A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

The Peer to Peer-Cloud (P2P-Cloud) is a suitable alternative to distributed cloud-based or peer-to-peer (P2P)-based content on a large scale. The P2P-Cloud is used in many applications such as IPTV, Video-On-Demand, and so on. In the P2P-Cloud network, overload is a common problem during overcrowds. If a node receives many requests simultaneously, the node may not be able to respond quickly to user requests, and this access latency in P2P-Cloud networks is a major problem for their users. The replication method in P2P-Cloud environments reduces the time to access and uses network bandwidth by making multiple data copies in diverse locations. The replication improves access to the information and increases the reliability of the system. The data replication's main problem is identifying the best possible placement of replica data nodes based on user requests for data access time and an NP-hard optimization problem. This paper proposes a new replica replacement to improve average access time and replica cost using fuzzy logic and Ant Colony Optimization algorithm. Ants can find the shortest path to discover the optimal node to place the duplicate file with the least access time latency. The fuzzy module evaluates the historical information of each node to analyze the pheromone value per iteration. The fuzzy membership function is also used to determine each node's degree based on the four characteristics. The simulation results showed that the access time and replica cost are improved compared to other replica replacement algorithms.

     

Dam break flood wave under different reservoir’s capacities and lengths

Dam failure has been the subject of many hydraulic engineering studies due to its complicated physics with many uncertainties involved and the potential to cause many losses of lives and economical losses. A primary source of uncertainties in many dam failure analyses refers to prediction of the reservoir’s outflow hydrograph, which is studied in the present investigation. This paper presents an experimental study on instantaneous dam failure flood under different reservoir’s capacities and lengths in which the side slopes change within a range of 30°–90°. Thus, several outflow hydrographs are calculated and compared. The results reveal the role of the side slopes on dam break flood wave, such that lower side slope creates more catastrophic outflow. The reservoir capacity and length are also recognized to be important factors, such that they do affect peak discharge and time to peak of the outflow hydrograph. Finally, the paper presents two simple relations for peak discharge and maximum water level estimation at any downstream location.     

INFLUENCE OF VIBRATION ON SORET-DRIVEN CONVECTION IN POROUS MEDIA

Two-dimensional thermosolutal natural convection with Soret effect under the simultaneous action of vibrational and gravitational accelerations is investigated. We consider a porous cavity saturated by a binary mixture and adopt the time-averaging formulation. For an infinite horizontal layer, a stability analysis is performed from which the threshold of stability is determined. Numerical simulations, using a pseudo-spectral Chebyshev collocation method, are performed to describe the convective motion. The problem is investigated for different aspect ratios with various directions of vibration. It is concluded that, for both the stationary and the Hopf bifurcation, the vertical vibration has a stabilizing effect while the horizontal vibration has a destabilizing effect on the onset of convection.     

Mechanical and thermal characterization of compression moulded polylactic acid natural fiber composites reinforced with hemp and lyocell fibers

This research evaluates the effects of PLA/PP blend ratio and Lyocell/hemp mixture ratio on the morphology, water absorption, mechanical and thermal properties of PLA‐based composites. The composites were fabricated with 30 mass % hemp using compression moulding. As a reference composites made from PP were also studied. Combining of hemp and Lyocell in PLA composite leads to the reduction of moisture absorption and can improve the impact, tensile, flexural properties when compared with PLA/hemp. Composite based on the PLA/PP blend‐matrix could not improve the tensile and flexural properties compared with PLA/hemp, however; the lighter composite with better impact properties was obtained. The crystallization temperature of the PLA‐PP/hemp increased compared with pure PLA. This result was also confirmed by the SEM micrographs. The moisture absorption of PLA‐PP/hemp was higher than PLA/hemp. Based on theoretical analysis of DMTA data, there was favorable adhesion in all composites. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40534.     

Nutrition and sarcopenia: A review of the evidence of nutritional influences

Prevention of age related decline in muscle mass and strength is a key strategy to keeping physical capacity in older age and allowing independent living. To emerge preventive strategies, a better understanding is required of life style factors that impacts on sarcopenia. However, since muscle mass and strength in later life depend on both the rate of muscle loss and the peak achieved in early life, attempts to prevent sarcopenia also require considering diet through the life course and the potential benefits of early interventions. Optimizing diet and nutrition status during the life may be an important strategy to preventing sarcopenia and enhancing physical ability in older age.     

Modeling the oxidative coupling of methane using artificial neural network and optimizing of its operational conditions using genetic algorithm

The effect of some operating conditions such as temperature, gas hourly space velocity (GHSV), CH₄/O₂ ratio and diluents gas (mol% N₂) on ethylene production by oxidative coupling of methane (OCM) in a fixed bed reactor at atmospheric pressure was studied over Mn/Na₂WO₄/SiO₂ catalyst. Based on the properties of neural networks, an artificial neural network was used for model developing from experimental data. To prevent network complexity and effective data input to the network, principal component analysis method was used and the number of output parameters was reduced from 4 to 2. A feed-forward back-propagation network was used for simulating the relations between process operating conditions and those aspects of catalytic performance including conversion of methane, C₂ products selectivity, C2 yielding and C₂H₄/C₂H₆ ratio. Levenberg-Marquardt method is presented to train the network. For the first output, an optimum network with 4-9-1 topology and for the second output, an optimum network with 4-6-1 topology was prepared. After simulating the process as well as using ANNs, the operating conditions were optimized and a genetic algorithm based on maximum yield of C₂ was used. The average error in comparing the experimental and simulated values for methane conversion, C₂ products selectivity, yield of C₂ and C₂H₄/C₂H₆ ratio, was estimated as 2.73%, 10.66%, 5.48% and 10.28%, respectively.     

Synthesis of TiO2-polymer nanocomposite in supercritical CO2 via RAFT polymerization

Polymer chains of PMMA were grown from nano titania (n-TiO₂) spherical surfaces by the Reversible Addition Fragmentation Chain Transfer Polymerization process (RAFT) using the green solvent, supercritical carbon dioxide (scCO₂). The RAFT agent (1), 4-cyano-4-(dodecylsulfanylthiocarbonylsulfanyl)pentanoic acid, with an available carboxyl group was first coordinated to the n-TiO₂ surface, with the SC(SC₁₂H₂₅) moiety subsequently used for RAFT polymerization of MMA to form the n-TiO₂/PMMA nanocomposites. The livingness of polymerization was verified using GPC, while the morphology of the nanocomposites was studied using thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and dynamic light scattering (DLS). The rate of polymerization and molecular weights at different pressures in scCO₂ and in non-pressurized and pressurized organic solvent (THF) were compared, showing that increased CO₂ pressure provided a higher rate of polymerization and longer chain lengths indicating the utility of this approach.     

Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

Accurate short-term load forecasting (STLF) is one of the essential requirements for power systems. In this paper, two different seasonal artificial neural networks (ANNs) are designed and compared in terms of model complexity, robustness, and forecasting accuracy. Furthermore, the performance of ANN partitioning is evaluated. The first model is a daily forecasting model which is used for forecasting hourly load of the next day. The second model is composed of 24 sub-networks which are used for forecasting hourly load of the next day. In fact, the second model is partitioning of the first model. Time, temperature, and historical loads are taken as inputs for ANN models. The neural network models are based on feed-forward back propagation which are trained and tested using data from electricity market of Iran during 2003 to 2005. Results show a good correlation between actual data and ANN outcomes. Moreover, it is shown that the first designed model consisting of single ANN is more appropriate than the second model consisting of 24 distinct ANNs. Finally ANN results are compared to conventional regression models. It is observed that in most cases ANN models are superior to regression models in terms of mean absolute percentage error (MAPE).