heating systems for small houses: Improved procedures for whole system test methods : Deliverable 2.3

Dynamic system test methods for heating systems were developed and applied by the institutes SERC and SP from Sweden, INES from France and SPF from Switzerland already before the MacSheep project started. These test methods followed the same principle: a complete heating system – including heat generators, storage, control etc., is installed on the test rig; the test rig software and hardware simulates and emulates the heat load for space heating and domestic hot water of a single family house, while the unit under test has to act autonomously to cover the heat demand during a representative test cycle. Within the work package 2 of the MacSheep project these similar – but different – test methods were harmonized and improved. The work undertaken includes:  • Harmonization of the physical boundaries of the unit under test. • Harmonization of the boundary conditions of climate and load. • Definition of an approach to reach identical space heat load in combination with an autonomous control of the space heat distribution by the unit under test. • Derivation and validation of new six day and a twelve day test profiles for direct extrapolation of test results.   The new harmonized test method combines the advantages of the different methods that existed before the MacSheep project. The new method is a benchmark test, which means that the load for space heating and domestic hot water preparation will be identical for all tested systems, and that the result is representative for the performance of the system over a whole year. Thus, no modelling and simulation of the tested system is needed in order to obtain the benchmark results for a yearly cycle. The method is thus also applicable to products for which simulation models are not available yet. Some of the advantages of the new whole system test method and performance rating compared to the testing and energy rating of single components are:  • Interaction between the different components of a heating system, e.g. storage, solar collector circuit, heat pump, control, etc. are included and evaluated in this test. • Dynamic effects are included and influence the result just as they influence the annual performance in the field. • Heat losses are influencing the results in a more realistic way, since they are evaluated under "real installed" and representative part-load conditions rather than under single component steady state conditions.   The described method is also suited for the development process of new systems, where it replaces time-consuming and costly field testing with the advantage of a higher accuracy of the measured data (compared to the typically used measurement equipment in field tests) and identical, thus comparable boundary conditions. Thus, the method can be used for system optimization in the test bench under realistic operative conditions, i.e. under relevant operating environment in the lab.   This report describes the physical boundaries of the tested systems, as well as the test procedures and the requirements for both the unit under test and the test facility. The new six day and twelve day test profiles are also described as are the validation results.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

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

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity

Aplicação de ontologias para métodos de negociação de um sistema multiagente para o reconhecimento de padrões

The use of intelligent agents in multi-classifier systems appeared in order to making the centralized decision process of a multi-classifier system into a distributed, flexible and incremental one. Based on this, the NeurAge (Neural Agents) system (Abreu et al 2004) was proposed. This system has a superior performance to some combination-centered methods (Abreu, Canuto, and Santana 2005). The negotiation is important to the multiagent system performance, but most of negotiations are defined informaly. A way to formalize the negotiation process is using an ontology. In the context of classification tasks, the ontology provides an approach to formalize the concepts and rules that manage the relations between these concepts. This work aims at using ontologies to make a formal description of the negotiation methods of a multi-agent system for classification tasks, more specifically the NeurAge system. Through ontologies, we intend to make the NeurAge system more formal and open, allowing that new agents can be part of such system during the negotiation. In this sense, the NeurAge System will be studied on the basis of its functioning and reaching, mainly, the negotiation methods used by the same ones. After that, some negotiation ontologies found in literature will be studied, and then those that were chosen for this work will be adapted to the negotiation methods used in the NeurAge.

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Shear bond strength of metal-ceramic repair systems

Statement of problem. When clinical fractures of the ceramic veneer on metal-ceramic prostheses can be repaired, the need for remake may be eliminated or postponed. Many different ceramic repair materials are available, and bond strength data are necessary for predicting the success of a given repair system.Purpose. This study evaluated the shear bond strength of different repair systems for metal-ceramic restorations applied on metal and porcelain.Material and methods. Fifty cylindrical specimens (9 X 3 mm) were fabricated in a nickel-chromium alloy (Vera Bond 11) and 50 in feldspathic porcelain (Noritakc). Metal (M) and porcelain (P) specimens were embedded in a polyvinyl chloride (PVC) ring and received I of the following bonding and resin composite repair systems (n=10): Clearfil SE Bond/Clearfil AP-X (CL), Bistite II DC/Palfique (BT), Cojet Sand/Z100 (Q), Scotchbond Multipurpose Plus/Z100 (SB) (control group), or Cojet Sand plus Scotchbond Multipurpose Plus/Z100 (CJSB). The specimens were stored in distilled water for 24 hours at 37 degrees C, thermal cycled (1000 cycles at 5 degrees C to 55 degrees C), and stored at 37 degrees C for 8 days. Shear bond tests between the metal or ceramic specimens and repair systems were performed in a mechanical testing machine with a crosshead speed of 0.5 mm/min. Mean shear bond strength values (MPa) were submitted to 1-way ANOVA and Tukey honestly significant difference tests (alpha=.05). Each specimen was examined under a stereoscopic lens with X 30 magnification, and mode of failure was classified as adhesive, cohesive, or a combination.Results. on metal, the mean shear bond strength values for the groups were as follows: MCL, 18.40 +/- 2.88(b); MBT, 8.57 +/- 1.00(d); MCJ, 25.24 +/- 3.46(a); MSB, 16.26 +/- 3.09(bc); and MCJSB, 13.11 +/- 1.24(c). on porcelain, the mean shear bond strength values ofeach group were as follows: PCL, 16.91 +/- 2.22(b); PBT, 18.04 +/- 3.2(ab); PCJ, 19.54 +/- 3.77(ab); PSB, 21.05 +/- 3.22(a); and PCJSB, 16.18 +/- 1.71(b). Within each substrate, identical superscript letters denote no significant differences among groups.Conclusions. The bond strength for the metal substrate was significantly higher using the Q system. For porcelain, SB, Q, and BT systems showed the highest shear bond strength values, and only SB was significantly different compared to CL and CJSB (P <.05).

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

Computer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.

Suppression of vibrations in strongly nonhomogeneous 2DOF systems

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

On energy transfer phenomena, in a nonlinear ideal and nonideal essential vibrating systems, coupled to a (MR) magneto-rheological damper

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

Phase Transition in Dynamical Systems: Defining Classes of Universality for Two-Dimensional Hamiltonian Mappings via Critical Exponents

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)