Abstract
The forest-to-bioenergy supply chain is significantly vulnerable to natural disruptions, including wildfires, heavy snowfall, and windstorms. The increased occurrence of these disruptive events has caused severe challenges in forest biomass harvesting and transportation processes, which are difficult to manage. With the need to support decision-makers in designing resilient supply chains (SCs), we propose a Decision Support System (DSS) combining a two-stage stochastic programming framework with various flexibility mechanisms, such as dynamic network reconfiguration and operations postponement. The DSS incorporates an AI-based methodology to identify the most appropriate datasets and resilience metrics, capturing different supply chain dimensions (supply, demand, and operations). This integrated framework supports the selection of effective resilience-enhancing strategies to mitigate large-scale disruptions, with a particular focus on wildfires. The proposed approach is applied in a real case study in Portugal, where the most significant risk factor is wildfires. We perform computational studies and sensitivity analysis to evaluate the applicability and performance of the model and to drive managerial insights. The results show that adopting the model solutions can significantly reduce supply chain logistics and operational costs under more severe disruptive scenarios. Moreover, the results indicate up to a 60% increase in the tons of forest residues that can be removed and processed.

Abstract
The increasing complexity and distributed nature of Robot Operating System (ROS)-based robotic systems require advanced Fault Detection and Diagnosis (FDD) approaches that operate autonomously with minimal human intervention. The goal of this systematic literature review is to investigate how observability-driven FDD can be automated in ROS-based robotic systems to minimise human effort. Through this lens, the review surfaces four recurring gaps that collectively limit observability-driven automation: rich telemetry sources-logs, traces, and metrics-exist in isolation and are rarely integrated into real-time detection pipelines or leveraged collectively to improve failure diagnostics; online monitoring enables automatic fault detection but depends heavily on predefined rules and expert configuration and interpretation; failure explanations are generated post hoc and rely heavily on logs; and systems remain largely reactive, lacking the continuous monitoring infrastructure needed to anticipate faults before they propagate. Although Large Language Models (LLMs) show considerable promise for automated fault explanation and natural language interaction with robotic systems, current implementations fall short of comprehensive, real-time monitoring that unifies logs, traces, metrics, and sensor streams with Artificial Intelligence (AI) reasoning. To address these gaps, this paper motivates hybrid architectures that combine observability-first design, runtime monitoring, static analysis, and agentic LLM-based reasoning, laying the groundwork for more proactive and autonomous fault management in ROS-based systems.

Abstract
This study aims to explore the integration challenges faced by immigrant entrepreneurs in Portugal. It employed a multiple case study approach, drawing on semi-structured interviews with nine immigrant entrepreneurs from three distinct communities in Portugal. The findings of this study highlight the role of social networks in enabling and shaping the entrepreneurial journeys of immigrants in Portugal. These networks act as a bridge to help immigrants overcome barriers such as unfamiliarity with local markets, restricted access to resources, and cultural differences. In this context, community knowledge and referrals play a particularly significant role. Furthermore, the findings also identify five types of challenges faced by these communities including the financial, regulatory, social, institutional, and psychological dimensions. This study is relevant due to the role of immigrants in fostering economic growth and social cohesion. Understanding and addressing the integration challenges is key to enabling their success, which in turn strengthens local economies and promotes inclusive growth. Additionally, exploring these issues helps policymakers and organizations develop targeted strategies to support immigrant entrepreneurs, ensuring they can fully realize their potential and contribute positively to the host society.

Abstract
This paper proposes an innovative directional Benefit-of-the-Doubt (BoD) model for setting benchmarking targets on the frontier of the Production Possibility Set (PPS) in alignment with strategic goals defined a priori by experts or decision makers. The proposed model iteratively adjusts the directional vectors assigned to each Decision Making Unit (DMU) ensuring that once specific goals are achieved, further improvement efforts are directed towards indicators with remaining performance gaps. This mechanism enables a dynamic prioritization of improvement consistent with strategic objectives. Additionally, we define a Composite Indicator (CI) that measures the overall effectiveness of each DMU relative to a strategy-based reference. The CI can be decomposed into a technical score - reflecting proximity to the PPS frontier - and a strategical score - capturing the extent to which goals remain unmet upon reaching the frontier. The framework proposed is illustrated and validated through an empirical assessment of 27 European countries using 2022 data from the 'Education and Training 2030' indicators.

Abstract
This work introduces JEMA (Joint Embedding with Multimodal and multi-view Alignment), a novel co-learning framework and loss function to combine multiple sensors and process parameters in Directed Energy Deposition (DED), a critical process in metal additive manufacturing. As Industry 5.0 advances in industrial applications, effective process monitoring becomes increasingly essential. However, the limited availability of data and the black-box nature of AI solutions present significant implementation challenges in industrial settings. JEMA addresses these limitations by leveraging multimodal data, including multi-view images and process parameters, to learn transferable semantic representations. By implementing a supervised regression contrastive loss function, JEMA shapes the embedding space to enable interpretable inference. Furthermore, the framework allows for simplified hardware requirements and reduced computational overhead during deployment by utilizing only the primary on-axis sensor. We evaluate the effectiveness of JEMA loss in DED process monitoring, with particular focus on its generalization capabilities for downstream tasks such as melt pool geometry prediction without extensive fine-tuning. Our empirical results demonstrate the effectiveness of JEMA, showing improvements of 29% and 20% in multimodal and unimodal settings, respectively, compared to models without any regularization loss. Additionally, JEMA outperforms supervised contrastive learning methods by 8% and 2% in the same settings. These improvements are also accompanied by a more structured and meaningful representation in the embedding space. Importantly, the learned embedding representation provides direct interpretability of the feature space, which can be utilized by both human operators and automated systems for process optimization, control, and anomaly detection based on defined thresholds. This human-centered approach ensures that operators can actively engage with the system, making informed decisions and enhancing their trust in the process. Our framework establishes a foundation for integrating multisensor data with metadata, enabling diverse downstream applications both within manufacturing processes and beyond, while keeping human expertise central to the loop.

Abstract
This chapter explores the innovative development and integration of tailored user research instruments to inform digital health solutions for People Living with Amyotrophic Lateral Sclerosis (PALS) exhibiting characteristics of partial Locked-In Syndrome (LIS). Addressing the complex interplay of motor, cognitive, and emotional impairments typical of this population, the study proposes a synergistic framework combining three adapted instruments: the ALS Functional Rating Scale-Revised (ALSFRS-R/EX), the User Experience Questionnaire Plus (UEQ+), and a bespoke Cognitive-Motor-Emotional (CME) Observation Grid. These instruments were tailored to detect subtle variations in user function, affect, and interaction. Results show how embodied and sensory drawing participatory methods and customisation of instruments, along with semi-structured questionnaire and interviews with caregivers, can yield actionable insights for designing a model for solutions in neurodegenerative or communication-limiting contexts beyond Augmentative and Alternative Communication (AAC).