On the necessary and sufficient requirement of a CIOQ switch to emulate an Output Queued switch

There has been much interest to emulate the behavior of Output Queued switches. The early result of such attempts was reported by Prabhakar and McKeown using the CIOQ switches with speedup factor of 4. Subsequently, Stoica and Zhang and independently Chuang et al. showed that a speedup of 2 in conjunction with their scheduling schemes would be sufficient for CIOQ switches to emulate Output Queued switches.Additionally, Chuang et al. showed that in “Average Sense” a speedup of 2?1/N is necessary and sufficient for CIOQ to emulate Output Queued switch behavior.Our paper reports that in the “Strict Sense” a speedup of 2 is both necessary and sufficient. We show this requirement using examples for 2x2 and 3x3 switches. Then, with a constructed traffic pattern, it is proved that in the “Strict Sense” a speedup of 2 is necessary to emulate the behavior of an Output Queued switch for any switch size N.Combining this result with the previous scheduling schemes, we conclude that in the “Strict Sense”, a speedup of 2 is the necessary and sufficient condition to emulate the behavior of an Output Queued switch, using a CIOQ switch.Additionally, easing the assumptions and allowing the packet segmentation, it is shown that the speedup requirement to emulate the behavior of an Output Queued switch can be reduced to values even smaller than 2?1/N. For this case a lower bound of 3/2 and an upper bound of 2 is proved.     

The food preservative potential of essential oils: is lemongrass the answer?

Several essential oils of medicinal plants possess proven antimicrobial activity and are suitable for applications on the food industry. The oil from Cymbopogon citratus (lemongrass) has been reported to have a wide range of biological activities. However, there are few controlled studies confirming its antimicrobial activity. The aim of our study was to assess the antimicrobial potential of lemongrass oil against a wide spectrum of food spoilage and pathogenic bacteria and yeast in liquid and vapour phase. The chemical composition of the oil was analyzed by gas chromatography; 14 components were identified and geranial (28.93 %), neral (24.30 %) and myrcene (23.92 %) were the most abundant constituents. Lemongrass oil showed potent antimicrobial activity against Gram positive bacteria and exhibited the strongest antifungal effect against Candida albicans and C. parapsilosis. Minimum inhibitory concentration varied from 0.019 to 1.25 mg mL^?1 for Gram positive bacteria and yeasts, indicating Staphylococcus aureus, S. epidermidis and C. albicans as the most susceptible strains. Moreover, the diameter of inhibition zone (DIZ) increased with increasing essential oil (EO) volume. Considerably superior antimicrobial activity was observed in the vapour phase. The DIZ resulting from the exposure to EO vapour was significantly larger than that from the same volume in the liquid phase. The DIZ varied from 22 to 90 mm for Candida strains. There is growing evidence that EO in vapour phase are effective antimicrobial systems and that they do have advantages over the use of oil in liquid phase. Our results imply that lemongrass oil could be useful for the development of novel types of natural preservatives for food control.     

Catalytic upgrading of 4-methylaniosle as a representative of lignin-derived pyrolysis bio-oil: Process evaluation and optimization via coupled application of design of experiment and artificial neural networks

Catalytic upgrading of 4-methylanisole as a representative of lignin-derived pyrolysis bio-oil was investigated over Pt/γ-Al₂O₃ catalyst. The catalytic upgrading process was conducted at different operating condition to determine the detailed reactions network. Additionally, artificial neural network and design of experiment were applied by feeding the reaction temperature, operating pressure and space velocity to predict 4-methylanisole conversion, main products selectivity, reactions rate and reactions network. The main products of 4-methylanisole upgrading were toluene, phenol derivatives, cyclohexanone, 4-methylcyclohexanone, and 2-tert-butyl-4-methylphenol. The major classes of reactions during the upgrading process were hydrogenolysis, hydrodeoxygenation, alkylation, and hydrogenation. For optimization of experimental data obtained at suggested conditions by design of experiment, the response surface methodology was applied. Artificial neural network model was used to investigate the kinetics behavior of the system due to the complex nature of system. A combination of the response surface methodology, artificial neural network, and design of experiment has revealed its ability to solve a quadratic polynomial model. The coefficients of determination were close to 1, and the mean square error of the artificial neural network model was close to 0 which showed the high accuracy of model predictions. It was inferred that during the upgrading process of 4-methylanisole, increasing temperature and pressure and setting space velocity at the minimum value are the reasons to come close to the optimum reaction rate. The comparison of experimental results with simulated data from the artificial neural network and the response surface methodology models illustrated that the developed model can create an applicable situation for practical design of large-scale production of valuable fuels from renewable resources.     

A method providing bidirectional control of coil delivery in occlusions of patent ductus arteriosus with shallow ampulla and Pott's shunts

A novel method using a snare and bioptome to provide bidirectional control of a Gianturco coil for occlusion of a patent ductus arteriosus with a shallow ampulla and Pott's shunts is presented. This method greatly reduces the risk of coil embolization and optimizes coil position in difficult cases.     

Mie and Rayleigh modeling of visible-light scattering in neonatal skin

Reduced-scattering coefficients of neonatal skin were deduced in the 450-750-nm range from integrating-sphere measurements of the total reflection and total transmission of 22 skin samples. The reduced-scattering coefficients increased linearly at each wavelength with gestational maturity. The distribution of diameters d and concentration ρ(A) of the skin-sample collagen fibers were measured in histological sections of nine neonatal skin samples of varying gestational ages. An algorithm that calculates Mie scattering by cylinders was used to model the scattering by the collagen fibers in the skin. The fraction of the reduced-scattering coefficient μ(s)' that was attributable to Mie scattering by collagen fibers, as deduced from wavelength-dependent analysis, increased with gestational age and approached that found for adult skin. An assignment of 1.017 for n(rel), the refractive index of the collagen fibers relative to that of the surrounding medium, allowed the values for Mie scattering by collagen fibers, as predicted by the model for each of the nine neonatal skin samples to match the values for Mie scattering by collagen fibers as expected from the measurements of μ(s)'. The Mie-scattering model predicted an increase in scattering with gestational age on the basis of changes in the collagen-fiber diameters, and this increase was proportional to that measured with the integrating-sphere method.     

Instances selection algorithm by ensemble margin

Abstract

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.     

Self‐Limiting Galvanic Growth of MnO2 Monolayers on a Liquid Metal—Applied to Photocatalysis

Liquid metals offer unprecedented chemistry. Here it is shown that they can facilitate self‐limiting oxidation processes on their surfaces, which enables the growth of metal oxides that are atomically thin. This claim is exemplified by creating atomically thin hydrated MnO₂ using a Galvanic replacement reaction between permanganate ions and a liquid gallium–indium alloy (EGaIn). The “liquid solution”–“liquid metal” process leads to the reduction of the permanganate ions, resulting in the formation of the oxide monolayer at the interface. It is presented that under mechanical agitation liquid metal droplets are established, and simultaneously, hydrated gallium oxides and manganese oxide sheets delaminate themselves from the interfacial boundaries. The produced nanosheets encapsulate a metallic core, which is found to consist of solid indium only, with the full migration of gallium out of the droplets. This process produces core/shell structures, where the shells are made of stacked atomically thin nanosheets. The obtained core/shell structures are found to be an efficient photocatalyst for the degradation of an organic dye under simulated solar irradiation. This study presents a new research direction toward the modification and functionalization of liquid metals through spontaneous interfacial redox reactions, which has implications for many applications beyond photocatalysis.     

An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen’s Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

     

Structure-property Relationships in Radiation Grafted Poly(tetrafluoroethylene)-<Emphasis Type="Italic">graft</Emphasis>-polystyrene Sulfonic Acid Membranes

Structure-property relationships in poly(tetrafluoroethylene)-graft-polystyrene sulfonic acid (PTFE-g-PSSA) membranes prepared by radiation-induced grafting of styrene onto poly(tetrafluoroethylene) (PTFE) films using simultaneous radiation-induced grafting followed by sulfonation reaction were established. The physico-chemical properties of the membranes such as ion exchange capacity, swelling and ionic conductivity were correlated with the degree of grafting and the structural changes taking place in the membrane matrix during the preparation procedure. The variation in the crystallinity of membranes was studied by differential scanning calorimetry (DSC). The membranes thermal stability was evaluated using thermogravimetric analysis (TGA) and the effect of the heat treatment on the ion exchange capacity and the water uptake was investigated. The membranes were found to undergo substantial structural changes in the form of ionic sites increase, hydrophilicity enhancement, hydrophobicity reduction and crystallinity decrease with the increase in the degree of grafting. These structural changes were found to have a collective effect on the physico-chemical properties of the membranes but their relative contribution depends on the degree of grafting.