This research introduces and optimizes a novel multi-generation power system integrating a steam Rankine cycle (SRC), a gas turbine (GT), an absorption refrigeration cycle (ARC), a proton exchange membrane (PEM) electrolyzer, and a CO2 separation unit. This system is designed to improve energy efficiency while simultaneously capturing CO2 and producing hydrogen through electrolysis. Two configurations-with and without ARC-are evaluated using a genetic algorithm-based multi-objective optimization framework, which considers exergetic efficiency, CO2 emission reduction, and total cost rate. The findings demonstrate that the proposed system improves exergetic efficiency by up to 71% and reduces CO2 emissions by up to 3.9% compared to a standalone GT system. Furthermore, the system without ARC achieves higher hydrogen production, while the system with ARC provides valuable cooling. These findings demonstrate the feasibility and environmental advantages of integrated power, CO2 capture, and H 2 blending systems for sustainable energy generation.
Accurate forecasting of renewable energy generation is essential for efficient grid management and sustainable power planning. However, traditional supervised models often require access to labeled data from the target site, which may be unavailable due to privacy, cost, or logistical constraints. In this work, we propose FreeGNN, a Continual Source-Free Graph Domain Adaptation framework that enables adaptive forecasting on unseen renewable energy sites without requiring source data or target labels. Our approach integrates a spatio-temporal Graph Neural Network (GNN) backbone with a teacher-student strategy, a memory replay mechanism to mitigate catastrophic forgetting, graph-based regularization to preserve spatial correlations, and a drift-aware weighting scheme to dynamically adjust adaptation strength during streaming updates. This combination allows the model to continuously adapt to non-stationary environmental conditions while maintaining robustness and stability. We conduct extensive experiments on three datasets: GEFCom2012, Solar PV, and Wind SCADA, encompassing multiple sites, temporal resolutions, and meteorological features. The ablation study confirms that each component-memory, graph regularization, drift-aware adaptation, and teacher-student strategy-contributes significantly to overall performance. The experiments show that FreeGNN achieves an MAE of 5.237 and an RMSE of 7.123 on the GEFCom dataset, an MAE of 1.107 and an RMSE of 1.512 on the Solar PV dataset, and an MAE of 0.382 and an RMSE of 0.523 on the Wind SCADA dataset. These results demonstrate its ability to achieve accurate and robust forecasts in a source-free, continual learning setting, highlighting its potential for real-world deployment in adaptive renewable energy systems. Project details are available at https://github.com/AraoufBh/FreeGNN.
Deep saline aquifers represents one of the most geologically promising solutions for large-scale carbon dioxide (CO2) sequestration, boasting a global storage potential of up to 10,000 Gt of CO2, which far exceeds other subsurface options. This comprehensive review systematically examines recent advancements in aquifers-based CO2 sequestration through experimental studies, simulations, pore-scale analyses, and field applications. Major trapping mechanisms, including structural, residual, solubility, and mineral trapping, are critically evaluated together with key factors affecting storage efficiency, injectivity, and long-term containment security. This study highlights the potential of CO2 foams for enhancing storage efficiency through improved mobility control and sweep efficiency within saline aquifers as overlooked by previous reviews. Additionally, it provides a detailed pore-scale analysis of CO2-brine-rock interactions, providing new insights into multiphase flow dynamics and trapping mechanisms at the microscopic level, which are crucial for accurate reservoir-scale predictions. Moreover, it explore the emerging role of microbial processes in CO2 sequestration, including their impact on mineralization and the security of long-term storage through biogeochemical interactions. In addition, the mechanical integrity of saline aquifers during CO2 injections was discussed in details as not discussed in previous reviews. A dedicated discussion of salt precipitation is presented, including deposition stages, influencing factors such as brine chemistry and injection conditions, and mitigation strategies for maintaining injectivity. The review also identifies key challenges and research gaps for futures researches related to long-term storage prediction, coupled process modeling, and economic feasibility. By integrating insights from pore-scale mechanisms to field-scale applications, this work provides a multidisciplinary framework for advancing aquifers-based CO2 sequestration as a safe and scalable climate mitigation technology.
Generative artificial intelligence (GenAI) is fundamentally reshaping the planning, development, and execution of advertising campaigns. Despite the growing application of GenAI in advertising and scholarly interest, research findings on the effects of AI-generated ads on consumer-related outcomes are mixed and rapidly evolving. In particular, the extant literature does not provide sufficient insight into how consumers process cognitive (informativeness and credibility) and affective (creativity and entertainment) values delivered by AI-generated advertisements compared with human-generated advertisements. To fill this gap, the present research used two scenario-based experiments to collect data from 405 (study 1) and 602 (study 2) tourists by randomly exposing them to human and AI-generated advertisements. Data was analyzed in two stages. First, tourists' perceptions of advertising values were compared using multivariate analysis of variance (MANOVA), and then the impact of advertising values on outcome variables was measured using structural equation modeling (SEM). The findings indicate that tourists perceive human-generated advertisements as slightly more informative, credible, creative, and entertaining than AI-generated advertisements. However, the SEM results indicate that the effects of advertising values on tourists' self-expression, attitudes toward the destination, and destination evangelism do not differ significantly between the two advertising formats. These findings are particularly noteworthy, as they suggest that advertising values that emerge from AI-generated advertisements positively influence tourists' attitudes and self-expressive responses. Consequently, the results support tourism marketing professionals in strategically leveraging emerging GenAI tools to develop advertising content that fosters favorable destination attitudes, enhances tourists’ self-expression, and ultimately stimulates destination evangelism.
Public art and culturally responsive urban environment play a crucial role in shaping national identity, particularly in culturally diverse and rapidly evolving societies like the UAE. This study examines how different forms of public art, their integration into urban spaces, and digital platforms influence perceptions of national identity. Grounded in Social Identity Theory, Place Attachment Theory, and Public Space Theory, it employs structural equation modeling (SEM) and thematic analysis using data from 543 respondents across UAE emirates. The SEM model explains 40.3% of the variance in Perceived National Identity (PNI), emphasizing the significant role of public art and digital platforms in cultural engagement. Mediation analysis highlights the importance of interactive experiences in strengthening cultural connections, while thematic analysis identifies cultural integration, community involvement, and sustainability as key themes. Findings suggest that combining traditional Emirati artistic elements with modern digital technologies enhances inclusivity and engagement. However, the limited direct impact of urban design on national identity highlights the need for culturally relevant elements to maximize its effectiveness. By integrating quantitative and qualitative insights, this study offers clear implications for policy and practice by recommending participatory planning, integration of heritage-based design elements and use of digital platforms to enhance cultural visibility and engagement. It provides a practical framework for urban planners, cultural policymakers, and designers to develop inclusive public spaces that reinforce national identity in the UAE.
