Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

It is estimated that 70% or more of broadband bandwidth is consumed by transmitting music, games, video and other content through Peer-to-Peer (P2P) clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a network processors (NPs) based online hybrid traffic classifier is proposed. The designed hardware classifier is able to classify P2P traffic based on the static characteristic namely on line speed, and the Flexible Neural Tree(FNT) based software classifier helps learning and selecting P2P traffic attributes from the statistical characteristics of the P2P traffic. Experiment results illustrate that the hybrid classifier performs well for online classification of P2P traffic from gigabit network. The proposed framework also depicts good expansion capabilities to add new P2P features and to adapt to new P2P applications online.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

Low-intensity optical bistability in an active Fabry-Perot mirror induced by input-phase-insensitive parametric downconversion

A Fabry-Perot resonator filled with second-order nonlinear optical material is investigated. Attention is devoted to making the resonator act as a nonlinear mirror for ultralow-intensity light signals that can be switched by a control beam at the second-harmonic frequency. The interaction process is an input-phase-independent parametric downconversion. The theoretical problem is solved through implementation of a dummy variable method optimized for a parametric process. Efficient amplification and bistability of low-intensity signals have been found.     

Designing resilient networks using multicriteria metaheuristics

The paper deals with the design of resilient networks that are fault tolerant against link failures. Usually, fault tolerance is achieved by providing backup paths, which are used in case of an edge failure on a primary path. We consider this task as a multiobjective optimization problem: to provide resilience in networks while minimizing the cost subject to capacity constraint. We propose a stochastic approach, which can generate multiple Pareto solutions in a single run. The feasibility of the proposed method is illustrated by considering several network design problems using a single weighted average of objectives and a direct multiobjective optimization approach using the Pareto dominance concept.     

The Effect of Electroporation of a Lyotroic Liquid Crystal Genistein-Based Formulation in the Recovery of Murine Melanoma Lesions

A lamellar lyotropic liquid crystal genistein-based formulation (LLC-Gen) was prepared in order to increase the aqueous solubility of the lipophilic phytocompound genistein. The formulation was applied locally, in a murine model of melanoma, with or without electroporation. The results demonstrated that, when the formulation was applied by electroporation, the tumors appeared later. During the 21 days of the experiment, the LLC-Gen formulation decreased the tumor volume, the amount of melanin and the degree of erythema, but when electroporation was applied, all these parameters indicated a better prognosis even (lower tumor volume, amount of melanin and degree of erythema). Although hematoxylin–eosin (HE) staining confirmed the above events, application of the LLC-Gen formulation by electroporation did not lead to a significant effect in terms of the serum concentrations of the protein S100B and serum neuron specific enolase (NSE), or the tissue expression of the platelet-derived growth factor receptor β (PDGFRβ) antibody.     

Application of a New Method for Radiative Heat Transfer to Flat Glass Tempering

Temperature distributions within semi-transparent materials like glass strongly determine their behavior. It is fundamental to take radiation into account to determine exact temperature distributions. The Rosseland Approximation for radiation is usually an appropriate method, but is only valid with an optically thick glass. In the present paper, we propose an improved approximation that is both efficient and sufficiently accurate even for the semi-transparent region. This new radiation model is used to determine the temperature along the flat glass thickness during tempering. The impact of temperature evaluation on residual stresses is discussed.     

Making Asset Investment Decisions for Wastewater Systems That Include Sustainability

Effective integrated water management is a key component of the World Water Vision and the way in which aspirations for water equity may be realized. Part of the vision includes the promotion of sustainability of water systems and full accountability for their interaction with other urban systems. One major problem is that “sustainability” remains an elusive concept, although those involved with the provision of urban wastewater systems now recognize that decisions involving asset investment should use the “triple bottom line” approach to society, the economy, and the environment. The Sustainable Water Industry Asset Resource Decisions project has devised a flexible and adaptable framework of decision support processes that can be used to include the principles of sustainability more effectively. Decision mapping conducted at the outset of the project has shown that only a narrow range of criteria currently influence the outcome of asset investment decisions. This paper addresses the concepts of sustainability assessment and presents two case studies that illustrate how multicriteria decision support systems can enhance the assessment of the relative sustainability of a range of options when decisions are being made about wastewater asset investment.     

Can simplified design methods for domestic rainwater harvesting systems produce realistic water‐saving and financial predictions?

Rainwater harvesting systems are a recognised approach for reducing reliance on potable mains supply. Many domestic systems are designed and assessed using simplified approaches that can be applied with little prior information. Oversimplification of the physical processes involved and ad hoc consideration of associated costs means that the results produced by these simplified methods are of questionable accuracy. This paper looks at three popular simplified approaches and compares the predicted water‐saving and financial performance with that of a more detailed simulation model, which includes explicit consideration of the physical processes and associated system costs. The simplified approaches predict significantly higher water and financial savings compared with the detailed model. Consequently it is recommended that simplified approaches should not be relied upon and greater preference should be given to the use of more detailed simulations that explicitly consider the important physical processes and include a thorough assessment of costs.