Abstract
Selective thinning is a crucial operation to reduce forest ignitable material, to control the eucalyptus species and maximise its profitability. The selection and removal of less vigorous stems allows the remaining stems to grow healthier and without competition for water, sunlight and nutrients. This operation is traditionally performed by a human operator and is time-intensive. This work simplifies selective thinning by removing the stem selection part from the human operator's side using a computer vision algorithm. For this, two distinct datasets of eucalyptus stems (with and without foliage) were built and manually annotated, and three Deep Learning object detectors (YOLOv5, YOLOv7 and YOLOv8) were tested on real context images to perform instance segmentation. YOLOv8 was the best at this task, achieving an Average Precision of 74% and 66% on non-leafy and leafy test datasets, respectively. A computer vision algorithm for automatic stem selection was developed based on the YOLOv8 segmentation output. The algorithm managed to get a Precision above 97% and a 81% Recall. The findings of this work can have a positive impact in future developments for automatising selective thinning in forested contexts.

Abstract
Industry 4.0 was publicly introduced in Germany in 2011 and is known as the fourth industrial revolution, whose goal is to improve manufacturing processes and increase the competitiveness of the manufacturing industry. Industry 4.0 uses technological concepts such as Cyber-Physical Systems, Internet of Things and Cloud Computing to create services, reduce costs and increase productivity in industry. This paper aims to explore the use of context-aware applications in Industry 4.0 in order to assist workers in decision making and thus improve the performance of factory production lines. This literature review is part of the project “Continental AA’s Factory of the Future” (Continental FoF) and will integrate a context-aware system in Industry 4.0 of the mentioned company, which is a manufacturer of radio frequency devices for the automotive industry. This systematic literature review identifies, from the researched solutions, the concept of context and context-awareness, the main technologies used in context-aware systems, how context management is performed, as well as the most used integration and communication protocols. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Problem definition: Online retailers often receive customer orders comprising several products of differing origins. To fulfill these orders, retailers must ship multiple parcels from different locations and-unless they are grouped somewhere along the supply chain-these may reach the customer's doorstep one by one. Academic/practical relevance: We conjecture here that receiving products sequentially instead of all together affects a consumer's reaction to her purchases, possibly influencing-for good or ill-her decision to return products, as well as her overall service satisfaction. We use two-year granular data from an online fashion marketplace to test this hypothesis and characterize consumer behavioral responses to delivery consolidation and examine how it impacts supply chain stakeholders. Methodology: To achieve causal inference, we exploit the fact that the couriers used by the focal marketplace gather together certain parcels for reasons related more to the timing of their arrival than their actual customers, thereby exogenously consolidating the delivery of some orders. We construct a balanced sample of matched twin multiproduct orders that are alike in all respects except their delivery: consolidated (all parcels delivered jointly) versus otherwise (split). Results: We find that delivery consolidation benefits the marketplace and all its suppliers. By eliminating the stress associated with split deliveries, delivery consolidation pleases consumers as it leads to fewer returns and higher overall satisfaction. Managerial implications: Delivering all products in an order together, even if later, reduces the probability of a return, which improves the financial performance of the marketplace and its suppliers and reduces reverse logistics. Our results suggest that in our context, delivery speed matters less than the convenience of receiving all ordered goods in a single delivery, and we provide directions for adapting logistics strategies accordingly. Our empirical findings also imply that the return decisions of multiple products purchased at once should not be considered to be independent. Finding tractable ways of modeling this feature will be necessary in further driving retail practice through theoretical research that accounts for the behavioral implications of delivery consolidation when optimizing fulfillment decisions.

Abstract
Reducing vibration and noise in electrical machines for a given application is not a straightforward task, especially when the application imposes some restrictions. There are many techniques for reducing vibration based on design or control. Switched reluctance motors (SRMs) have a double-saliency structure, which results in a radial pulsation force. Consequently, they cause vibration and acoustic noise. This paper investigates the correlation between the radial force and the skew angle of the stator and/or rotor circuits. We computed the analysis from two-dimensional (2D) transient magnetic finite-element analysis (FEA) of three machine topologies, namely the 12/8 three-phase SRM, the 6/4 three-phase SRM and the 8/6 four-phase SRM. Compared to SRM, these topologies have the same basic dimensions (stator outer diameter, rotor outer diameter, and length) and operate in the same magnetic circuit saturation. The flux linkage and torque characteristics of the different motors are presented. The radial force distributed on the stator yoke under various skewing angles is studied extensively by FEA for the three machines. It is also demonstrated the effect of skewing angles in the reduction of radial force without any reduction in torque production. © 2023, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Purpose: This study aims to present an innovative sandbox platform that implements a decision support system (DSS) to assess the sustainable development goals (SDGs) addressed at the municipal level. It intends to determine the relative importance of each SDG in municipalities and explore the synergies that can be discovered among them. Design/methodology/approach: Participatory action research is used to develop a DSS and an algorithm designated as discrete heavy fuzzy was also developed, which extends the Apriori algorithm to include discrete quantitative assessments of the level of SDG compliance by each project. A scenario consisting of three municipalities in Portugal (i.e. Porto, Loulé and Castelo de Vide) was chosen to demonstrate the implementation of the sandbox platform and to interpret the observed results. Findings: The results reveal significant differences in the typology of SDGs addressed by each municipality. It was found that municipal sustainable projects are strongly influenced by the contextual factors of each municipality. Porto has projects that address the first five SDGs. Loulé appears projects that promote innovation, the fight against climate change and the development of sustainable cities. Castelo de Vida has initiatives related to innovation and infrastructure and decent work and economic growth. Research limitations/implications: This study provides knowledge about the relative importance of the SDGs in Portuguese municipalities and explores the synergies among them. The proposed sandbox platform fills the gaps of the ODSlocal Webtool by proposing a dynamic and interactive approach for the exploration of quantitative indicators regarding the implementation status of the SDGs established in the 2030 Agenda. Originality/value: This study provides knowledge about the relative importance of the SDGs and the various synergies that exist between them considering the Portuguese municipalities. The sandbox platform presented and developed within this study allows filling the gaps of the ODSlocal Webtool that gathers essentially qualitative information about each project and offers a dynamic and interactive exploration with quantitative indicators of the implementation status of the SDGs established in the 2030 Agenda.

Abstract