Adolescence is marked by difficulties in anger regulation, which are linked to internalising and externalising problems. Despite this, the instruments currently available to measure anger regulation in adolescents have a limited evidence base and require further investigation. The present study aimed to examine the psychometric properties of the Anger Regulation Scale for Adolescents (ARS-A) in the Portuguese context. A sample of 432 adolescents aged 11 to 18 (M = 13.73, SD = 1.61) from different regions of Portugal participated in this study. The factor structure and model fit were assessed through Principal Component Analysis and Confirmatory Factor Analysis, using the Solomon method for equivalent sample splitting. All subscales demonstrated acceptable internal consistency and fit indices, confirming the original three-dimensional structure after two items were removed. Pearson correlations with other measures of behavioural and cognitive emotion regulation were consistent with theoretical expectations, although not all associations were as expected. Correlations between anger regulation, depression and anxiety corroborated the relationship between dysfunctional anger regulation and emotional distress. The ARS-A is a reliable and valid measure of anger regulation for adolescents.
This review focuses on the emerging role of multi-energy microgrids (MEMG) in addressing energy poverty in remote regions, drawing on 220 highly cited publications from 2019 to 2026. PRISMA-guided research on ScienceDirect and IEEE Xplore was conducted to explore key trends, challenges, and opportunities in implementing MEMG. The findings reveal that growing research interest is focused on MEMG that integrates multiple types of renewable sources (e.g., solar photovoltaic (PV), wind, and biomass) with various energy storage technologies to overcome intermittency and improve reliability. The review identifies four major research efforts: problem formulation and simulation (52%), experimental (10%), systematic review (24%), and state-of-the-art assessments (14%). While most of the literature focuses on technical optimisation studies, several gaps in the experimental validation, socio-economic impact assessments, and community engagement strategies were identified. The review indicates that integrating advanced energy management systems, hybrid storage solutions, and decentralised control architectures is critical to efficiently deploying MEMG in remote areas. The present review provides perspectives to leverage the synergies of renewable energy via MEMG to address energy poverty in remote communities.
Digital maturity assessments are essential for guiding industrial firms through their transformation toward Industry 4.0. This study proposes a novel composite indicator to evaluate digital maturity using a Benefit-of-the-Doubt (BoD) approach grounded in robust and conditional efficiency techniques. Leveraging a unique dataset of 490 Portuguese firms that completed a self-assessment, following the IMPULS model adapted to the Portuguese context, we constructed firm-specific scores that account for performance heterogeneity and environmental factors. The empirical results show that firms exhibit low maturity in Smart Infrastructure, Smart Products, and Data-Driven Services, while Human Resources emerges as the comparatively most developed dimension, indicating that digital transformation remains at an early stage for most firms, especially SMEs. Importantly, this work pioneers the application of Generalized Additive Models (GAMs) to explore the nonlinear influence of contextual variables, such as firm size, sector, and region, on digital maturity scores obtained via the BoD methodology. We find that conditional BoD scores differ significantly from the standard BoD and the fixed-weight IMPULS-adapted index, and that firm age category and sector exert significant effects on digital maturity, whereas financial productivity indicators do not. This innovative use of GAMs provides richer and more flexible insights than traditional parametric methods, uncovering complex interactions that affect firms' digital maturity. Our findings offer actionable guidance for policymakers and industry stakeholders, highlighting priority areas for tailored support and capacity-building interventions. The proposed framework contributes both methodologically and empirically to the evaluation of Industry 4.0 maturity and provides a replicable approach for assessing digital transformation in other national and sectoral contexts.
Global food insecurity and increasing freshwater scarcity continue to intensify as a result of population growth, urbanisation, climate variability, and the depletion of natural resources. Addressing these interconnected challenges requires a transition away from conventional, resource-intensive food systems toward sustainable protein alternatives that can deliver adequate nutrition with a reduced freshwater demand. Microalgae have emerged as a strong candidate in this context due to their rapid growth rates, broad environmental tolerance, ability to utilise carbon dioxide, and capacity to grow in brackish water, seawater, or nutrient-rich wastewater, thereby substantially lowering their blue-water footprint. While several microalgal species are already produced as single-cell protein (SCP), large-scale deployment across food, health, and industrial applications remains limited by economic, technical, and operational constraints. This review critically evaluates the potential and limitations of microalgae as a scalable solution for food and water security. Considering the increasing digitalisation of biomanufacturing, particular attention is given to the role of computational biology and artificial intelligence (AI)–enabled strategies in overcoming cultivation and process optimisation bottlenecks. Recent advances demonstrate that artificial intelligence (AI) approaches, particularly machine learning (ML), alongside Internet of Things (IoT)-based sensing, can significantly improve resource-use efficiency and nutrient recovery in microalgae production systems. In parallel, the growing application of multi-omics and systems biology tools is generating high-resolution datasets that are increasingly important for the development, validation, and deployment of robust ML models. This review distinguishes itself from previous studies by presenting an integrated perspective that links alternative protein production with environmental sustainability, particularly within the Water–Food–Energy nexus, while systematically examining ML applications across the microalgal bioprocess value chain. Key knowledge gaps, future research priorities, and the practical challenges associated with implementing AI-driven solutions in microalgae-based systems are also critically discussed. Microalgae are a sustainable alternative protein addressing food security and freshwater scarcity. Artificial intelligence, particularly machine learning-based microalgal cultivation optimization, resource efficiency, and nutrient recovery. Digital twins, IoT monitoring, and predictive control reduce economic and operational barriers to scale-up. AI-enabled bioprocessing links alternative proteins to sustainability within the Water–Food–Energy nexus.
