Search Results (849)

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to ensure controllability and observability. A state observer for an inverter system is then developed to estimate the nominal states in the derived state-space model. The system performance is evaluated with MATLAB simulation by implementing load disturbances, which validate the effectiveness of the proposed power management control algorithm. Full article

Reliable data availability and transparent governance are fundamental requirements for distributed edge-to-cloud systems that must operate across multiple administrative domains. Conventional cloud-centric architectures centralize control and storage, creating bottlenecks and limiting autonomous collaboration at the network edge. This paper introduces a decentralized governance and service-management framework that leverages Decentralized Autonomous Organizations (DAOs) and Decentralized Applications (DApps) to to govern and orchestrate verifiable, tamper-resistant, and continuously accessible data exchange between heterogeneous edge and cloud components. By embedding blockchain-based smart contracts within swarm-enabled edge infrastructures, the approach enables automated decision-making, auditable coordination, and fault-tolerant data sharing without relying on trusted intermediaries. The proposed OASEES framework demonstrates how DAO-driven orchestration can enhance data availability and accountability in real-world scenarios, including energy grid balancing, structural safety monitoring, and predictive maintenance of wind turbines. Results highlight that decentralized governance mechanisms enhance transparency, resilience, and trust, offering a scalable foundation for next-generation edge-to-cloud data ecosystems. Full article

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

Bullfighting in the bullrings of the Iberian Peninsula, with its most direct antecedent in the Roman amphitheatre, represents an established tradition that has been exported to the Americas. Bullfighting in Portugal shares general aspects with the bullfighting culture in neighbouring Spain. However, in Portugal, particular aspects are present: there is a preference for horseback bullfighting (cavaleiros) and forcados (a special type of bullfighting), and the bull is not killed in the ring. In this work, the authors aim to contribute to the acoustic narrative of bullfighting by linking architecture with the sounds of voice, environment, music, and silence that manifest in the emblematic Campo Pequeno bullring in Lisbon, thereby providing valuable information regarding its unknown intangible acoustic heritage. The presence of a mobile roof increases the number of reflections in the bullring, leads to a more linear energy decay, and prevents the acoustic inconveniences of roofless performance venues. The 3D impulse response measurements enable an overall monaural parametric analysis, together with the analysis of the distribution of sound energy in the time–frequency domain of early reflections, to determine the acoustic signature of the venue complemented with the direction of arrival of these early reflections. Full article

Long-duration energy storage (LDES) can deliver system-wide flexibility and decarbonization benefits, yet investment is often hindered because these benefits are diffuse and not fully monetized under conventional market structures. A public-asset-oriented valuation and cost-allocation framework is proposed for LDES. First, LDES externality benefits are quantified through a system-level optimization-based simulation on a stylized aggregated regional network, with key indicators including thermal generation cost, carbon penalty, renewable curtailment cost, involuntary load shedding, and end-user electricity expenditures. Second, LDES investment costs are allocated among thermal generators, renewable operators, grid entities, and end users via a benefit-based Nash bargaining mechanism. In the case study, introducing LDES reduces thermal generation cost by 3.92%, carbon penalties by 5.59%, and renewable curtailment expenditures by 7.07%, while eliminating load shedding. The resulting cost shares are 46.9% (renewables), 28.7% (end users), 22.4% (thermal generation), and 0.5% (grid entity), consistent with stakeholder-specific benefit distributions. Sensitivity analyses across storage capacity and placement further show diminishing marginal returns beyond near-optimal sizing and systematic shifts in cost responsibility as benefit patterns change. Overall, this framework offers a scalable, economically efficient, and equitable strategy for cost redistribution, supporting accelerated LDES adoption in future low-carbon power systems. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

Rapid integration of renewable energy sources poses a serious problem to the functionality of microgrids since they are characterized by underlying uncertainties and variability. This paper proposes a multi-stage approach to energy management to overcome these issues in a virtual power plant that combines heterogeneous microgrids. The solution is based on multi-agent deep reinforcement learning to coordinate internal energy pricing, microgrid scheduling, and virtual power plant-level energy storage system management. The proposed model autonomously learns the optimal dynamic pricing strategies based on load and generation dynamics, which is efficient in dealing with operational uncertainties and maintaining microgrid privacy due to its decentralized structure. The efficiency of the proposed solution is tested on comparative simulations based on real-world data, which prove the superiority of the framework to the traditional operation modes, which are isolated microgrids and the energy sharing scenarios. The findings prove that the suggested solution has a dual beneficial impact on both virtual power plant operators and involved microgrids, as it leads to profit enhancement and, at the same time, system stability. This process facilitates the successful balancing of conflicting interests among the stakeholders at a time when the operation is low-carbon. The study offers an overall solution to dealing with complicated multi-microgrids and brings substantial changes in the integration of renewable energy, as well as the distributed management of energy resources. The framework is a scalable model that can be used in the future perspective of power systems with high-renewable penetration to address both economic and operational issues of the contemporary energy grids. Full article

The growing share of distributed energy resources in the power system increases the number of power quality issues. The variable nature of their generation contributes to voltage fluctuations. This paper proposes a method for compensating voltage fluctuations utilising reactive power generated by a doubly fed induction generator (DFIG). The proposed method was first evaluated using a simulation model developed in the Matlab Simulink R2025a environment and subsequently validated experimentally under laboratory conditions. The results obtained are highly satisfactory, with the compensation time in laboratory tests not exceeding 500 ms. Since DFIGs are used in approximately 50% of wind power plants and the implementation of the proposed approach does not require additional hardware—only modifications to the generator control software—the method appears highly promising. It offers the possibility of rapid deployment without incurring significant costs. Full article

The exponential growth in the usage of textual data across industries and data sharing across institutions underscores the critical need for frameworks that effectively balance data utility and privacy. This paper proposes an innovative agentic AI-based framework specifically tailored for textual data, integrating user-driven qualitative inputs, differential privacy, and generative AI methodologies. The framework comprises four interlinked topics: (1) A novel quantitative approach that translates qualitative user inputs, such as textual completeness, relevance, or coherence, into precise, context-aware utility thresholds through semantic embedding and adaptive metric mapping. (2) A differential privacy-driven mechanism optimizing text embedding perturbations, dynamically balancing semantic fidelity against rigorous privacy constraints. (3) An advanced generative AI approach to synthesize and augment textual datasets, preserving semantic coherence while minimizing sensitive information leakage. (4) An adaptable dataset-dependent optimization system that autonomously profiles textual datasets, selects dataset-specific privacy strategies (e.g., anonymization, paraphrasing), and adapts in real-time to evolving privacy and utility requirements. Each topic is operationalized via specialized agentic modules with explicit mathematical formulations and inter-agent coordination, establishing a robust and adaptive solution for modern textual data challenges. Full article

With the rapid growth of residential electric vehicles, synchronized charging during peak periods can induce severe load ramping and exceed distribution network capacity limits. To mitigate these issues, governments have promoted a unified build-and-operate community model that enables centralized coordination of community charging and ensures real-time responsiveness to grid dispatch signals. Targeting this emerging operational paradigm, a dual-dimensional compensation mechanism for real-time electric vehicle (EV) demand response is proposed. The mechanism integrates two types of compensation: power regulation compensation, which rewards users for providing controllable power flexibility, and state-of-charge (SoC) loss compensation, which offsets energy deficits resulting from demand response actions. This dual-layer design enhances user willingness and long-term engagement in community-level coordination. Based on the proposed mechanism, a bi-level optimization framework is developed to realize efficient real-time regulation: the upper level maximizes the active response capacity under budget constraints, while the lower level minimizes the aggregator’s total compensation cost subject to user response behavior. Simulation results demonstrate that, compared with conventional fair-share curtailment and single-compensation approaches, the proposed mechanism effectively increases active user participation and reduces incentive expenditures. The study highlights the mechanism’s potential for practical deployment in unified build-and-operate communities and discusses limitations and future research directions. Full article

Maximizing the optimal scheduling capability of a virtual power plant (VPP) over its aggregated resources is crucial for increasing its revenue. However, the limited dispatchable resources in single-energy VPPs hinder maximum economic efficiency. To address this issue, in this paper, a multienergy virtual power plant (MEVPP), which aggregates distributed electrical, thermal, and demand-side flexible resources, is introduced. Furthermore, a low-carbon economic dispatch strategy model is proposed for the coordinated operation of the MEVPP with shared energy storage. First, an MEVPP model incorporating shared energy storage is constructed, with equipment modeling developed from both electrical and thermal dimensions. Second, a low-carbon dispatch strategy that incorporates multiple types of demand responses is formulated, accounting for the effects of electrical and thermal demand responses, as well as carbon emissions, on dispatch. The simulation results demonstrate that, compared with models that do not consider the multienergy demand response, the proposed model reduces system operating costs to 54.2% and system carbon emissions to 42%. Additionally, the MEVPP can leverage energy storage by charging during low-price periods and discharging during high-price periods, thereby enabling low-carbon and economically viable system operation. This study offers valuable insights for the optimized operation of MEVPP systems. Full article

Background: Vulnerable road users (VRUs), including children and older adults, face a high risk of fatal road traffic accidents (RTAs) due to limited protection and greater injury susceptibility. Romania reports some of the highest child and elderly RTA mortality rates in the European Union. This study analyzed medico-legal autopsies from the Timisoara Institute of Legal Medicine (TILM) between 2017 and 2021 to compare fatalities in these two groups and identify key risk factors. Methods: A retrospective analysis was conducted on autopsy records of children (0–17 years) and older adults (>70 years) who died in RTAs during the study period. Data on demographics, type of road user, traumatic injuries, cause of death, and accident circumstances were extracted and supplemented by police reports. Comparative statistical analyses were performed for categorical and continuous variables. Results: Among 395 RTA autopsies, 23 (5.8%) involved children and 51 (12.9%) older adults. Most child victims were passengers (56.5%), whereas elderly fatalities occurred mainly among pedestrians (33.3%) and cyclists (25.5%), with statistically significant differences between age groups. Polytrauma was the leading cause of death in both categories, though isolated cranio-cerebral trauma was proportionally more frequent in children. Crash circumstances also showed age-related patterns, with children more involved in high-energy collisions and older adults more frequently struck as pedestrians. Survival intervals showed a similar distribution across groups. Conclusions: Child and elderly RTA fatalities in Romania share common determinants, primarily driver-related behaviors and insufficient safety measures, while also exhibiting distinct age-related vulnerabilities. Autopsy-based data highlights these patterns and can guide targeted interventions such as stricter law enforcement, public education, and infrastructure improvements. Full article