A general boundary-integral formulation for zoned three-dimensional media

A new boundary-integral formulation is proposed to analyse the heat transfer in zoned three-dimensional geometries. The proposed formulation couples the boundary formula, the gradient of the boundary formula and the exterior formula. An advantage of this method over the traditional methods is that any linear condition at the interface between subdomains may be incorporated into the formulation at the outset. In addition, the new method provides a sparse and well-conditioned matrix of coefficients with a minimum number of equations.     

Aqueous preparation and evaluation of albumin-chitosan microspheres containing indomethacin

Controlled-release egg albumin-chitosan microspheres containing indomethacin as a model drug were successfully prepared by coacervation method. The proposed method can offer a simple method for microsphere preparation in an aqueous system with the elimination of the use of organic solvents that are usually needed in conventional techniques of microencapsulation. The interaction between negatively charged egg albumin molecules in phosphate buffer, pH 7.2, or sodium hydroxide solution and positively charged chitosan molecules dissolved in diluted acetic acid to form an insoluble precipitate was the principle for the formation of the microspheres. The effects of many process variables, such as amount of formaldehyde as a cross-linking agent, stirring time, final pH of encapsulation medium, initial drug loading, and albumin concentration or albumin-to-chitosan weight ratio, on the properties of the prepared microspheres were investigated. Incorporation efficiencies of the microspheres to the drug were high in most cases and ranged between 63.3 ± 3.6% and 92.39 ± 3.2%, while particle sizes were 435.2 ± 12.6 up to 693.9 ± 34.6 µm for the different tested batches. On the other hand, the values of angles of repose and compressibility indices were in the range of 23.5 ± 0.4 to 32.0 ± 0.7 degrees and 11.1 ± 0.7% to 23.6 ± 0.7% respectively, which indicate overall good free flowing nature of the microspheres of all batches. The maximum required amount of the cross-linking agent was determined to avoid excessive unnecessary chemicals. It was also noticed that excessive time of stirring and excessive initial drug loading are not recommended as it may lead to microspheres of low properties. The pH of the encapsulation media (pH 3.77 up to pH 4.91) significantly affected the properties of the microspheres. As the pH of the encapsulation media was increased, the incorporation efficiency, particle size, and flowability decreased, along with increase of drug release rate, which could be related to incomplete cross linking of the microspheres matrix. It was also observed that high concentration of albumin solution and accordingly the increase of albumin-to-chitosan weight ratio were accompanied with increases in incorporation efficiency and particle size with improved microsphere flowability and slow indomethacin release. Thus, the proposed microspheres showed the ability to control the release of indomethacin, and their properties were highly affected by many process variables that could be controlled to obtain an optimized system.     

Study on novel poly (vinyl pyrrolidone) doped MWCNTs/polyrhodanine nanocomposites: Synthesis,characterization, and thermal performance

In this paper, poly (vinyl pyrrolidone) (PVP) doped multiwall carbon nanotubes (MWCNTs/polyrhodanine), were synthesized through one-step chemical oxidative polymerization of rhodanine monomers on the surface-modified carbon nanotubes. Characterization of MWCNTs/polyrhodanine was conducted by using scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transforms infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and EDX spectrum analyses in which the results confirmed the successful formation of MWCNTs/polyrhodanine. In addition, to investigate the thermal properties of samples, thermogravimetric analysis (TGA) was employed, and results exhibited significant improvement in the nanocomposite thermal stability due to the addition of MWCNTs with reinforcement effect in polymer matrix.     

A NEW HYBRID EVOLUTIONARY OPTIMIZATION ALGORITHM FOR DISTRIBUTION FEEDER RECONFIGURATION

This article extends a hybrid evolutionary algorithm to cope with the feeder reconfiguration problem in distribution networks. The proposed method combines the Self-Adaptive Modified Particle Swarm Optimization (SAMPSO) with Modified Shuffled Frog Leaping Algorithm (MSFLA) to proceed toward the global solution. As with other population-based algorithms, PSO has parameters which should be tuned to have a suitable performance. Thus, a self-adaptive framework is proposed to adjust the parameters dynamically. In SAMPSO, the PSO learning factors are considered to be the new control variables and are changed in the evolutionary process. To enhance the quality of the solutions, the SAMPSO is combined with MSFLA and a new hybrid algorithm is proposed to minimize the electrical energy losses of the distribution system by feeder reconfiguration. The effectiveness of the proposed method is demonstrated through two test systems.     

Improving inter-node communications in multi-core clusters using a contention-free process mapping algorithm

High performance clusters, which are established by connecting many computing nodes together, are known as one of main architectures to obtain extremely high performance. Currently, these systems are moving from multi-core architectures to many-core architectures to enhance their computational capabilities. This trend would eventually cause network interfaces to be a performance bottleneck because these interfaces are few in number and cannot handle multiple network requests at a time. The consequence of such issue would be higher waiting time at the network interface queue and lower performance. In this paper, we tackle this problem by introducing a process mapping algorithm, which attempts to improve inter-node communications in multi-core clusters. Our mapping strategy reduces accesses to the network interface by distributing communication-intensive processes among computing nodes, which leads to lower waiting time at the network interface queue. Performance results for synthetic and real workloads reveal that the proposed strategy improves the performance from 8 % up to 90 % in tested cases compared to other methods.     

LAMR: learning automata based multicast routing protocol for multi-channel multi-radio wireless mesh networks

Multicast routing is a crucial issue in wireless networks in which the same content should be delivered to a group of recipients simultaneously. Multicast is also considered as a key service for audio and video applications as well as data dissemination protocols over the last-mile backhaul Internet connectivity provided by multi-channel multi-radio wireless mesh networks (MCMR WMNs). The multicast problem is essentially related to a channel assignment strategy which determines the most suitable channel-radio associations. However, channel assignment brings about its own complications and hence, solving the multicast problem in MCMR WMNs will be more complicated than that of traditional networks. This problem has been proved to be NP-hard. In the major prior art multicast protocols developed for these networks, channel assignment and multicast routing are considered as two separate sub-problems to be solved sequentially. The work in this article is targeted at promoting the adoption of learning automata for joint channel assignment and multicast routing problem in MCMR WMNs. In the proposed scheme named LAMR, contrary to the existing methods, these two sub-problems will be solved conjointly. Experimental results demonstrate that LAMR outperforms the LCA and MCM proposed by Zeng et al. (IEEE Trans. Parallel. Distrib. Syst. 21(1):86–99, 2010) as well as the genetic algorithm-, tabu search-, and simulated annealing-based methods by Cheng and Yang (Int. J. Appl. Soft Comput. 11(2):1953–1964, 2011) in terms of achieved throughput, end-to-end delay, average packet delivery ratio, and multicast tree total cost.     

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.     

Optimal Design of Concrete Canal Section for Minimizing Costs of Water Loss,Lining and Earthworks

Canal section design with minimum cost, which can be considered as an objective function, involves minimization of total costs per unit length of the canal, including direct costs of per cubic meter earthworks and per meter canal lining and indirect costs of water losses through canal seepage and evaporation. Since the costs (both direct and indirect) are associated with the canal geometry and dimensions, it is possible to lower them by optimization of the mentioned objective function. For this purpose, some constraints were subjected and considered to solve the problem. Flow discharge, as the main constraint, was considered in addition to the minimum permissible velocity and Froude’s number, as subsidiary constraints. MATLAB programming software was used to demonstrate and run the optimization algorithm. The results finally were illustrated in forms of dimensionless graphs, which simplify the optimum design of canal dimensions with minimum cost per meter length. Comparing the results with other similar studies, however show the importance and role of earthworks and lining costs, as well as including the subsidiary constraints in the optimization process.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.