Artificial intelligence (AI) adoption in strategic sectors such as energy is often framed in optimistic narratives, yet its actual economic contribution remains under-quantified. This study proposes a novel, multi-stage methodology at the intersection of machine learning, statistics, and big data analytics to bridge this gap. First, we construct a media-derived AI Adoption Score using natural language processing (NLP) techniques, including dictionary-based keyword extraction, sentiment analysis, and zero-shot classification, applied to a large corpus of firm-related news and scientific publications. To enhance reliability, we introduce a Misinformation Bias Score (MBS)—developed via zero-shot classification and named entity recognition—to penalise speculative or biased reporting, yielding a credibility-adjusted adoption metric. Using these scores, we classify firms and apply a Fixed Effects Difference-in-Differences (FE DiD) econometric model to estimate the causal effect of AI adoption on turnover. Finally, we scale firm-level results to the macroeconomic level via a Leontief Input–Output model, quantifying direct, indirect, and induced contributions to GDP and employment. Results show that AI adoption in Romania’s energy sector accounts for up to 42.8% of adopter turnover, contributing 3.54% to national GDP in 2023 and yielding a net employment gain of over 65,000 jobs, despite direct labour displacement. By integrating machine learning-based text analytics, statistical causal inference, and big data-driven macroeconomic modelling, this study delivers a replicable framework for measuring credible AI adoption and its economy-wide impacts, offering valuable insights for policymakers and researchers in digital transformation, energy economics, and sustainable development.

In the past decades, the Erasmus+ program has been a foundation of the European educational exchanges, promoting academic and professional mobility across the EU. This paper analyzes the regional trends in Erasmus+ project funding across Europe, using heatmap visualizations to represent project density and grant distribution. The study highlights key regions with high project participation and significant grant awards, suggesting a strong concentration in Central and Western Europe, particularly in France, Germany, Benelux, UK, Spain. The integration of thematic clusters provides insights into the types of projects funded and their regional prevalence and suggests that Erasmus-funded projects are more frequent in wealthier, central European countries where there is a stronger institutional network for research and education. These findings emphasize the need for more inclusive funding strategies to support project development in underrepresented regions.

Despite growing academic interest in informality, skills, regional performance, and resilience, these domains are rarely examined together. This paper addresses this gap by offering the first integrated literature review that links all four dimensions and their policy relevance. Given the absence of indexed studies covering all concepts simultaneously, the analysis is structured around three intersecting strands: skills and regional performance or resilience; informal employment and performance or resilience; and undeclared work and skills with policy implications. Using a bibliometric approach, the study combines R/Biblioshiny analyses with a custom agentic retrieval-augmented generation (RAG) system to explore thematic trends across a large scientific corpus. Results highlight two dominant themes (human capital and entrepreneurship, and regional development and innovation) and show that informality remains weakly integrated into skills and regional analyses. Policy insights derived from the literature are classified into three tiers: evidence-based interventions, theoretically grounded but empirically underexplored measures, and emerging ideas requiring further validation. These findings point to the need for integrated strategies that link skills development, informality, and regional resilience to support inclusive and sustainable growth.

The rapid growth of Cyber-Physical Systems (CPS) has increased interest in digital twins (DTs) as virtual representations of physical systems to improve efficiency, monitoring, and security. However, as DTs become integral components of CPS, ensuring their own security also becomes critical. In this article, we present a systematic literature review (SLR) on DTs and security for CPS. Following a standard SLR methodology, we defined three research questions, performed a structured search in four publication databases complemented by forward snowballing, applied inclusion and exclusion criteria, and extracted and synthesized data from the selected studies. The review examines both how DTs are used to enhance CPS security and how DTs themselves are secured within CPS. Our main results are: (i) a classification of CPS security challenges, threats, and vulnerabilities addressed using DTs; (ii) a mapping of security mechanisms, application domains, and emerging trends in using DTs for CPS security; (iii) a classification of DT security challenges, threats, and vulnerabilities across architectural, operational, data, and trust dimensions; (iv) a synthesis of state-of-the-art approaches for securing DTs; and (v) an analysis of open research problems and future directions for advancing both CPS security through DTs and the security in DTs themselves.

The rapid expansion of artificial intelligence (AI) is documented in heterogeneous sources that differ in terminology, granularity, and temporal patterns. Existing scientometric approaches often rely on single-source datasets and flat topic labels, limiting their ability to capture multi-level conceptual change or differences between communities. This paper introduces a metric-based framework for analyzing topic trends across heterogeneous sources using hierarchical taxonomies. Rather than proposing a new taxonomy or labelling method, we show how hierarchical topic structures can support aggregation and comparison across levels of abstraction. The framework provides general metrics quantifying topic popularity, granularity, diversity, temporal dynamics, and cross-source temporal uniqueness. We illustrate the approach using three contemporary sources (arXiv, Hugging Face, and the Deep Learning Weekly newsletter) mapped to a taxonomy through an adapted labelling pipeline. The analyses highlight distinct patterns in topical focus, detail depth, and temporal development across sources. The framework is extensible and offers a foundation for scalable, and multi-source monitoring of AI’s evolving landscape. In addition, an interactive tool was developed to assist exploration of the trend analysis results.

The rapid growth of scientific publishing creates both opportunities and challenges for understanding emerging research trends. Traditional approaches to trend detection, such as citation analyses or latent topic models, often lag behind real developments or lack interpretability. To address these limitations, this study proposes a lightweight and transparent methodology for detecting and evaluating emerging scientific concepts through temporal n -gram analysis. From the OpenAlex corpus of scholarly metadata, n -grams are extracted from titles and abstracts, and aggregated into quarterly frequency series. Bursty dynamics are identified using complementary detection algorithms, including Kleinberg’s state-machine model and a moving average convergence divergence (MACD) approach. Evaluation uses a human-centered framework in which participants judge temporal plots of anonymized n -grams, providing both pairwise comparisons and graded burstiness scores. These judgments are compared with algorithmic rankings, and alignment between large language models (LLMs) and human raters is examined. We further contrast burst dynamics across fast- and slow-evolving subfields and verify the robustness of the approach to corpus-growth effects through a normalization experiment. By integrating scalable temporal analysis and multi-perspective evaluation, this work contributes a framework for monitoring and characterizing the trajectory of emerging research topics. The approach is intended as an interpretable tool for digital libraries, funding agencies, and research analysts seeking to navigate fast-evolving scientific landscapes.