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.

     

A fast and noise resilient cluster-based anomaly detection

Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods.     

A novel image encryption/decryption scheme based on chaotic neural networks

This paper presents a novel image encryption/decryption algorithm based on chaotic neural network (CNN). The employed CNN is comprised of two 3-neuron layers called chaotic neuron layer (CNL) and permutation neuron layer (PNL). The values of three RGB (Red, Green and Blue) color components of image constitute inputs of the CNN and three encoded streams are the network outputs. CNL is a chaotic layer where, three well-known chaotic systems i.e. Chua, Lorenz and Lü systems participate in generating weights and biases matrices of this layer corresponding to each pixel RGB features. Besides, a chaotic tent map is employed as the activation function of this layer, and makes the relationship between the plain image and cipher image nonlinear. The output of CNL, i.e. the diffused information, is the input of PNL, where three-dimensional permutation is applied to the diffused information. The overall process is repeated several times to make the encryption process more robust and complex. A 160-bit-long authentication code has been used to generate the initial conditions and the parameters of the CNL and PNL. Some security analysis are given to demonstrate that the key space of the new algorithm is large enough to make brute-force attacks infeasible and simulations have been carried out with detailed numerical analysis, demonstrating the high security of the new image encryption scheme.     

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.     

Material Behaviours in 3D-Printed Fashion Items

New advances in material technologies and 3D printing are giving a whole new definition to the notion of tailoring. Recent projects by Guest-Editor Behnaz Farahi have developed items of apparel that are not only a perfect fi t for the wearer, but also interact with his or her surroundings with dynamic qualities. Here she describes their production and their unique functionalities – from bending or bristling in reaction to the onlooker's gaze or the wearer's brain activity, to producing light patterns in response to the body's movement.     

Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

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.     

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.     

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).