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.

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.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

Zinc oxide (ZnO) hollow spheres are gaining attention due to their exceptional properties and potential applications in various fields. This study investigates the impact of different zinc precursors Zinc Chloride (ZnCl₂), Zinc Nitrate [Zn(NO₃)₂], and Zinc Acetate [Zn(CH₃COO)₂] on the hydrothermal synthesis of ZnO hollow spheres. A comprehensive set of characterization techniques, including Field Emission Scanning Electron Microscopy (FE-SEM), X-ray Diffraction (XRD), Thermogravimetric analysis (TGA), and Brunauer-Emmett-Teller (BET) analysis, was utilized to assess the structural and morphological features of the synthesized materials. Our findings demonstrate that all samples exhibit a high degree of crystallinity with a wurtzite structure, and crystallite sizes range between 34 to 91 nm. Among the different precursors, ZnO derived from Zinc Nitrate showed markedly higher porosity and a well-defined mesoporous structure than those obtained from Zinc Acetate and Zinc Chloride. This research underscores the significance of precursor selection in optimizing the properties of ZnO hollow spheres, ultimately contributing to advancements in the design and application of ZnO-based nanomaterials.

The discourse on advocacy planning involving actors has not explicitly addressed the question of who the actor advocate planner is and how an actor can become an advocate planner. This paper attempts to exploring the actor advocate planner in the context of Regional Splits as, employing social network analysis as a research tool. This research employs an exploratory, mixed-methods approach, predominantly qualitative in nature. The initial phase entailed the investigation and examination of qualitative data through the acquisition of information from interviews with key stakeholders involved in Regional Splits, including communities, non-governmental organizations (NGOs), governmental entities, and political parties. The subsequent phase utilized quantitative techniques derived from the findings of the qualitative analysis, which were then analysis into the Gephi application. The findings indicate that the Regional Splits the Presidium Community represents civil society and political parties serve as crucial advocate planners, facilitating connections between disparate actors and promoting Regional Splits through political parties.