Efficient Computation of the Nonlinear Schrödinger Equation with Time-Dependent Coefficients

open access articleMotivated by the limited work performed on the development of computational techniques for solving the nonlinear Schrödinger equation with time-dependent coefficients, we develop a modified Runge-Kutta pair with improved periodicity and stability characteristics. Additionally, we develop a modified step size control algorithm, which increases the efficiency of our pair and all other pairs included in the numerical experiments. The numerical results on the nonlinear Schrödinger equation with periodic solution verified the superiority of the new algorithm in terms of efficiency. The new method also presents a good behaviour of the maximum absolute error and the global norm in time, even after a high number of oscillations

Nitrogen and Copper doped solar light active TiO2 photocatalyst for water decontamination

A novel class of photocatalytic coating capable of degrading bacterial and chemical contaminants in the presence of visible sunlight wavelengths was produced by depositing a stable photocatalytic TiO2 film on the internal lumen of glass bottles via a sol gel method. This coating was prepared in either undoped form or doped with nitrogen and/or copper to produce visible light-active TiO2 films which were annealed at 600 °C and were characterized by Raman, UV-Vis, and X-ray photoelectron spectroscopy. The presence of doped and undoped TiO2 films was found to accelerate the degradation of methylene blue in the presence of natural sunlight, while copper-doped TiO2 films were found to accelerate bacterial inactivation (of E. coli and E. faecalis) in the presence of natural sunlight

Concomitant orbital and intracranial abscess: A rare complication of sinusitis

Background:  Intracranial and orbital abscesses in combination together are rare complications of sinusitis. They can be life-threatening and can result in multiple sequelae. Case presentation: A 9-year-old female presented with left periorbital swelling, gaze restriction and headache. Following scans, she underwent emergency endoscopic sinus surgery, evacuation of the intraorbital empyema and stereotactic mini-craniectomy with the evacuation of the extradural empyema as a joint case. The patient recovered well and was discharged to complete intravenous antibiotics for 6 weeks. Conclusion: In the pediatric population intracranial complications of acute sinusitis can have more devastating consequences. Therefore prompt recognition and management are essential within a multidisciplinary team setting. We also highlight the rarity of concomitant multi-site abscess formation and the need to be vigilant for same

Assessing the perceived realism of agent grouping dynamics for adaptation and simulation

Virtual crowds are a prominent feature for a range of applications; from simulations for cultural heritage, to interactive elements in video games. A body of existing research seeks to develop and improve algorithms for crowd simulation, typically with a goal of achieving more realistic behaviours. For applications targeting human interaction however, what is judged as realistic crowd behaviour can be subjective, leading to situations where actual crowd data is not always perceived to be more real than simulation, making it difficult to identify a ground truth. We present a novel method using psychophysics to assess the perceived realism of behavioural features with respect to virtual crowds. In this instance, a focus is given to the grouping dynamics feature, whereby crowd composition in terms of group frequency and density is evaluated through thirty-six conditions based on crowd data captured from three pedestrianised real-world locations. The study, conducted with seventy-eight healthy participants, allowed for the calculation of perceptual thresholds, with configurations identified that appear most real to human viewers. The majority of these configurations correlate with the values extracted from the crowd data, with results suggesting that viewers have more perceptual flexibility when group frequency and density are increased, rather than decreased.</p

Effect of Organic Chelates on the Performance of Hybrid Sol–Gel Coated AA2024-T3 Aluminium Alloys

Sol–gels are organic–inorganic polymers formed by hydrolysis/condensation reactions of alkoxide precursors, primarily silanes, which have found applications as electronic, optical and protective coatings. These coatings possess important characteristics such as chemical stability, physical strength and scratch resistance. Further performance improvement is achieved through the incorporation of zirconium and titanium based nanoparticles, also formed through the sol–gel process. However due to the inherent difference in the reactivity of the precursors, the hydrolysis of each precursor must be carried out separately before being combined for final condensation. Zirconium precursors are commonly chelated using acetic acids, prior to hydrolysis, to lower the hydrolysis rate. In this body of work various ligands such as organic acids, acetyl acetone (AcAc) and 2,2-bipyridine (Bipy) were used to control the zirconium hydrolysis reaction and form nanoparticles within the silane sol matrix. Nanoparticle modified coatings formed from the silane sol on AA 2024-T3 aluminium were characterised spectroscopically, electrochemically and calorimetrically to evaluate the potential effect of the different chelates on the final film properties while neutral salt spray tests were performed to study their anti-corrosion performance. Results indicate that the acid ligand modified coatings provided the best performance followed by AcAc, while Bipy was the poorest. In all cases the zirconium nanoparticle improved the protective properties of the sol–gel coating

Corrosion Protection Properties of Various Ligand Modified Organic Inorganic Hybrid Coating on AA 2024-T3

The inclusion of zirconium precursors to prepare organosilane solgel coatings improves the corrosion protection performance of the coatings on aluminium and steel. The inherent differences in the hydrolysis rates of the silane and zirconium precursors, various ligands were used to control the hydrolysis by decreasing the number of reactive alkoxide group. Hybrid sols were synthesised using 3-(trimethoxysilyl) propylmethacrylate (MAPTMS) and zirconium n-propoxide chelated with organic ligands including different organic acids, acetyl acetone and 2 2’ bipyridyl. The effects of zirconia inclusion on the properties of the coatings were compared on the aerospace alloy AA 2024-T3. Electrochemical analysis and salt spray exposure characterized the corrosion protective properties. The results indicate that acid chelated systems possess better corrosion protection when compared to the other ligands, due to smaller zirconium nanoparticles being formed. In particular superior performance was displayed by the coatings involving 3,4 diaminobenzoic acid (DABA) due to inherent anticorrosive properties

A Neural Network for Interpolating Light-Sources

This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method interpolates light-sources by utilising a line-algorithm, which detects the direction of light sources in the image. Both of these methods are single-image solutions and employ deterministic methods to calculate the values from the image alone, without the need for illumination-models. They extract real-time geometry from the light source in an image, rather than mapping an illumination-model onto the image, which are the only models used today. Finally, those outputs are used to train a Convolutional Neural Network. This displays greater accuracy than previous methods for shadow detection and can predict light source-direction and thus orientation accurately, which is a considerable innovation for an unsupervised CNN. It is significantly faster than the deterministic methods. We also present a reference dataset for the problem of shadow and light direction detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Direct deduction of chemical class from NMR spectra

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. Therefore, for a long time automation in the area of NMR has been actively sought. The method identified as suitable for the classification is a convolutional neural network (CNN). Other methods, including clustering and image registration, have not been found suitable for the task in a comparative analysis. The result shows that deep learning can offer solutions to automation problems in cheminformatics.Comment: 8 pages, 1 figure, 4 table