Assessment of green roof thermal behavior: A coupled heat and mass transfer model

Green roofs have a positive effect on the energy performance of buildings, providing a cooling effect in summer, along with a more efficient harnessing of the solar radiation due to the reflective properties found inside the foliage. For assessing these effects, the thermodynamic model was developed as well as the thermo-physical properties of the green roof components were characterized. Its typologies and vegetation styles should also be studied. The proposed model is based on energy balance equations expressed for foliage and soil media. In this study, the influence of the mass transfer in the thermal properties and evapotranspiration were taken into account. We then added the water balance equation into our model and performed a numerical simulation. By assuming the outdoor conditions, the roof support temperature and the drainage water as inputs, the model evaluates the temperatures evolution at foliage and soil ground levels. A parametric study was performed using the proposed model to classify green roofs depending on the considered climate condition. Comparisons were undertaken with a roof slab concrete model; a significant difference (of up to 30 °C) in temperature between the outer surfaces of the two roofs was noticed in summer. The model was experimentally validated according to green roof platform, which was elaborated. The mass transfer effect in the subtract was very effective in reducing the model errors. Simulation results show that the use of vegetation in the roof building improves not only thermal comfort conditions, but the energy performance of a building.     

The micro-imaging station of the TopoTomo beamline at the ANKA synchrotron light source

The TopoTomo bending magnet beamline at the ANKA synchrotron facility in Karlsruhe (Germany) operates in the hard X-ray regime (above 6 keV). Recently, an X-ray micro-imaging station has been installed at TopoTomo. For typical imaging applications, a filtered white beam or from 2009 on a double-multilayer monochromator is used. In order to optimize the field of view and the resolution of the available indirect pixel detectors, different optical systems have been installed, adapted, respectively, to a large field of view (macroscope) and to high spatial resolution (microscope). They can be combined with different camera systems, ranging from 14-bit dynamic range CCDs to fast CMOS cameras. The spatial resolution can be brought substantially beyond the micrometer limit by using a Bragg magnifier. Due to the moderate flux of the beamline compared to insertion-device beamlines on third generation light sources, special emphasis has been put on the efficiency of the detectors via a dedicated scintillator concept. The layout of the beamline optics makes optimal use of the coherence properties. Thus, absorption contrast, phase-contrast and analyzer-based imaging can be applied. Additionally, white beam synchrotron topography is performed, using digital indirect X-ray pixel detectors as well as X-ray film.     

Catalytic wet peroxide oxidation (CWPO) of phenol over mixed (AlCu)-pillared clays

New mixed (AlCu)-pillared clays are prepared from a crude bentonite sample (H) (Tunisia deposit) by two discrete procedures: (a) a classical ‘D’ method by which Al or (Al, Cu) nitrate solutions, hydrolyzed with NaOH, are added to a 2% clay suspension and (b) a ‘P’ method where the clay powder is directly dispersed in the pillaring solution. Intercalation reactions are performed at 40°C, with an OH/(Al, Cu) molar ratio equal to 1.5 or 2 and an initial atomic percentage of copper (100 Cu/Al + Cu) of 0, 5 or 10%. Chemical analyses, XRD spectra and surface properties show that clays are intercalated after calcination at 300°C from both preparation procedures. There is increase of the Al content, d₀₀₁ and of the BET surface area, the copper content remains quite low whatever the conditions of preparation. Nevertheless, it seems that the copper content resulting from the ‘P’ method is higher. During phenol oxidation with hydrogen peroxide (CWPO reaction), the copper content and the procedure of preparation significantly influence the catalytic activity. Moreover, the very low copper solubilization proves that copper is certainly associated with alumina at the surface of the catalyst in mixed species intercalated or/and dispersed on the clay surface.     

A model-driven approach for usability engineering of interactive systems

Usability is considered to be one of the most important quality factors that determine the success/failure in the actual use of an interactive system. This can explain the ever-increasing number of publications addressing the problem of usability evaluation. However, most of these proposals only consider usability evaluations after the application is fully implemented and deployed. Some others are based on reviewing usability principles in intermediate artifacts with regard to their conformance with a set of guidelines. Since the traceability between these artifacts and the final application is not well established, performing usability evaluations by considering these artifacts as input may not ensure the usability of the final application. This problem may be alleviated by using a model-driven engineering (MDE) approach due to its intrinsic traceability mechanisms that are established by the transformation processes. The present paper aims to delineate a method for evaluating usability throughout an MDE development life cycle by considering conceptual models as input. To do this, two main contributions are proposed. The first one, called usability-driven model transformation, aims to ensure that an intermediate artifact with the required level of usability is generated. It controls the model transformation process according to a set of usability attributes. The second contribution, called early usability evaluation, performs the usability evaluation from the conceptual models by defining metrics based on conceptual primitives that constitute the conceptual models. This evaluation would be a significant advantage with regard to saving time and resources. The early usability evaluation is empirically validated by comparing the usability measure obtained by our proposal and the level of usability perceived by the end-users.     

Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data

Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. This paper describes a new machine learning approach, called ILoNDF (Incremental data-driven Learning of Novelty Detector Filter). The approach is inspired by novelty detection theory and its learning method, which typically requires only examples from one class to learn a model. One advantage of ILoNDF is the ability of its generative learning to capture the intrinsic characteristics of the training data by continuously integrating the information relating to the relative frequencies of the features of training data and their co-occurrence dependencies. This makes ILoNDF rather stable and less sensitive to noisy features which may be present in the representation of the positive data. In addition, ILoNDF does not require extensive computational resources since it operates on-line without repeated training, and no parameters need to be tuned. In this study we mainly focus on the robustness of ILoNDF in dealing with high-dimensional noisy data and we investigate the variation of its performance depending on the amount of data available for training. To make our study comparable to previous studies, we investigate four common methods: PCA residuals, Hotelling’s T ² test, an auto-associative neural network, and a one-class version of the SVM classifier (lately a favored method for one-class classification). Experiments are conducted on two real-world text corpora: Reuters and WebKB. Results show that ILoNDF tends to be more robust, is less affected by initial settings, and consistently outperforms the other methods.