Abstract
Traffic flow prediction is a critical component of intelligent transportation systems. This study introduces a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for predicting traffic flow. The model utilizes traffic, weather, and holiday data. To evaluate the model's performance, three experiments were assessed: E1, using all available inputs; E2, excluding weather conditions; and E3 excluding holiday information. The model was trained using the previous 3, 12, and 24 h of data to predict traffic flow for the next 12 h, and its performance was compared with a LSTM model. Traffic predictions benefit from having a large and diverse dataset. Bi-LSTM model can capture temporal patterns more effectively than the LSTM. The MAPE value is improved in around 1% when we increase the historical from 3h to 24 h, plus 1% if Bi-LSTM model is used. Better results are obtained when contextual information is provided. These results reinforce the potential that deep learning models have in the prediction of traffic conditions and the impact of a large and varied dataset in the accuracy of these predictions.

Abstract

Abstract
Small, medium, and large organizations collect vast amounts of data with the expectation of using it to generate commercial value. Machine learning is a powerful tool for extracting valuable insights from this data and serves as a pivotal sales strategy for companies to maximize profits. This paper seeks to analyze sales data and discern patterns in sales among products that exhibit similarities, such as boxes and bags. In order to achieve this goal, was used unsupervised learning methods that allow the segmentation of groups, specifically Principal Component Analysis (PCA), k-means algorithms, and hierarchical clustering. PCA was used to identify correlated variables and find hidden patterns in the data, particularly pertaining to product families with similar sales. Elbow, Silhouette, and 30 indices methods were applied to determine the optimal number of clusters. Based on these results, it was determined the optimal number of clusters. Validation methods were employed to identify the clustering algorithm exhibiting the best performance. Stability measures evaluated the consistency of the clusters, while the cophenetic coefficient aided in determining the most effective data grouping method. After validation, the clustering algorithms were implemented. The results indicated that all clustering algorithms effectively segmented the data, with particular emphasis on the performance of the k-means algorithm. This study identified product groups with similar sales patterns and key products that impact the company’s global sales. Multivariate analysis provided a deeper understanding of sales dynamics, enabling the company to implement targeted marketing strategies and optimize resource allocation to boost bag and box sales in Portugal and other countries. © 2025 Elsevier B.V., All rights reserved.

Abstract
Airborne Wind Energy (AWE) systems represent an innovative method for capturing wind energy at high altitudes, where wind conditions are typically stronger and more consistent. These systems utilize flying devices tethered to a ground station to harness wind energy. An AWE system comprises a tether connecting the flying device to a base station, a control system for maneuvering the device, and a mechanism for converting kinetic energy into electricity. Researchers are exploring various materials, designs, and control methods to enhance the efficiency and reliability of AWE systems. Over the past decade, interest in AWE has surged, leading to a substantial increase in scholarly publications on the topic. This research conducts an in-depth bibliometric analysis. This analysis highlights emerging topics, allowing researchers to identify new trends and areas of interest within a field. By emphasizing these emerging topics, researchers and stakeholders can better align their efforts with the latest developments and opportunities in their area of study. Findings reveal that research on control techniques in AWE has grown at an average annual rate of 16% since 2013. Additionally, the study identifies the most influential aspects of the literature, including key topics, articles, authors, and keywords. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract

Abstract
The concept of circular economy aims to extend the end-of-life of products by reducing or reusing products and materials, being Design a central part of a successful circular product. In line with the 6Rs strategy for circularity, Redesign (applied by eco-design practices) allows the creation of products that can be easily repaired, upgraded, or disassembled, extending their life, and fomenting a circular economy. For that reason, the aim of this research is to analyse the role of Redesign in the circularity of footwear products. Exploratory qualitative research was conducted, with five in-depth interviews with founders and R&D managers of prominent footwear organizations. Results demonstrate that most interviewed companies, which were born circular, considered Redesign practices from the definition of the product concept. In conclusion, looking at Redesign strategies holistically and through its specific sub-relationships have a major impact on the company's circularity practises.