Abstract
Although the concept of Circular Economy (CE) has become popular in recent years, the transition towards a CE system requires a change in consumers' behaviour. However, there is still limited knowledge of consumers' efforts in CE initiatives. The present paper aims to analyse and compare consumers' behaviour towards circular approaches and compare the results on items like generation and demographics. 495 answers were collected through a questionnaire from 3 countries (Albania, Poland, and Portugal). Data collected was analysed mainly through a Crosstabs analysis to identify associations or different behaviours regarding nationality, gender, generation, education, and place of residence. From the paper's findings, we can emphasise that residents of EU countries seem to be more aware of the concept of circular economy. However, price is still a very important factor for EU residents when it comes to deciding on a greener purchase. Albanians (non-EU residents) tend to take a more linear approach when it comes to purchasing a new product regardless of its cost. Regarding the Digital Product Passport, a tool proposed by the European Commission through its Circular Economy Action Plan, non-EU residents have a better understanding of the concept. This tool seems to be more relevant for Millennials and Generation X. Generation Z, i.e., the tech generation, does not show an overwhelming propensity for technological options, such as online buying and digital technologies for a greener society.

Abstract
We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. Hierarchical time series, where lower-level forecasts must sum to upper-level ones, are prevalent in various contexts, such as retail sales across countries. Current empirical evaluations of forecasting methods are often limited to a small set of benchmark datasets, offering a narrow view of algorithm behavior. RHiOTS addresses this gap by systematically altering existing datasets and modifying the characteristics of individual series and their interrelations. It uses a set of parameterizable transformations to simulate those changes in the data distribution. Additionally, RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. This approach allows an in-depth analysis of algorithm and model behavior under diverse conditions. We illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. Furthermore, we found no significant differences in the robustness of the algorithms when applying specific reconciliation methods, such as MinT. RHiOTS provides researchers with a comprehensive tool for understanding the nuanced behavior of forecasting algorithms, offering a more reliable basis for selecting the most appropriate method for a given problem. © 2024 Copyright held by the owner/author(s).

Abstract
Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.

Abstract
When never produced as much data as today, and tomorrow will probably produce even more data. The increase is due not only to the larger number of data sources, but also because the source can continuously produce more recent data. The discovery of temporal patterns in continuously generated data is the main goal in many forecasting tasks, such as the average value of a currency or the average temperature in a city, in the next day. In these tasks, it is assumed that the time difference between two consecutive values produced by the same source is constant, and the sequence of values form a time series. The importance, and the very large number, of time series forecasting tasks make them one of the most popular data analysis application, which has been dealt with by a large number of different methods. Despite its popularity, there is a dearth of research aimed at comprehending the conditions under which these methods present high or poor forecasting performances. Empirical studies, although common, are challenged by the limited availability of time series datasets, restricting the extraction of reliable insights. To address this limitation, we present tsMorph, a tool for generating semi-synthetic time series through dataset morphing. tsMorph works by creating a sequence of datasets from two original datasets. The characteristics of the generated datasets progressively depart from those of one of the datasets and a convergence toward the attributes of the other dataset. This method provides a valuable alternative for obtaining substantial datasets. In this paper, we show the benefits of tsMorph by assessing the predictive performance of the Long Short-Term Memory Network and DeepAR forecasting algorithms. The time series used for the experiments come from the NN5 Competition. The experimental results provide important insights. Notably, the performances of the two algorithms improve proportionally with the frequency of the time series. These experiments confirm that tsMorph can be an effective tool for better understanding the behaviour of forecasting algorithms, delivering a pathway to overcoming the limitations posed by empirical studies and enabling more extensive and reliable experiments. Furthermore, tsMorph can promote Responsible Artificial Intelligence by emphasising characteristics of time series where forecasting algorithms may not perform well, thereby highlighting potential limitations. © 2024 Copyright for this paper by its authors.

Abstract
This study develops some sustainable tourism advertising effects and consumer environmental awareness-raising and examines them by advertising certification and advertising format in a field experiment. The tourism advertising effects are analyzed by five dependent variables: trust and credibility, environmentalism, ad relevance, realism, and flow. Several ANOVA and multiple comparison tests were performed to understand whether these variables varied between groups. Experimental research findings indicate that flow and video format affect tourism advertising and consumer environmental awareness-raising. This study demonstrates the importance of understanding the concept of sustainable tourism and awareness-raising. It also points to identifying the best communication strategies to promote a sustainable destination, as different communication methods may lead to different results. In addition, it provides valuable information for marketers to consider when implementing their communication strategies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Recent advances in optical trapping have opened new opportunities for manipulating micro and nanoparticles, establishing optical tweezers (OT) as a powerful tool for single-cell analysis. Furthermore, intelligent systems have been developed to characterize these particles, as information about their size and composition can be extracted from the scattered radiation signal. In this manuscript, we aim to explore the potential of optical tweezers for the characterization of sub-micron size variations in microparticles. We devised a case study, aiming to assess the limits of the size discrimination ability of an optical tweezer system, using transparent 4.8 µm PMMA particles, functionalized with streptavidin. We focused on the heavily studied streptavidin-biotin system, with streptavidin-functionalized PMMA particles targeting biotinylated bovine serum albumin. This binding process results in an added molecular layer to the particle’s surface, increasing its radius by approximately 7 nm. An automatic OT system was used to trap the particles and acquire their forward-scattered signals. Then, the signals’ frequency components were analyzed using the power spectral density method followed by a dimensionality reduction via the Uniform Manifold Approximation and Projection algorithm. Finally, a Random Forest Classifier achieved a mean accuracy of 94% for the distinction of particles with or without the added molecular layer. Our findings demonstrate the ability of our technique to discriminate between particles that are or are not bound to the biotin protein, by detecting nanoscale changes in the size of the microparticles. This indicates the possibility of coupling shape-changing bioaffinity tools (such as APTMERS, Molecular Imprinted Polymers, or antibodies) with optical trapping systems to enable optical tweezers with analytical capability. © 2024 SPIE.