A novel proposal for all-optical 1-bit comparator using nonlinear PhCRRs

Photonic Network Communications - All-optical comparators play crucial roles in all-optical data processing systems. In this paper, we proposed a novel structure for realizing an all-optical...     

Simple template-free solution route for the synthesis of Cu(OH)2 and CuO nanostructures and application for electrochemical determination three ß-blockers

The synthesis of Cu(OH)₂ nanoparticles in a solution phase has been realised with high yield at low cost by simply dropping ammonia solution into an aqueous solution of CuCl₂ at ambient temperature. Furthermore, well-defined CuO nanostructures were produced by thermal dehydration of the as-prepared Cu(OH)₂ nanostructures in the solid state. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) were used to characterise the products. The electrochemical behaviour of three ß-blocker drugs on copper-oxide nanoparticle modified carbon paste electrodes was investigated. The modified electrodes showed excellent catalytic activity towards the oxidation of atenolol, carvedilol and propranolol in buffer solution. The linear concentration range of the proposed sensor for the atenolol, carvedilol and propranolol detection were 12–96, 5–37 and 10–104?µM, respectively.     

Quality Assessment of Poultry Egg Based on Visible–Near Infrared Spectroscopy and Radial Basis Function Networks

In this research the possibility of a non-destructive prediction of two main quality parameters of poultry egg using principle component analysis and radial basis function network by the transmission visible–near infrared spectroscopy method was investigated. The studied parameters include Haugh unit and air cell height as a function of a 5-week storage duration at room (25°C and 40% relative humidity) and refrigerator (5°C and 75% relative humidity) conditions. The spectra were interpreted and a radial basis function network model was developed for both storage conditions at wavelength ranges of 300–1100 nm. The developed models yielded a good prediction accuracy of Haugh unit for intact egg (R² value 0.745 and 0.76) as well as air cell height (R² value 0.835 and 0.844) for room and refrigerator conditions, respectively. Results of the experiment showed the developed model can be used in the prediction of egg freshness indices satisfactorily.     

A regularization-based deep unsupervised model for affine multimodal co-registration

Extensive network receptive field is key for unsupervised affine registration because instead of deformable registration that takes care of local subtleties, the affine registration is global so that the last layers need to see big patches of the organ-in-interest. To extend the network's receptive field, we need to go for deeper networks, which causes producing complex models. On the other hand, affine transformation is restricted by its low degree-of-freedom (DoF) where larger models increasingly develop the hazard of overfitting. To worsen the situation, the regularizer module cannot be applied to the affine transformation with such a restricted DoF. In this paper, we propose a differentiable computational layer to convert the affine transformation outputted by the network to its corresponding dense displacement field. Such an affine-to-field layer enables us to apply different regularization terms on the outputted transformation in order to avoid the overfitting phenomenon while deepening the network. The proposed approach was evaluated on an annotated hard multimodal dataset containing 1109 pairs of CT/MR images of the brain with different heterogeneity for example, variety in scanners, setups and resolutions. Based on the results, the proposed customized layer is fully successful to handle the overfitting for deeper networks that are able to produce richer transformations than the shallower networks from different evaluation metrics for example, in target registration error the proposed network with seven layers has a 13.3% (or 9.1 mm) improvement in performance. The implementation of the proposed customized affine-to-field layer in the Python, Keras package with the Tensorflow backend can be publically accessed via https://github.com/boveiri/Deep-coReg .     

Social cost-efficient service quality—Integrating customer valuation in incentive regulation: Evidence from the case of Norway

In order to overcome the perverse incentives of excessive maintenance reductions and insufficient network investments arising with incentive regulation of electricity distribution companies, regulators throughout Europe have started regulating service quality. In this paper, we explore the impact of incorporating customers’ willingness-to-pay for service quality in benchmarking models on cost efficiency of distribution networks. Therefore, we examine the case of Norway, which features this approach to service quality regulation. We use the data envelopment analysis technique to analyse the effectiveness of such regulatory instruments. Moreover, we discuss the extent to which this indirect regulatory instrument motivates a socially desired service quality level. The results indicate that internalising external or social cost of service quality does not seem to have played an important role in improving cost efficiency in Norwegian distribution utilities.     

The capacitated multi-facility location–allocation problem with probabilistic customer location and demand: two hybrid meta-heuristic algorithms

A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed in this article. The model is formulated into the frameworks of the expected value model (EVM) and the chance-constrained programming (CCP) based on two different distance measures. In order to solve the model, two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms. However, in the first algorithm, named SSGA, a special type of genetic algorithm is combined and in the second, SSVDO, a vibration-damping optimisation (VDO) algorithm is united. The Taguchi method is employed to tune the parameters of the two proposed algorithms. Finally, some numerical examples are given to illustrate the applications of the proposed methodologies and to compare their performances.     

The effect of nisin and garlic (Allium sativum L.) essential oil separately and in combination on the growth of Listeria monocytogenes

In the present study, the anti-listerial activity of the nisin and garlic (Allium sativum L.) essential oils (GO) alone and in combination was investigated at different temperatures (20 and 30 °C), pH (6.8, 5.6 and 4.8) and NaCl concentrations (0, 0.5, 2.5 and 4.5 g/100 mL). Minimum inhibitory concentrations (MICs) against Listeria monocytogenes were assessed for the nisin and essential oil. Furthermore, for combinations of the antimicrobials, the Differences in Population (DP) method were used to determine their effect. Both essential oil and nisin possessed considerable antimicrobial effects on the microorganism. The MICs for nisin and GO were 12.5 IU/mL and 100 μg/mL, respectively. Regardless of NaCl concentration and temperature, the anti-listerial activity of both GO and nisin was strongly influenced by pH. Moreover, at the same temperature, and regardless of pH value, the growth of the organism was also affected by increasing NaCl concentration. In combination, the DP was related strongly to the agent’s concentration and media pH. Meanwhile, among all combined systems, the effect of the combination (EC) of nisin with GO at 30 °C, pH 5.6 and 0 g/100 mL NaCl, showed significant anti-listerial activity (P ≤ 0.05).