Abstract
This research aims to analyze the impact of social responsibility (SR) on the performance of 216 European companies from 2017 to 2021. The objective of this research is to determine how the operational, financial, and market performance of companies is influenced by social responsibility practices. The methodology adopted is quantitative in nature, using the estimation of models for panel data. To quantify corporate performance, this study uses the return on assets (ROA), the return on equity (ROE), and finally Tobin's Q ratio. Additionally, environment, social, and governance (ESG) and United Nations Global Compact (GC) scores are used to quantify SR. Our findings indicate a complex relationship between SR and corporate performance. While SR positively impacts market performance, it negatively affects operational and financial performance. This disparity becomes more pronounced when comparing companies with the highest and lowest SR scores. Further analysis reveals that the environment, social, and governance dimensions of ESG negatively correlate with ROA and ROE, but positively correlate with Tobin's Q. The GC's anti-corruption and environment scores exhibit a negative relationship with Tobin's Q, the human rights dimension negatively correlates with ROE and ROA, and the labor law dimension positively influences ROE. Notably, firm size amplifies these relationships, whereas firm age has a dampening effect. This research offers significant contributions to the literature by providing a comprehensive analysis of the impact of social responsibility on corporate performance based on ESG and GC scores.

Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning.

Abstract
The literature reveals that science parks offer numerous benefits and support services to the activity of a technological startup. However, the decision of choosing the best science park for the startup tends to be an informal process, technically not very rigorous and planning, arising essentially by affinities with the research centre and university. In this study, a decision support system is presented to support entrepreneurs in the process of selecting a science park for the implementation of their startup. The AHP method is used to compare the importance of the criteria for selecting a science park, which includes factors such as location, activity sector, infrastructure, cost, and size. The findings reveal that the use of this decision support system helps entrepreneurs to find a science park that is suitable for the needs of their startup and allows them to comparatively identify the most relevant criteria when choosing a science park. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

Abstract
European industrial value chains and manufacturing companies have recently faced critical challenges imposed by disruptive events related to the pandemic and associated social/political problems. Many European manufacturing industries have already recognized the importance of digitalization to increase manufacturing systems' autonomy and, consequently, become more resilient to adapt to new contexts and environments. Augmented reality (AR) is one of the emerging technologies associated with the European Industry 5.0 initiative, responsible for increasing human-machine interactions, promoting resilience through decision-making, and flexibility to deal with variability and unexpected events. However, the application and benefits of AR in increasing manufacturing resilience are still poorly perceived by academia and by European Manufacturing companies. Thus, the purpose of this paper is to contribute to the state of the art by relating the application of AR with current industrial processes towards manufacturing systems resilience. In order to cope with this objective, the industrial resilience and augmented human worker concepts are first presented. Then, through an exploratory study involving different manufacturing companies, a list of relevant disruptive events is compiled, as well as a proposal with specific ideas and functionalities on how AR can be applied to address them. In conclusion, this research work highlights the importance of AR in coping mainly with disruptive events related to Human Workforce Management and Market/Sales Management. The AR application ideas shared a common thread of availability and delivery of information to the worker at the right time, place, and format, acting on the standardization and flexibility of the work to support manufacturing resilience.

Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator.

Abstract
The NEXUS project involves innovation and entrepreneurship. The first task of the project team, which is eclectic and includes several different profiles (research assistants, researchers, managers, engineers, designers and marketing specialists) was to do a stakeholder analysis, which involved doing some field work – six unstructured interviews – as well as some secondary data analysis (scientific articles on the Scopus database; Google searches; among others). The main concerns were to: unlock value through stakeholder engagement; move from concept to collaboration – for sustainable port development; and to connect ports worldwide which will involve international stakeholder collaboration and synergies for sustainable innovation. A Stakeholder analysis is a process consisting of identifying the individuals or groups that have a specific interest in a project, which can affect them, or be affected by them, including large groups such as customers and suppliers; employees; shareholders and investors. It is necessary to sort them according to their impact and consequences regarding the project. It is essential to manage stakeholder expectations when making decisions, especially in a project with thirty-five partners and with a global reach. The initial funding is around 92 million euros (approved by the Recovery and Resilience Plan (RRP)), to ensure the ambitious implementation of the new NEXUS platform and other related products. To minimize backlash, early as well as ongoing stakeholder involvement is a prerequisite. Strategy formulation should not occur in an “ivory tower” and involvement is key. For effective and efficient decision-making, to minimize risks, which need to be identified as well as all the connections and relationships to be created and nurtured. The research question at the outset was: How may we successfully implement a multi-partner project which involves considerable investment, and which aims to digitalize maritime ports worldwide? The theme is, currently, without precedent as the impact intended is for there to be a positive impact on all stakeholders involved – a win-win situation all around. The project will finish in 31-12-2025 and will produce 28 innovative solutions for commercialization. NEXUS is a green and digital project which is up to date as regards the latest sustainability norms and regarding overall operational efficiency in multimodal logistics. Work Package 10 is a specific exploitation, internationalization, promotion, and dissemination package.