The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges

Electronic Markets - The growth of technology has resulted in the use of state-of-the-art systems such as artificial intelligence (AI) and robot-based applications and services in the hotel...     

Functionalization of UiO-66-NH2 by In-Situ Incorporation of Nanomaterials to Enhance Photocatalytic Efficiency Towards Oxygen Evolution Reaction

The catalysts for oxygen evolution reaction (OER) are required to generate clean energy. Herein, one-step solvothermal synthesis and modification of amino functionalized Zr-based metal organic framework, UiO-66-NH₂, is reported. The catalytic efficiency of UiO-66-NH₂ towards OER is improved by incorporating the cerium-based nanocomposite such as Ce₂O₃, TiO₂/Ce₂O₃ and CoO/Ce₂O₃. The synthesized samples are characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray, diffraction, and ultraviolet–visible spectroscopy. The synthesized materials are coated on nickel foam for investigating the catalytic activity towards OER by cyclic voltammetry and linear sweep voltammetry. Amongst, CoO/Ce₂O₃@UiO-66-NH₂/NF exhibits excellent OER-catalytic activity and delivers 10 mA cm^?2 current density at just 228 mV overpotential which is superior to many previously reported OER catalysts and its comparative products. To understand kinetics, Tafel slope is derived from LSV curve and is just 92 mV dec^?1.

Graphical Abstract

     

On the computation of analytical solutions of an unsteady magnetohydrodynamics flow of a third grade fluid with Hall effects

In this article, a combination of Lie symmetry and homotopy analysis methods (HAM) are used to obtain solutions for the unsteady magnetohydrodynamics flow of an incompressible, electrically conducting third grade fluid, bounded by an infinite porous plate in the presence of Hall current. In particular, similarity reductions are performed on the governing equations in its complex scalar and corresponding vector system forms. Also, nontrivial conservation laws, using the multiplier approach, are constructed for the complex scalar equation. Furthermore, a comparison of the results with numerical results already existing in the literature is done. The analytical solutions are presented through graphs by choosing a range of the relevant physical parameters. The underlying calculations were obtained via a combination of software packages in Mathematica and Maple, in particular, for the Lie symmetry generators, Euler Lagrange operators and homotopy operators; the latter being towards the construction of the conserved flows.     

Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities. With resting state threshold circle, VPA has been used to detect a hemodynamic response in fNIRS signals, whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial. Multiple threshold circles are drawn in the vector plane, where each circle is drawn after task completion in each trial of EEG signal. Finally, both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity. Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy, that is 91.35%, as compared to other classifiers such as support vector machine (SVM), convolutional neural networks (CNN), deep neural networks (DNN) and VPA (with dual-threshold circles) with classification accuracies 82%, 89%, 87% and 86% respectively. Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.     

Novel 1,4‐Substituted‐1,2,3‐Triazoles as Antitubercular Agents

Tuberculosis (TB) remains a pressing unmet medical need, particularly with the emergence of multidrug‐resistant and extensively drug‐resistant tuberculosis. Here, a series of 1,4‐substituted‐1,2,3‐triazoles have been synthesized and evaluated as potential antitubercular agents. These compounds were assembled via click chemistry in high crude purity and in moderate to high yield. Of the compounds tested, 12 compounds showed promising antitubercular activity with six possessing minimum inhibitory concentration (MIC) values <10 μg mL^?1, and total selectivity for Mycobacterium tuberculosis (Mtb) growth inhibition. A second set of 21 compounds bearing variations on ring C were synthesized and evaluated. This second library gave an additional six compounds displaying MIC values ≤10 μg mL^?1 and total selectivity for Mtb growth inhibition. These compounds serve as an excellent starting point for further development of antitubercular therapies.