The Internet of Underwater Things (IoUT) supports marine sensing, environmental monitoring, subsea inspection, and autonomous underwater operations. However, IoUT communication is constrained by limited bandwidth, long propagation delay, time-varying underwater channels, intermittent connectivity, and strict energy budgets. Semantic Communication (SC) offers a promising alternative by transmitting task-relevant meaning rather than raw data, thereby improving communication efficiency in resource-constrained underwater networks. This paper presents a critical and feasibility-aware survey of SC for IoUT, focusing on architectures, opportunities, challenges, limitations, and future research directions. We first review the fundamentals of SC-enabled IoUT systems, including semantic representations, layered architectures, impairment-aware semantic channel modeling, and task-oriented evaluation metrics. We then examine learning-driven approaches based on machine learning (ML), knowledge graphs (KGs), vision-language models (VLMs), generative models, and federated learning (FL), with emphasis on their feasibility under underwater edge and channel constraints. Representative applications, including environmental monitoring, marine ecology, subsea infrastructure inspection, disaster response, and autonomous underwater vehicle (AUV) coordination, are analyzed from an SC perspective. Finally, we identify key research directions involving standardized semantic models, reproducible testbeds, compute–communication trade-offs, trustworthy reconstruction, hybrid underwater links, energy-aware edge intelligence, semantic security, digital twins (DTs), and cross-domain interoperability. This survey provides a structured foundation for developing reliable, efficient, and meaning-driven IoUT communication systems.
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.
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.
Generative artificial intelligence (GenAI) is moving from back-end support into the creative core of advertising, raising questions about how consumers respond when brands use GenAI for visible, consumer-facing content. This study investigates the consumer backlash to GenAI advertising by analyzing 7822 YouTube and Reddit comments on Coca-Cola's AI-generated remake of its iconic Holidays Are Coming campaign. Using large-scale data collection and a LLM-assisted, researcher-validated thematic analysis, we identify a three-dimensional pattern of aversion against GenAI ads, which we organize as the CPS (Content-Psychological-Socio-ethical) framework. The framework consists of content-related judgments focused on visual realism, execution quality, and contextual fit; psychological reactions centered on authenticity, creativity, and human connection; and socio-ethical attributions involving corporate motives, labor displacement, and broader ethical concerns. The findings demonstrate that backlash against GenAI ads emerges not only from perceived inferiority to human work, but also from what the use of GenAI signals to consumers about a brand's values and intentions. Built on real-world consumer discourse from a GenAI-enabled ad campaign, the CPS framework empirically grounds how backlash develops along the three dimensions. For practitioners, the framework offers a diagnostic tool to anticipate potential backlash from using GenAI in ads.
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.
