Alzira Mota

Abstract
PurposeIn an increasingly competitive market, equipment availability is a strategic variable for the competitiveness and success of companies. The objective of the research in this article is to present contributions to reduce unplanned production stoppages and optimise the operational efficiency of an injection moulding machine. This will be achieved by developing a systematic strategy to integrate predictive and condition-based maintenance systems with maintenance management software.Design/methodology/approachThe model developed is based on the continuous monitoring of electrical signals and vibrations, with the processing of data collected in real time through a script developed in Python. This integrates the information into the maintenance management software, facilitating a quick and accurate response to component wear conditions. The methodology employed was action research, as it was a case study developed in a real context, with active participation in development and implementation, with the aim of continuous improvement.FindingsIn August, a substantial increase was observed in the primary indicators: The mean time between failures (MTBF) increased by 97.36%, the mean time to repair (MTTR) increased by 313.31%, and the downtime was reduced by 65.04%. In December, although the figures were more moderate, significant improvements were maintained: The MTBF increased by 20%, the MTTR increased by 84%, and the downtime was reduced by 79%.Originality/valueThe findings of the study indicated that the implementation of a structured approach for the acquisition and monitoring of electrical signals and vibration data was imperative to achieve substantial gains.

Abstract
The implementation of Onshore Power Supply (OPS) systems is recognised as an effective strategy to reduce air emissions and noise pollution from ships at berth, thereby supporting decarbonization goals established by the European Union. Despite its environmental advantages, the economic viability of OPS remains one of the main barriers to its widespread adoption in ports. In particular, limited research attention has paid to the extent to which the service level provided by OPS infrastructure affects the financial performance of such investments.This paper addresses this gap by analysing the relationship between service level and economic viability through a case study of the Southern Container Terminal at the Port of Leixões (Portugal). Three investment scenarios were evaluated, corresponding to the installation of one, two, and three OPS systems, delivering service levels of 65%, 94%, and 100%, respectively. A 20-year financial analysis (2025–2044) was conducted using Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period as key indicators, under discount rates of 3% and 5%.The results demonstrate that service level significantly influences investment feasibility. The scenario with a 65% service level is economically unviable, presenting negative NPVs and an IRR below the cost of capital. In contrast, the 94% and 100% service level scenarios generate positive NPVs (at 3%) and IRRs slightly above 5%, indicating moderate but acceptable profitability. However, increasing the service level from 94% to 100% does not produce a proportional improvement in financial returns, revealing diminishing marginal gains relative to the intermediate scenario.The findings indicate that maximising service level does not necessarily maximise economic attractiveness. Instead, an optimal balance between infrastructure capacity and expected demand is essential to ensure financial sustainability while delivering substantial environmental benefits. These findings offer practical guidance for port authorities and policymakers seeking to design economically viable OPS systems.

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
This paper compares the performance of Simulated Annealing and Tabu Search meta-heuristics in addressing a parallel machine scheduling problem aimed at minimizing weighted earliness, tardiness, total flowtime, and machine deterioration costs-a multi-objective optimization problem. The problem is transformed into a single-objective problem using weighting and weighting relative distance methods. Four scenarios, varying in the number of jobs and machines, are created to evaluate these metaheuristics. Computational experiments indicate that Simulated Annealing consistently yields superior solutions compared to Tabu Search in scenarios with lower dimensions despite longer run times. Conversely, Tabu Search performs better in higher-dimensional scenarios. Furthermore, it is observed that solutions generated by different weighting methods exhibit similar performance. © 2025 The Author(s).