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.

     

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.     

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).     

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.     

Radiation pattern design of photonic crystal LED optimized by using multi-objective grey wolf optimizer

Photonic Network Communications - This paper proposes an effective method for shaping the radiation pattern intensity of photonic crystal (PhC) light-emitting diode (LED). In this method, the...     

An improved model predictive control of low voltage ride through in a permanent magnet synchronous generator in wind turbine systems

This research investigates a wind energy conversion system based on a permanent magnet synchronous generator (PMSG). In addition, a model predictive control (MPC) is proposed for the PMSG in normal and fault conditions. The most efficient mode of the control algorithm is found for maximum power point tracking in normal conditions and fast dynamic response in fault conditions following the selection of the optimum voltage vector. This method prevents a sudden increase in the DC‐link voltage by storing the active power in the generator rotor inertia. Moreover, during the low voltage, the grid code adoption of the reactive current is injected into the grid side. The performance of the proposed control scheme is evaluated for a wind power generator using MATLAB software. The simulation results demonstrate that the proposed method can safeguard the DC‐link during the fault.     

Saccharomyces cerevisiae for the biosorption of basic dyes from binary component systems and the high order derivative spectrophotometric method for simultaneous analysis of Brilliant green and Methylene blue

In this work, biosorption of Brilliant green (BG) and Methylene blue (MB) dyes in binary mixture onto Saccharomyces cerevisiae were studied. pH at which the biosorption capacity of biomass is maximum was found to be 6 which is close to the pH of natural aqueous solutions. This is a big advantage of S. cerevisiae which makes it applicable for the technology of dye removal from natural aqueous dye solutions. Note that the time for the applied biosorption process for the dye removal is considerably short (about 5 min) which is a big improvement for the adsorption processes. This proves that the S. cerevisiae is a promising adsorbent. The BG and MB dyes were simultaneously analyzed using the fifth and fourth order derivative spectrophotometric method, respectively. Several isotherm models were applied to experimental data and the isotherm constants were calculated for BG and MB dyes. Among the applied models, Freundlich isotherm model showed best fit to the biosorption equilibrium data.