The effects of moderately oxidised dietary oil with or without vitamin E supplementation on performance, nutrient digestibility, some blood traits, lipid peroxidation and antioxidant defence of male broilers

BACKGROUND: The experiment was conducted to determine the effects of dietary supplementation of oxidised oil with or without vitamin E on performance, nutrient digestibility, some blood traits, lipid peroxidation and antioxidant defence system of male broilers. RESULTS: The supplementation of oxidised oil with or without vitamin E to the grower diets did not significantly affect performance, the pH and viscosity values of excreta and nutrient digestibilities in male broilers. Oxidised oil supplementation slightly increased plasma triglyceride and cholesterol concentrations but did not alter plasma glucose concentration. Although malondialdehyde (MDA) and nitric oxide concentrations tended to be higher in the oxidised oil group, these increases were not significant. Birds fed the diet containing oxidised oil had significantly lower superoxide dismutase (SOD) activity. However, no differences were observed in glutathione peroxidase (GSH‐Px) activity and uric acid concentrations of broilers fed oxidised oil as compared to the control group. Dietary vitamin E supplementation decreased MDA concentration whereas increasing SOD activity, suggesting that vitamin E supplementation reduced susceptibility to lipid peroxidation. CONCLUSION: The results showed that a milder oxidative stress occurred by supplementation of moderately oxidised oil to the diet of broilers and vitamin E supplementation had been helpful in alleviating lipid peroxidation. Copyright © 2011 Society of Chemical Industry     

Superwetting nanowire membranes for selective absorption

The construction of nanoporous membranes is of great technological importance for various applications, including catalyst supports, filters for biomolecule purification, environmental remediation and seawater desalination. A major challenge is the scalable fabrication of membranes with the desirable combination of good thermal stability, high selectivity and excellent recyclability. Here we present a self-assembly method for constructing thermally stable, free-standing nanowire membranes that exhibit controlled wetting behaviour ranging from superhydrophilic to superhydrophobic. These membranes can selectively absorb oils up to 20 times the material's weight in preference to water, through a combination of superhydrophobicity and capillary action. Moreover, the nanowires that form the membrane structure can be re-suspended in solutions and subsequently re-form the original paper-like morphology over many cycles. Our results suggest an innovative material that should find practical applications in the removal of organics, particularly in the field of oil spill cleanup.     

Application of supramolecular nanostamping to the replication of DNA nanoarrays

The rapid development of molecular biology is creating a pressing need for arrays of biomolecules that are able to detect smaller and smaller volumes of analytes. This goal can be achieved by shrinking the average size and spacing of the arrays' constituent features. While bioarrays with dot size and spacing on the nanometer scale have been successfully fabricated via scanning probe microscopy-based techniques, such fabrication methods are serial in nature and consequently slow and expensive. Additionally, the development of truly small arrays able to analyze scarce volumes of liquids is hindered by the present use of optical detection, which sets the minimum dot spacing on the order of roughly half the excitation wavelength. Here, we show that supramolecular nanostamping, a recently introduced truly parallel method for the stamping of DNA features, can efficiently reproduce DNA arrays with features as small as 14 +/- 2 nm spaced 77 +/- 10 nm. Moreover, we demonstrate that hybridization of these nanoarrays can be detected using atomic force microscopy in a simple and scaleable way that additionally does not require labeling of the DNA strands.     

Biosorption characteristics of Bacillus sp. ATS-2 immobilized in silica gel for removal of Pb(II)

The bacterial strain Bacillus sp. ATS-2 isolated from Pb(II) polluted soil was immobilized with a silica matrix and Pb(II) biosorption properties of immobilized biosorbent were examined. Optimum biosorption conditions were investigated in the fixed bed column with the variation in the parameters of pH, bed length, flow rate and influent concentration. The Pb(II) biosorption equilibrium was attained within 60 min and the maximum biosorption yield for silica gel immobilized Bacillus sp. ATS-2 was determined as 91.73% at pH 4.0. The higher biosorption yields were observed at flow rates of 60 and 180 ml h(-1). The optimum bed length for the column was found as 10 cm. Data obtained from batch studies were evaluated by Freundlich, Langmuir and Dubinin-Radushkevich (D-R) isotherm models. The maximum monolayer capacity of Bacillus sp. ATS-2 for Pb(II) was 2.36 x 10(-5) mol g(-1). The involvement of the functional groups on the surface of immobilized cells in biosorption process was also evaluated by FTIR spectral analysis.     

Effect of binder type and content on physical and mechanical properties of geopolymers

In this study, the physical and mechanical behaviors of geopolymers prepared by using different amounts of silica fume and calcium hydroxide as binding materials, acidic pumice as fine aggregate and waste aluminium particles as air-entraining agent were investigated. Test results showed that binder types, amount of binders and alkali activator (sodium hydroxide) significantly affected the physical and mechanical behavior of geopolymer specimens. Bulk density, compressive and flexural strength decreased with the higher alkali activator content. Addition of waste aluminium particles led to decrease in bulk density and strength due to the some extent of entrained air. In the case of same alkali activator content, compressive and flexural strength increased with increase in silica fume and calcium hydroxide up to a certain level.     

Malicious code detection in android: the role of sequence characteristics and disassembling methods

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers.

     

A variable iterated greedy algorithm with differential evolution for the no-idle permutation flowshop scheduling problem

This paper presents a variable iterated greedy algorithm (IG) with differential evolution (vIG_DE), designed to solve the no-idle permutation flowshop scheduling problem. In an IG algorithm, size d of jobs are removed from a sequence and re-inserted into all possible positions of the remaining sequences of jobs, which affects the performance of the algorithm. The basic concept behind the proposed vIG_DE algorithm is to employ differential evolution (DE) to determine two important parameters for the IG algorithm, which are the destruction size and the probability of applying the IG algorithm to an individual. While DE optimizes the destruction size and the probability on a continuous domain by using DE mutation and crossover operators, these two parameters are used to generate a trial individual by directly applying the IG algorithm to each target individual depending on the probability. Next, the trial individual is replaced with the corresponding target individual if it is better in terms of fitness. A unique multi-vector chromosome representation is presented in such a way that the first vector represents the destruction size and the probability, which is a DE vector, whereas the second vector simply consists of a job permutation assigned to each individual in the target population. Furthermore, the traditional IG and a variable IG from the literature are re-implemented as well. The proposed algorithms are applied to the no-idle permutation flowshop scheduling (NIPFS) problem with the makespan and total flowtime criteria. The performances of the proposed algorithms are tested on the Ruben Ruiz benchmark suite and compared to the best-known solutions available at http://soa.iti.es/rruiz as well as to those from a recent discrete differential evolution algorithm (HDDE) from the literature. The computational results show that all three IG variants represent state-of-art methods for the NIPFS problem.     

Multiplicative neuron model artificial neural network based on Gaussian activation function

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.     

Fumed silica filled poly(dimethylsiloxane-urea) segmented copolymers: Preparation and properties

Novel fumed silica filled thermoplastic poly(dimethylsiloxane-urea) (TPSU) segmented copolymers were synthesized and characterized. TPSU copolymers were prepared from a cycloaliphatic diisocyanate, aminopropyl terminated PDMS oligomers with number average molecular weights of 3,200, 10,800 and 31,500 g/mol and 2-methyl-1,5-diaminopentane chain extender. Two different types of fumed silica HDK H2000 (hydrophobic) and HDK N20 (hydrophilic) were utilized and incorporated into silicone-urea copolymers in amounts of 1-60% by weight. Influence of the silica type (hydrophilic versus hydrophobic), amount of silica loading and the PDMS soft segment molecular weight on the morphology, tensile properties and modulus-temperature behavior of the nanocomposites were determined. Major observations of this study were: (i) under the blending conditions used, incorporation of silica does not seem to interfere significantly with the hydrogen bonding between urea groups, (ii) incorporation of silica does not affect the glass transition temperature of PDMS, (iii) incorporation of silica influences the tensile and thermomechanical properties of silicone-urea segmented copolymers significantly, (iv) average molecular weight of the PDMS soft segment in the silicone-urea copolymer plays a critical role on the improvement of the tensile properties of the fumed silica/TPSU composites.     

Electrochemical oxidation of terbium(III) in aqueous media: influence of supporting electrolyte on oxidation potential and stability

Journal of Applied Electrochemistry - Apart from the generally dominant trivalent oxidation state, several lanthanides can occur in divalent or tetravalent states as well. Changing the valence...