Electricity consumption in Europe has risen significantly in recent years, with households being the largest consumers of final electricity. Managing and reducing residential power consumption is critical for achieving efficient and sustainable energy management, conserving financial resources, and mitigating environmental effects. Many studies have used statistical models such as linear, multinomial, ridge, polynomial, and LASSO regression to examine and understand the determinants of residential energy consumption. However, these models are limited to capturing only direct effects among the determinants of household energy consumption. This study addresses these limitations by applying a path analysis model that captures the direct and indirect effects. Numerical and theoretical comparisons that demonstrate its advantages and efficiency are also given. The results show that Sub-metering components associated with specific uses, like cooking or water heating, have significant indirect impacts on global intensity through active power and that the voltage affects negatively the global power (active and reactive) due to the physical and behavioral mechanisms. Our findings provide an in-depth understanding of household electricity power consumption. This will improve forecasting and enable real-time energy management tools, extending to the design of precise energy efficiency policies to achieve SDG 7’s objectives.

Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.

In the 21st century, brand communication has been significantly transformed through the interaction of users and artificial intelligence (AI), who co-create and recreate texts in digital environments. This evolution challenges traditional disciplines and roles, opening new perspectives for textual production on multiple platforms. The study examines the current state and application of the textual component in brand communication, exploring its disciplinary foundations, rhetorical traces, and research methodologies. To this end, a content analysis of 97 relevant publications from 2000 to 2024 was conducted, selected for their impact on the field of brand communication and following the guidelines established in the PRISMA statement. The results identified three sources of textual creation: Organization, users and algorithms. In addition, persuasion and sentiment take precedence at the rhetorical level, while data mining stands out in message analysis. In conclusion, the advertising text, which previously prevailed in brand communication with corporate authorship, formal prefiguration and a closed entity, now expands in a media and networked context. This text originates from a multiplicity of human and automated sources, overlapping rhetorical phases and fluid textualities. The shift implies a transition from unidirectional communication, characterized by repeated impacts, to multidirectional communication with spiraling trajectories and iterative adjustments. This challenges the boundaries of genres and formats, merging the persuasiveness of rhetoric and the imagination of storytelling. This situation demands commercial policies that integrate new professionals and roles, in partnership with the educational sector, and that address copyright with AI and users.

This study examines the determinants of inflation in Tunisia from 1998 to 2023, with a particular focus on the role of fiscal policy. The study analyzes the long-run and short-run relationships between inflation and key macroeconomic variables, including government expenditure, government revenue, money supply, balance of trade, and budget deficits using ARDL model. The empirical findings reveal that budget deficits have a significant and positive impact on inflation, underscoring the critical role of fiscal imbalances in driving price instability. In contrast, government expenditure, government revenue, money supply, and balance of trade do not exhibit statistically significant long-term effects on inflation. The results highlight the importance of fiscal discipline and effective coordination between fiscal and monetary policies to achieve price stability. These findings provide valuable insights for policymakers in Tunisia and other developing economies facing similar inflationary pressures, emphasizing the need for prudent fiscal management and structural reforms to mitigate inflation volatility and ensure macroeconomic stability.