Abstract
Time series forecasting is an important tool for planning and decision-making. Considering this, several forecasting algorithms can be used, with results depending on the characteristics of the time series. The recommendation of the most suitable algorithm is a frequent concern. Metalearning has been successfully used to recommend the best algorithm for a time series analysis task. Additionally, it has been shown that decomposition methods can lead to better results. Based on previously published studies, in the experiments carried out, time series components were used. This work proposes and empirically evaluates METAFORE, a new time series forecasting approach that uses seasonal trend decomposition with Loess and metalearning to recommend suitable algorithms for time series forecasting combinations. Experimental results show that METAFORE can obtain a better predictive performance than single models with statistical significance. In the experiments, METAFORE also outperformed models widely used in the state-of-the-art, such as the long short-term memory neural network architectures, in more than 70%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70\%$$\end{document} of the time series tested. Finally, the results show that the joint use of metalearning and time series decomposition provides a competitive approach to time series forecasting.

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PurposeThis study aims to understand the perceived emotions of human-artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector.Design/methodology/approachThis research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach.FindingsThe results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human-AI interactions.Practical implicationsThe insights from human-AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value.Originality/valueBeyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors' knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human-AI interaction.

Abstract
Computer simulations, which are performed at a single wavelength at a time, have been traditionally used to estimate the optical properties of tissues. The results of these simulations need to be interpolated. For a broadband estimation of tissue optical properties, the use of computer simulations becomes time consuming and computer demanding. When spectral measurements are available for a tissue, the use of the photon diffusion approximation can be done to perform simple and direct calculations to obtain the broadband spectra of some optical properties. The additional estimation of the reduced scattering coefficient at a small number of discrete wavelengths allows to perform further calculations to obtain the spectra of other optical properties. This study used spectral measurements from the heart muscle to explain the calculation pipeline to obtain a complete set of the spectral optical properties and to show its versatility for use with other tissues for various biophotonics applications.

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Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) have emerged as key innovations in the industry world, with AMRs offering flexibility a nd adaptability for dynamic environments, while AGVs provide high accuracy for repetitive tasks; thus, this research proposes a study of fleets of both AGVs and AMRs to enhance productivity and efficiency in industrial settings. Several tests were performed where the duration of a mission, the success and collision rate, and the average number of disputes per mission were analyzed in order to obtain results. In conclusion, while AGVs tend to be more reliable and consistent in task completion, AMRs offer greater flexibility a nd speed.

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