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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry
The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry
The adsorption of potentially toxic element ions from contaminated water sources has garnered significant attention due to its critical role in environmental remediation and ensuring safe drinking water. Potentially toxic element ions can be removed from water using conventional adsorbents such as activated zeolites; however, these materials have low absorption and slow kinetics. To solve these issues, carbon-based adsorbents that exhibit easy synthesis, high porosity, designability, and stability have been proposed. In this study, a carbon-based adsorbent, named Magnetic Nitrogen-Doped Carbon (M-NC), and graphene oxide were developed for the selective removal of potentially toxic element ions. To increase the potential for HM immobilization, sulfide-modified biochar was created via a process called simultaneous carbon layer encapsulation. A theoretical physicochemical and thermodynamic investigation of the adsorption of potentially toxic elements s Zn2+, Cd2+, Ni2+, Ag2+, Pb2+ and Cu2+ on carbon-based adsorbents was performed with statistical physics fundaments. The biochar with large surface areas is used to remove potentially toxic element ions, one of the most important potentially toxic element pollutants, from aqueous solutions. The capacity of the adsorbent for removing potentially toxic element ions was studied using Langmuir adsorption isotherm under ultrasound-assisted conditions. The MNCs can be applied to the Langmuir model and pseudo-second-order kinetics. It is possible to use the Langmuir and second-order kinetic equations to accurately explain the adsorption method. Thermodynamic limitations were also envisioned because sorption is exothermic when it happens spontaneously. A homogeneous statistical physics adsorption model was used to describe and analyze the experimental potentially toxic element removal isotherms at 30 °C and pH5 utilizing adsorbents produced by pyrolysis of biomasses (broccoli stalks). The findings show the proposed adsorbent, with an efficiency of 98.7 % and even reaching 99.3 % in certain cases, making it a standout choice for potentially toxic element removal applications. This research holds significance in advancing the understanding of environmentally sustainable potentially toxic element removal processes, particularly in the context of biomass-derived adsorbents, offering potential solutions for water purification and environmental remediation.
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