Artificial Intelligence in Smart Tourism: A Conceptual Framework

Smart tourism destination as: an innovative tourist destination, built on an infrastructure of state-of-the-art technology guaranteeing the sustainable development of tourist areas, accessible to everyone, which facilitates the visitor’s interaction with and integration into his or her surroundings, increases the quality of the experience at the destination, and improves residents’ quality of life. Lopez de Avila (2015). Smart tourism involves multiple components and layers of “smart” include (1) Smart Destinations which was special cases of smart cities integration of ICT’s into physical infrastructure, (2) Smart experience which specifically focus on technology-mediated tourism experience and their engagement through personalization, context-awareness and real-time monitoring, (3) Smart business refer to the complex business ecosystem that creates and supports the exchange of touristic resource and the co-creation of tourism experience. Gretzel et al, (2015). Smart tourism also clearly relies on the ability to not only collect enormous of data but to intelligently store, process, combine, analyze and use big data to inform business innovation, operations and services by artificial intelligence and big data technique. The rapid development of information communication technology (ICT) such as artificial intelligent, cloud computing, mobile device, big data mining and social media cause computing, storage and communication relevant software and hardware popular. Facebook, Amazon, Apple, Microsoft and Google have risen rapidly since 2000. In recent years, Emerging technologies such as Artificial Intelligence, Internet of Thing, Robotic, Cyber Security, 3D printer and Block chain also accelerate the development of industry toward digital transformation trend such as Fintech, e-commerce, smart cities, smart tourism, smart healthcare, smart manufacturing... This study proposes a conceptual framework that integrates (1) artificial intelligence/machine learning, (2) institution/organizational and (3) business processes to assist smart tourism stake holder to leverage artificial intelligence to integrate cross-departmental business and streamline key performance metrics to build a business-level IT Strategy. Artificial intelligence as long as the function includes (1) Cognitive engagement to (voice/pattern recognition function) (2) Cognitive process automation (Robotic Process Automation) (3) Cognitive insight (forecast, recommendation)

Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme

Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

AbstractDifferential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations

Applying a Heuristic Approach for a Minimum-cost Operating Strategy for Tap Water

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information

Direct Large-Area Growth of Graphene on Silicon for Potential Ultra-Low-Friction Applications and Silicon-Based Technologies

Deposition of layers of graphene on silicon has the potential for a wide range of optoelectronic and mechanical applications. However, direct growth of graphene on silicon has been difficult due to the inert, oxidized silicon surfaces. Transferring graphene from metallic growth substrates to silicon is not a good solution either, because most transfer methods involve multiple steps that often lead to polymer residues or degradation of sample quality. Here we report a single-step method for large-area direct growth of continuous horizontal graphene sheets and vertical graphene nano-walls on silicon substrates by plasma-enhanced chemical vapor deposition (PECVD) without active heating. Comprehensive studies utilizing Raman spectroscopy, x-ray/ultraviolet photoelectron spectroscopy (XPS/UPS), atomic force microscopy (AFM), scanning electron microscopy (SEM) and optical transmission are carried out to characterize the quality and properties of these samples. Data gathered by the residual gas analyzer (RGA) during the growth process further provide information about the synthesis mechanism. Additionally, ultra-low friction (with a frictional coefficient ~0.015) on multilayer graphene-covered silicon surface is achieved, which is approaching the superlubricity limit (for frictional coefficients <0.01). Our growth method therefore opens up a new pathway towards scalable and direct integration of graphene into silicon technology for potential applications ranging from structural superlubricity to nanoelectronics, optoelectronics, and even the next-generation lithium-ion batteries

High-throughput Automated Muropeptide Analysis (HAMA) Reveals Peptidoglycan Composition of Gut Microbial Cell Walls

Peptidoglycan (PGN), a net-like polymer constituted by muropeptides, provides protection for microorganisms and has been a major target for antibiotics for decades. Researchers have explored host-microbiome interactions through PGN recognition systems and discovered key muropeptides modulating host responses. However, most common characterization techniques for muropeptides are labor-intensive and require manual analysis of mass spectra due to the complex cross-linked PGN structures. Each species has unique moiety modifications and inter-/intra-bridges, which further complicates the structural analysis of PGN. Here, we developed a high-throughput automated muropeptide analysis (HAMA) platform leveraging tandem mass spectrometry and in silico muropeptide MS/MS fragmentation matching to comprehensively identify muropeptide structures, quantify their abundance, and infer PGN cross-linking types. We demonstrated the effectiveness of HAMA platform using well-characterized PGNs from E. coli and S. aureus and further applied it to common gut bacteria including Bifidobacterium, Bacteroides, Lactobacillus, Enterococcus, and Akkermansia muciiniphila. Specifically, we found that the stiffness and strength of the cell envelopes may correspond to the lengths and compositions of interpeptide bridges within Bifidobacterium species. In summary, the HAMA framework exhibits an automated, intuitive, and accurate analysis of PGN compositions, which may serve as a potential tool to investigate the post-synthetic modifications of saccharides, the variation in interpeptide bridges, and the types of cross-linking within bacterial PGNs.</p