Abstract
Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision-making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical-scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision-making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company's experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy-AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman's rank correlation coefficient (rs) and Kendall's coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

Abstract
Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis - since the year 2000 and onwards - of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers ( e.g. , hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ionselective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Abstract
Purpose - This paper aims to generate knowledge of the impact of different virtual reality (VR) set-ups in tourism promotion regarding destination image, place attachment and behavioural intention.Design/methodology/approach - The paper presents a comparative study of the impact of different visualisation technologies (video, immersive VR and multisensory immersive VR) to promote tourism destinations. The study's dependent variables are destination image, place attachment and behaviour intention.Findings - Results show that VR content impacts these variables. Multisensory immersive VR is the preferred content type for destination promotion. It is also evidenced that female participants scored each variable higher than male participants. Males reported higher scores on the video set-up for destination image and place attachment. Behavioural intention reported higher values in the video when compared to immersive VR in both sexes.Practical implications - This paper concludes that there is a preference towards multisensory set-ups, which suggests that incorporating audiovisual and sensory elements can significantly enhance the effectiveness of VR experiences in attracting and engaging potential tourists.Originality/value - The paper contributes to the scarce body of knowledge regarding the impact of different VR factors on tourism promotion, including the multisensory VR component.

Abstract
This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, is less clear. Through a detailed analysis of L & ouml;wner inequalities between quantum and classical FIMs, this study uncovers the nuanced distinctions and implications of using each type of FIM. Our results indicate that a PQC-based agent using the quantum FIM without additional insights typically incurs a larger approximation error and does not guarantee improved performance compared to the classical FIM. Empirical evaluations in classic control benchmarks suggest even though quantum FIM preconditioning outperforms standard gradient ascent, in general, it is not superior to classical FIM preconditioning.

Abstract
More and more people are relying on the Web to find health information. Challenges faced by individuals with low health literacy in the real world likely persist in the virtual realm. To assist these users, our first step is to identify them. This study aims to uncover disparities in the information-seeking behavior of users with varying levels of health literacy. We utilized data gathered from a prior user experiment. Our approach involves a classification scheme encompassing events during web search sessions, spanning the browser, search engine, and web pages. Employing this scheme, we logged interactions from video recordings in the user study and subjected the event logs to descriptive and inferential analyses. Our data analysis unveils distinctive patterns within the low health literacy group. They exhibit a higher frequency of query reformulations with entirely new terms, engage in more left clicks, utilize the browser's backward functionality more frequently, and invest more time in interactions, including increased scrolling on results pages. Conversely, the high health literacy group demonstrates a greater propensity to click on universal results, extract text from URLs more often, and make more clicks with the mouse middle button. These findings offer valuable insights for inferring users' health literacy in a non-intrusive manner. The automatic inference of health literacy can pave the way for personalized services, enhancing accessibility to information and education for individuals with low health literacy, among other benefits.

Abstract
Socioeconomic growth and population increase are driving a constant global demand for energy. Renewable energy is emerging as a leading solution to minimise the use of fossil fuels. However, renewable resources are characterised by significant intermittency and unpredictability, which impact their energy production and integration into the power grid. Forecasting models are increasingly being developed to address these challenges and have become crucial as renewable energy sources are integrated in energy systems. In this paper, a comparative analysis of forecasting methods for renewable energy production is developed, focusing on photovoltaic and wind power. A review of state-of-the-art techniques is conducted to synthesise and categorise different forecasting models, taking into account climatic variables, optimisation algorithms, pre-processing techniques, and various forecasting horizons. By integrating diverse techniques such as optimisation algorithms and pre-processing methods and carefully selecting the forecast horizon, it is possible to highlight the accuracy and stability of forecasts. Overall, the ongoing development and refinement of forecasting methods are crucial to achieve a sustainable and reliable energy future.