Development and validation of a coupled heat and mass transfer model for green roofs

This paper describes a dynamic model of transient heat and mass transfer across a green roof component. The thermal behavior of the green roof layers is modeled and coupled to the water balance in the substrate that is determined accounting for evapotranspiration. The water balance variations over time directly impact the physical properties of the substrate and the evapotranspiration intensity. This thermal and hydric model incorporates wind speed effects within the foliage through a new calculation of the resistance to heat and mass transfer within the leaf canopy. The developed model is validated with experimental data from a one-tenth-scale green roof located at the University of La Rochelle. A comparison between the numerical and the experimental results demonstrates the accuracy of the model for predicting the substrate temperature and water content variations. The heat and mass transfer mechanisms through green roofs are analyzed and explained using the modeled energy balances, and parametric studies of green roof behavior are presented. A surface temperature difference of up to 25 °C was found among green roofs with a dry growing medium or a saturated growing medium. Furthermore, the thermal inertia effects, which are usually simplified or neglected, are taken into account and shown to affect the temperature and flux results. This study highlights the importance of a coupled evapotranspiration process model for the accurate assessment of the passive cooling effect of green roofs.     

A simplified solution of the regenerator periodic problem: the case for air conditioning

This work presents an analytical solution for the periodic heat transfer problem of regenerators used in air conditioning, which are operating at low regeneration temperatures and mass flow rates. These types of regenerators are characterized by NTU/Cr ^?<1. The partial differential equations for hot and cold airflows as well as the regenerator matrix were solved using a successive transformation of variables. They were reduced to the ordinary Bessel differential equation of the type xf″+f′?xf=0. The conventional initial and reversal boundary conditions were used in this work. The solution gives a correlation for the prediction of the regenerator effectiveness. Besides the effectiveness, the solution facilitates the calculation of the matrix temperature distribution and exit airflow temperatures. The result is compared with the available numerical and analytical solutions from literature. The result of this analysis reveals that the consideration of a non-linear matrix temperature distribution as in some previous work for low temperature regenerators just complicates the solution procedure with no significant improvement in the accuracy within the parameter space typical for air conditioning applications.     

A Parallel Concatenated Convolutional-Based Distributed Coded Cooperation Scheme for Relay Channels

In this paper, we investigate a coded cooperation diversity scheme suitable for L-relay channels operating in the soft-decode-and-forward (soft-DF) mode. The proposed scheme is based on parallel concatenated convolutional codes (PCCC). To improve the overall performance through diversity, the coded cooperation operates by sending the systematic and the first parity outputs via L?+?1 independent fading paths. Instead of using only a centralized turbo code system at the source node, we have proposed a DCC scheme, where the first recursive systematic coding is done at both source and relay nodes. At the destination, the received replicas are combined using the maximal ratio combining (MRC). The entire codeword, comprising the MRC sequence and the second parity part, is decoded via the maximum a-posteriori (MAP) algorithm and turbo decoding principle. We analyze the proposed scheme in terms of bit error rate (BER). In fact, we define the explicit upper bounds for error rate assuming Binary phase shift keying (BPSK) transmission for fully interleaved channels with channel state information (CSI). We use the Rayleigh fading channels with independent fading. Our study shows that the full diversity order is achieved when the source-relay link is more reliable than the other links. Otherwise, the diversity decreases. However, in both cases, it is shown that significant performance improvements are possible to achieve over non-cooperative coded systems. Theorical and simulation results are presented to demonstrate the efficacy of the proposed scheme.     

Molecular basis of the anti-diabetic properties of camel milk through profiling of its bioactive peptides on dipeptidyl peptidase IV (DPP-IV) and insulin receptor activity

The molecular basis of the anti-diabetic properties of camel milk reported in many studies and the exact active agent are still elusive. Recent studies have reported effects of camel whey proteins (CWP) and their hydrolysates (CWPH) on the activities of dipeptidyl peptidase IV (DPP-IV) and the human insulin receptor (hIR). In this study, CWPH were generated, screened for DPP-IV binding in silico and inhibitory activity in vitro, and processed for peptide identification. Furthermore, pharmacological action of intact CWP and their selected hydrolysates on hIR activity and signaling and on glucose uptake were investigated in cell lines. Results showed inhibition of DPP-IV by CWP and CWPH and their positive action on hIR activation and glucose uptake. Interestingly, the combination of CWP or CWPH with insulin revealed a positive allosteric modulation of hIR that was drastically reduced by the competitive hIR antagonist. Our data reveal for the first time the profiling and pharmacological actions of CWP and their derived peptides fractions on hIR and their pathways involved in glucose homeostasis. This sheds more light on the anti-diabetic properties of camel milk by providing the molecular basis for the potential use of camel milk in the management of diabetes.     

Surface plasmon resonance-based biosensors: From the development of different SPR structures to novel surface functionalization strategies

Surface plasmon resonance (SPR)-based biosensors are very powerful tools for the study of biomolecular interactions, chemical detection and immunoassays. This paper reviews the performance of various SPR structures and detection schemes focusing on propagating surface plasmons generated in planar structures. Some aspects of their surface functionalization, the key element which imparts biofunctionality to these structures and hence transforming them into biosensors, will also be discussed accordingly. The ultimate performance of SPR-based biosensors will thus be determined by both their inherent optical performance and suitable surface functionalization.     

Towards the Development of Realistic DoS Dataset for Intelligent Transportation Systems

Wireless Personal Communications - Vehicular ad-hoc networks (VANETs) present security vulnerabilities, which make them prone to diverse cyberattacks. Denial of Service (DoS) is one of the most...     

Real-time scene background initialization based on spatio-temporal neighborhood exploration

In this paper, we address the problem of scene background initialization to define a background model free from foreground objects. The complexity of this task resides in the continuous clutter of the scene by moving and stationary objects. To face this challenge, we propose a robust real-time iterative model completion method based on online block-level processing to initialize the background with low computational cost. First, temporal data analysis is conducted to cluster similar blocks. Meanwhile, a two-folded inter-block spatial neighborhood exploration is performed. It aims to capture relationships among neighboring clusters and reduce the number of candidate clusters employed in the next phase. Then, a smoothness analysis between neighboring locations is performed to iteratively reconstruct the background based on a newly proposed edge matching metric and an inter-block color discontinuity. Extensive evaluations of the proposed approach on the public Scene Background Initialization 2015 dataset and on the Scene Background Modeling Contest 2016 dataset revealed a performance superior or comparable to state-of-the-art methods.

     

Adaptive control of bilateral teleoperation system with compensatory neural-fuzzy controllers

This paper presents two adaptive neural-fuzzy controllers equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. To the first controller is applied compensatory neural-fuzzy inference (CNFI) and to the second compensatory adaptive neural fuzzy inference system (CANFIS). Each controller is incorporated into a two channel bilateral teleoperation architecture involving force-position scheme, which combines the position control of the slave system with force reflection on the master. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a one degree of freedom teleoperation system actuated by DC motors. The experimental results obtained show a fairly high accuracy in terms of position and force tracking, under free space motion and hard contact motion, what highlights the effectiveness of the proposed controllers.

     

On the utility of MIMO multi-relay networks for modulation identification over spatially-correlated channels

The necessity to perfectly monitor the intercepted signals for spatially-correlated multiple-input multiple-output (MIMO) systems, involves modulation identification algorithms. In this paper, we present an algorithm dedicated to the modulation identification for correlated MIMO relaying broadcast channels with direct link using multi-relay nodes. By modeling spatially-correlated MIMO channels as Kronecker-structured and the imperfect channel state information of both the source-to-destination and the relay-to-destination errors as independent complex Gaussian random variables, we firstly derive the ergodic capacity of the proposed transmission system. It turns out that the ergodic capacities improve with the number of relay nodes. Based on a pattern recognition approach using the higher order statistics features and the Bagging classifier, we show that the probability to distinguish among M-ary shift keying linear modulation types without any priori modulation information is enhanced compared to the decision tree (J48), the tree augmented naive Bayes, the naive Bayes using discretization and the multilayer perceptron classifiers. We also study the effect of increasing the number of relay nodes. Numerical simulations show that the proposed algorithm using the cooperation of multi-relay nodes with the source node can avoid the performance deterioration in modulation identification caused by both spatial correlation and imperfect CSI.