FTR: features tree based routing in mobile social networks

Mobile social networks are among subsets of delay tolerant networks. The nodes of these networks are mobile and the communication between them is done wireless and all nodes have social characteristics. The connection between these nodes is temporary and there is not end to end route between the source and the destination. Therefore, it is difficult to deliver the packets to the destination. One of the best routing methods in such networks is to use the information about the network context. These methods require the process of information collection and replicate the packets based on the context to increase the delivery ratio and enforce great overhead onto the network. Since the nodes have social characteristics and these features exist within the network, it seems that using them within the routing can be useful. In this paper a community based method of delivering is proposed that uses social characteristics of the individual members for routing the packets. Using the predetermined roles for the members that do not need data collection level can improve routing in these networks. In this method a tree is formed and each group of the members is entitled in one branch of it. The transmission of the packets from the source to the destination is done based on the differences between their characteristics and through the route which has the highest number of the nodes. The simulation results show that the delivery ratio of this method has increased regarding the related works and the overhead ratio has decreased.

     

Absorption of ethylene from ethylene‐ethane gaseous mixture by AgNO3 solution in a semi‐continuous process

Ethylene absorption from an ethylene–ethane gaseous mixture in the silver nitrate solution is studied at different temperatures and concentrations. Unlike the previous studies, in which the absorption was studied by batch processes, a semi‐continuous process is applied in the present research. The results show that increase in temperature reduces the amount of absorbed ethylene and absorption time. The amount of absorbed ethylene is increased in solutions with higher concentrations of AgNO₃, whereas the mole ratio of absorbed ethylene per silver nitrate is decreased by increasing the AgNO₃ concentration. Total absorption is modelled as a function of the temperature and concentration of absorbing solution. The estimated values from the model are in good agreement with the experimental data.     

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.     

Investigation of polyvinyl alcohol nanocomposite hydrogels containing chitosan nanoparticles as wound dressing

Polymeric hydrogels, water-swollen 3?D networks of the polymers, have found wide ranges of applications in the medical fields, such as wound care and wound dressing, in order to prevent infections. Prevention from microorganisms transfer in to the wounds is one of the ideal wound dressing duties of polyvinyl alcohol (PVA) hydrogels. In this study, at the start, under optimal conditions, nanoparticles of chitosan using ionotropic gelation method were synthesized and in the next step in order to achieve particles with a minimum size, they were evaluated by scanning electron microscope (SEM) and Fourier transform infrared spectroscopy (FTIR). Then after to obtain a wound dressing with preferable properties, nanocomposite hydrogels using a combination of PVA and 5, 10 and 15?wt% chitosan nanoparticles were prepared through freezing-thawing cycles. The necessary features of PVA nanocomposite hydrogels for wound dressing were investigated. The dispersion state of nanoparticles and structure of samples were evaluated by SEM microscopy. The nanoparticle size and the nanoparticle size distribution of chitosan was determined using the dynamic light scattering test at the nanometer scale. The physical behavior of hydrogels such as swelling and gel fraction was studied and their mechanical properties were investigated by compressive test. Finally the antimicrobial test and biocompatibility as cell viability were carried out. The results proved that the PVA nanocomposite hydrogels fulfill the requirements of a good wound dressing with desirable characteristics such as favorable swelling and acceptable strength, excellent barrier against microbial penetration.     

Multi-objective discrete urban road network design

This paper addresses the problem of designing urban road networks in a multi-objective decision making framework. Given a base network with only two-way links, and the candidate lane addition and link construction projects, the problem is to find the optimal combination of one-way and two-way links, the optimal selection of network capacity expansion projects, and the optimal lane allocations on two-way links to optimize the reserve capacity of the network, and two new travel time related performance measures. The problem is considered in two variations; in the first scenario, two-way links may have different numbers of lanes in each direction and in the second scenario, two-way links must have equal number of lanes in each direction. The proposed variations are formulated as mixed-integer programming problems with equilibrium constraints. A hybrid genetic algorithm, an evolutionary simulated annealing, and a hybrid artificial bee colony algorithm are proposed to solve these two new problems. A new measure is also proposed to evaluate the effectiveness of the three algorithms. Computational results for both problems are presented.     

Laboratory Investigation of Stilling Basin Slope Effect on Bed Scour at Downstream of Stepped Spillway: Physical Modeling of Javeh RCC Dam

Stepped spillway and stilling basin are one of the most important energy dissipation structures. Eventhough, most of energy dissipated by these structures, but in skimming flow, the upstream flow motion is nonaerated and the residual energy capable to destroyed structures during floods. In this study, effect of stilling basin slope on bed scour, downstream of Javeh dam was investigating. Experiments performed in hydraulic structures laboratory of the University of Kerman with six different discharges (5, 7, 13, 17, 25 and 30 l/s.m) and five various stilling basin slope (0.02, 0.01, 0, ?0.01 and???0.02). The parameters such as maximum scour depth (d_s), flow velocity (in three point), water depth on upstream and downstream of stepped spillway and stilling basin, the distance of the maximum scour depth to sill (L_s) and the gheometery of scour hole measured. Result shown that when stilling basin slopes was 0.02, the average of maximum relative scour depth, 47% Increased and in ?0.02, 52.2% Decreased. In addition, the distance of maximum scour depth until stilling basin increased by increasing and decreased by decreasing the stilling basin slope.

     

An investigation on the sensitivity analysis of the parameters of proposed wax precipitation model

Reservoir hydrocarbon fluids contain heavy paraffins that may form solid phase of wax at low temperatures. Formation of solid phases is highly unwanted in oil production assemblies, pipelines and in process equipments. A predictive technique is crucial to the solution of wax formation to alleviate this problem.The effect of different parameters to predict the conditions under which wax precipitation takes place using the proposed model of Sahand University of Technology and other models has been investigated. The proposed model uses regular solution theory to describe solid phase (wax) non-ideality and the liquid and gas phases are being described by an equation of state.In order to evaluate the reliability of the proposed model, wax appearance temperatures (WAT's) were calculated for several mixtures at different compositions and compared with different models. The proposed model predictions had very good agreement with experimental data over a wide range of compositional distributions in comparison with other models. Solid wax content was also calculated at different temperatures below WAT in several synthetic systems made up of a solvent (decane) and a paraffinic heavy fraction. The results of calculating the amount of wax precipitation showed very good agreement with experimental data. Effect of different parameters including fusion temperature (T^f), Enthalpy of fusion (Δh^f), solubility parameter (δ^S), and binary interaction parameters (BIP) in predicting the WAT and the amount of wax precipitated for different oil mixtures have been evaluated using the proposed model and compared with other models. The results showed that the T^f is the most sensitive parameter while δ^S shows the least sensitivity in matching the WAT. Even though using Δh^f could provide the same results as tuning T^f, but the required changes are much higher and sometimes not practical. Also using BIP as the tuning parameter, requires a fairly large coefficient that makes it unsuitable to be considered as the tuning parameter.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary Arithmetic Optimization Algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

     

Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

We propose “supervised principal component analysis (supervised PCA)”, a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. The proposed supervised PCA is solvable in closed-form, and has a dual formulation that significantly reduces the computational complexity of problems in which the number of predictors greatly exceeds the number of observations (such as DNA microarray experiments). Furthermore, we show how the algorithm can be kernelized, which makes it applicable to non-linear dimensionality reduction tasks. Experimental results on various visualization, classification and regression problems show significant improvement over other supervised approaches both in accuracy and computational efficiency.