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.     

Traps centers impact on Silicon nanocrystal memories given by Random Telegraph Signal and low frequency noise

This work reports on a comprehensive process of trapping centers in Silicon nanocrystal (nc-Si) memories devices. The trap centers have been studied using Random Telegraph Signal (RTS) and Low Frequency (LF) techniques. The study of the traps which are responsible for RTS noise in non-volatile memories (NVM) devices as a function of gate voltage and temperature, offers the opportunity of studying the trapping/detrapping behaviour of a single interface trap center. The RTS parameters of the devices having random discrete fluctuations in the drain current get more information about trap energy level and spatial localization from the SiO₂/Si interface. The impact of trap centers has been also investigated showing the significant noise between memories and references devices. Furthermore, it has convincingly been shown that this discrete switching of the drain current between a high and a low state is the basic feature responsible for l/f^γ flicker noise in MOSFETs transistors.     

Low-Frequency Model for Radio-Frequency Absorbers

A simple model is developed to characterize the behavior of radio-frequency absorbers at low frequency. The absorber is represented by a flat, homogeneous, isotropic slab of lossy material, with effective constitutive parameters. These parameters are determined by a fit to measured data. Excellent fits are obtained in the two applications considered. The model is intended for use in the characterization of absorber-lined chambers at low frequency. It could also be used to predict the low-frequency performance of partially loaded shielded enclosures.     

Dielectric properties of honeydew melons and correlation with quality.

Dielectric properties of three honeydew melon cultivars, grown and harvested to provide a range of maturities, were measured with an open-ended coaxial-line probe and impedance analyzer over the frequency range from 10 MHz to 1.8 GHz. Probe measurements were made on the external surface of the melons and also on tissue samples from the edible internal tissue. Moisture content and soluble solids content (SSC) were measured for internal tissue samples, and SSC (sweetness) was used as the quality factor for correlation with the dielectric properties. Individual dielectric constant and loss factor correlations with SSC were low, but a high correlation was obtained between the SSC and permittivity from a complex-plane plot of dielectric constant and loss factor, each divided by SSC, for both the external surface and internal tissue measurements. However, SSC prediction from the dielectric properties by these relationships was not as high as expected. Permittivity data (dielectric constant and loss factor) for the melons are presented graphically to show their relationships with frequency for external surface and internal tissue measurements. A dielectric relaxation for the external surface measurements, which is attributable to bound water and Maxwell-Wagner relaxations, is also illustrated. Coefficients of determination for complex-plane plots, moisture content and SSC relationship, and penetration depth are also shown graphically. Further studies are needed for determining the practicality of sensing melon quality from the dielectric properties.     

Ultrasonic velocity measurements in isobutyric acid - water binary mixtures over the temperature range from 300.15 to 313.15 K

We measured the ultrasonic velocity, U, density, ρ, and viscosity, η in Isobutyric acid - water binary liquid mixtures over the entire composition range at temperatures ranging from 300.15 to 313.15 K under atmospheric pressure. The experimental data values were analyzed to determine, various acoustical parameters: adiabatic compressibility, β, acoustic impedance, Z, free length, L_f, free volume, V_f, molar volume, V_m, relaxation time, τ, absorption coefficient, α/f², internal pressure, π_i, Gibb’s free energy, ΔG, cohesive energy, CE, Wada’s constant, W, Rao’s constant, R_m and relative association, R_A. The variation of these parameters versus the mixture composition was explained on the basis of the intermolecular interactions between the components of the mixture.     

Probabilistic dual heuristic programming-based adaptive critic

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.     

Behavior-based approach to detect spam over IP telephony attacks

Spam over IP telephony (SPIT) is expected to become a serious problem as the use of voice over IP grows. This kind of spam is appreciated by spammers due to its effectiveness and low cost. Many anti-SPIT solutions are applied to resolve this problem but there are still limited in some cases. Thus, in this paper, we propose a system to detect SPIT attacks through behavior-based approach. Our framework operates in three steps: (1) collecting significant calls attributes by exploring and analyzing network traces using OPNET environment; (2) applying sliding windows strategy to properly maintain the callers profiles; and (3) classifying caller (i.e., legitimate or SPITter) using ten supervised learning methods: NaïveBayes, BayesNet, SMO RBFKernel, SMO PolyKernel, MultiLayerPerceptron with two and three layers, NBTree, J48, Bagging and AdaBoostM1. The results of our experiments demonstrate the great performance of these methods. Our study, based on receiver operating characteristics curves, shows that the AdaBoostM1 classifier is more efficient than the other methods and achieve an almost perfect detection rate with acceptable training time.     

Toward intrusion detection using belief decision trees for big data

Big data refers to datasets that we cannot manage with standard tools and within which lie valuable information previously hidden. New data mining techniques are needed to deal with the increasing size of such data, their complex structure as well as their veracity which is on covering questions of data imperfection and uncertainty. Even though big data veracity is often overlooked, it is very challenging and important for an accurate and reliable mining and knowledge discovery. This paper proposes MapReduce-based belief decision trees for big data as classifiers of uncertain large-scale datasets. The proposed averaging and conjunctive classification approaches are experimented for intrusion detection on KDD’99 massive intrusion dataset. Several granularity attacks’ levels have been considered depending on whether dealing with whole kind of attacks, or grouping them in categories or focusing on distinguishing normal and abnormal connections.