Search Results (2,441)

The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores the current state of hydrogen technologies, differentiating between green, blue, and gray hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings show that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. This study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, this paper underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions. Full article

Understanding the intrinsic Li–H₂ interaction, decoupled from substrate effects, is essential to rationalize the performance of lithium-decorated hydrogen storage materials. To address the current lack of a clean theoretical baseline, we characterized the sequential H₂ adsorption on the gas-phase Li₆ superatomic cluster using high-level density functional theory (DFT), complemented by Energy Decomposition Analysis (EDA), QTAIM, and NICS(0) calculations. Li₆ acts as a structurally rigid platform (RMSD < 0.032 Å) where ligand-induced polarization progressively strengthens its σ-aromaticity (NICS(0) from −2.917 to −13.98 ppm) and increases the HOMO–LUMO gap up to 5.05 eV. EDA identifies the binding as field-activated physisorption, electrostatically dominated (65–67%) and mechanistically distinct from Kubas coordination, as confirmed by QTAIM closed-shell interaction parameters. Negative cooperativity governs an effective loading capacity of n = 2 molecules under cryogenic conditions (T_eq = 143.76 and 114.64 K), while an entropic bottleneck renders higher loading non-spontaneous at all temperatures. These results establish Li₆(H₂)_n as a foundational gas-phase reference, providing a systematic, contamination-free descriptor set for the intrinsic Li–H₂ interaction. This framework is essential for isolating the electronic role of the lithium superatom and unambiguously identifying substrate-induced modulations in supported hydrogen storage materials. Full article

Air–ground collaborative systems leverage the complementary strengths of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) and hold significant potential for logistics in complex, unstructured environments. However, trajectory planning in infrastructure-free mountainous regions remains challenging owing to the need for continuous tight coupling, obstacle avoidance, and reliable communication-link maintenance. To address these challenges, this study proposes a cooperative trajectory planning framework that enforces strict inter-vehicle distance constraints to maintain communication connectivity. By formulating the coordination problem in terms of relative configurations between air and ground vehicles, the proposed framework exhibits translational invariance, reflecting an underlying symmetry with respect to global position shifts. This symmetry-aware formulation reduces reliance on absolute coordinates and promotes consistent cooperative behavior under environmental variability. The trajectory planning problem is mathematically formulated as a constrained multi-objective nonlinear programming (MONLP) model that balances energy consumption and trajectory smoothness. An adaptive inertia weight particle swarm optimization (AIWPSO) algorithm is developed to efficiently solve the resulting optimization problem. Simulation results demonstrate that the proposed approach generates smooth, collision-free trajectories while maintaining stable air–ground coordination, demonstrating improved feasibility and robustness over conventional planning methods in unstructured mountainous environments. Full article

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks. Full article

The mechanism by which fish enhance hydrodynamic performance through collective swimming is a research hotspot in the field of underwater bionic robots. This study employs the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to conduct numerical simulations on a two-dimensional, single-degree-of-freedom (1-DOF) autonomous propulsion bionic fish swarm. It systematically investigates the effects of swarm size and inter-individual spacing on swimming speed and cost of transport (CoT) under two typical configurations: series and parallel arrangements. Findings reveal that hydrodynamic benefits are highly dependent on the spatiotemporal evolution of flow field structures. In the series configuration, an optimal spacing range of 1.5 L to 2.0 L exists within the school, where the “wake capture” effect is pronounced. Trailing fish achieve a maximum speed increase of approximately 41.1% while significantly reducing energy consumption. However, as spacing increases to 2.5 L, the cooperative gain for front and middle-row individuals rapidly diminishes, and the lead fish even experiences significant performance loss. Uniquely, the trailing fish in the four-fish formation exhibits distinct flow field reorganization and performance recovery at the 4.5 L trailing position. In the parallel formation, the “channel effect” and “blocking effect” of the fluid dominate. The study identifies 0.4 L laterally as the critical instability spacing under the investigated kinematic regime, where strong destructive interference causes a sharp deterioration in individual swimming performance. Additionally, the parallel formation exhibits pronounced positional differentiation. Central individuals, constrained by dual lateral flow fields, experience restricted lateral wake expansion and accelerated energy dissipation, resulting in significantly weaker escape capabilities from low-speed conditions compared to marginal individuals. The vortex-dynamic mechanism revealed herein provides theoretical foundations for formation control in multi-fish biomimetic cooperative systems. Full article

The development of new energy vehicles (NEVs) is key to the green and high-quality upgrading of China’s automotive industry, with the penetration rate of domestic NEV passenger cars exceeding 50%. However, deepening industry–university–research (IUR) collaborative innovation to break core technological bottlenecks remains a critical challenge. To address the limitations of existing studies—mostly focusing on dyadic interactions or hypothetical numerical simulations—this study constructs a novel tripartite evolutionary game model of NEV enterprises, university–research institutions, and the government, fully incorporating the industry’s unique attributes of high technological complexity, industrial integration, and innovation risk. Innovatively, we calibrate and verify the model using actual operation data from the Yancheng Institute of Technology–Yueda New Energy Vehicle College, bridging the gap between traditional theoretical simulation and industrial practice. The quantitative findings show that: a 40–60% balanced benefit distribution and matching cost-sharing mechanism are the core conditions for the system to reach an evolutionarily stable state; when the achievement transformation coefficient exceeds 50%, the convergence rate of stable cooperation willingness between both parties increases by over 40%; a moderate government subsidy intensity of 55% effectively accelerates the system’s positive evolution, with the incentive effect of subsidies diminishing rapidly in the mature collaboration stage; and robust collaborative innovation technology can reduce government intervention demand by more than 60%. This study enriches the theory of NEV IUR collaborative innovation, breaks the limitations of traditional research frameworks, and provides actionable references for promoting the high-quality development of the NEV industry. Full article

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol. Full article

The rapid expansion of photovoltaic (PV) power has escalated land-use conflicts, making spatial planning a complex socio-economic challenge. Existing Multi-Criteria Decision-Making (MCDM) methods often fail to reconcile top-down macro-climate goals with bottom-up local environmental and infrastructural constraints. To address this dilemma, this study proposes a novel spatial decision support framework integrating a mathematical compromise weighting method-optimizing AHP subjective policy preferences and CRITIC objective physical constraints with GIS-TOPSIS. Applied to China’s seven major geographic regions, the model accurately identifies four socio-economic evolutionary paradigms dictating PV spatial patterns. The results demonstrate that our framework effectively mitigates ecological and agricultural land squeezes, offering a robust tool for policymakers to harmonize renewable energy deployment with sustainable environmental management. Full article

The shift to renewable energy sources is widely seen as a promising way to reduce carbon emissions and mitigate the impacts of climate change. The abundance of renewable energy resources in Africa has enormous potential to reduce greenhouse gas emissions and promote climate resilience. This study conducted a bibliometric analysis of research trends in the adoption of renewable energy systems for climate change mitigation in Africa from 1993 to the first quarter of 2025. The results showed a steady growth in publications during the 2000s, with a growing annual rate of approximately 12.7%, reaching a peak in 2024, indicating increasing research interest in Africa. The thematic analysis highlights key but underdeveloped and emerging themes, including climate change mitigation, renewable energy sources, greenhouse gas assessment, climate change, energy policy, economic growth, carbon emissions, energy consumption, rural electrification, and energy transformation for further investigation. These findings also revealed regional disparities, highlighting the need to strengthen institutional capacity, develop clear long-term policies, and develop innovative financing mechanisms to expedite the deployment of renewable energy. Additionally, results from network analysis and emerging keyword detection revealed that enhanced regional and international cooperation, grid modernization, and technological innovation, such as energy storage and digital solutions, are vital in the developmental efforts to enhance optimized resource utilization and ensure energy access and security. The study thus provides insights into existing research gaps and future research directions, which will benefit policymakers, academics, and related stakeholders in their efforts to utilize Africa’s renewable energy potential to mitigate climate change, enable sustainable development, and achieve energy security throughout the continent. Full article

Galactic cosmic rays (CRs) are a fundamental non-thermal component of the interstellar medium (ISM). Understanding the transport of super-high-energy particles is essential for interpreting observations of Galactic PeVatrons. Classical diffusion models assuming a homogeneous and isothermal medium oversimplify the multiphase ISM. We utilize high-resolution three-dimensional magnetohydrodynamic simulations to self-consistently generate a multiphase ISM—comprising the warm (WNM), unstable (UNM), and cold neutral medium (CNM)—and investigate 1.5–15 PeV particle transport using a test-particle approach. We find that thermal phase transitions induce steep magnetic field strength gradients at phase boundaries, creating localized magnetic fluctuations that act as efficient sites for adiabatic mirror reflections and non-adiabatic pitch-angle scattering, strongly enhancing cross-field transport at these interfaces. However, because phase boundaries occupy only a small volume fraction and particles spend most of their trajectory in the weakly scattering WNM and UNM, the global pitch-angle scattering coefficient in the multiphase ISM is smaller than in an equivalent isothermal medium. This locally strong scattering nevertheless drives both parallel and perpendicular spatial diffusion coefficients to ∼${10}^{30}$ cm² s⁻¹ at 1.5 PeV, with the perpendicular component exceeding its isothermal counterpart (∼${10}^{28}$ cm² s⁻¹) by two orders of magnitude. Using a phase–phase diffusion matrix decomposition, we show that global CR transport is governed by the volume-filling, trans-Alfvénic WNM and UNM, where particles stream along stochastically wandering field lines. Cross-phase displacement correlations are universally positive, indicating cooperative transport between thermal phases. In contrast, the super-Alfvénic CNM acts as an efficient confinement that substantially suppresses local diffusion. Full article

This study develops a Life-Cycle Sustainability Framework for circular business models in the context of post-war economic reconstruction and sustainable value chain transformation. Ukraine is used as the main case study due to its post-war reconstruction context and the need for resource-efficient economic recovery strategies. Under conditions of disrupted supply systems, resource constraints, and structural economic change, circular economy principles are conceptualized as strategic mechanisms for enhancing resilience, resource efficiency, and long-term competitiveness rather than solely as environmental policy instruments. Building on a structured hierarchy of circular business models aligned with product life-cycle stages, the framework emphasizes value retention through functional and usage extension beyond material recovery. The framework includes a hierarchical classification of 12 circular business models and a sustainability evaluation approach based on four criteria (K1–K4), which allow for the comparative assessment of circular business models and their combinations across life-cycle stages. Using secondary statistical data and policy review as analytical inputs, the study identifies sectors with high potential for circular transformation and sustainable investment, including agriculture, energy, industry, construction, and logistics. The results indicate that circular business models applied at early life-cycle stages, such as reuse, repair, and remanufacturing, provide the highest potential for reducing resource intensity and improving long-term economic sustainability, while recycling and energy recovery play a supporting role. These findings highlight how life-cycle-oriented circular strategies can support sustainable reconstruction pathways, strengthen international cooperation, and inform policy and managerial decision-making in transitional economic contexts. Full article

The zinc–iron redox battery (ZIRB) has become one of the hot technologies of electrochemical energy storage due to its safety, stability and low cost of the electrolyte. In this paper, a collaborative control strategy for an MW-level zinc–iron flow battery energy storage system is studied, and the operation control and management of the MW-level zinc–iron flow battery energy storage system are coordinated and optimized to improve the operation efficiency of the whole system. The model of the megawatt zinc–iron flow battery energy storage system is established in this paper. A ZIRB state of charge (SOC) estimation method based on least squares (LS) and an extended Kalman filter (EKF) is proposed. Experiments under constant-current discharge show that the proposed LS-EKF method can achieve accurate SOC estimation for the tested ZIRB system, with a maximum estimation error of approximately 2.3%. Experiments show that the proposed algorithm has good accuracy, rapidity and robustness at different SOC initial values. According to SOC differences between battery cells, the coordination strategy of each cell is designed to meet the requirements of frequency modulation while taking into account the safety of battery operation. On this basis, the optimization problem is designed and solved with the goal of optimal frequency modulation effect and battery energy loss, and the collaborative control of the MW-level ZIRB energy storage system is realized. Full article

To address cooperative path planning for multiple UAVs in complex environments, this paper proposes an improved multi-agent deep deterministic policy gradient algorithm, named Prioritized Experience Multi-Agent Deep Deterministic Policy Gradient (PE-MADDPG). An urban low-altitude inspection environment is first constructed within a reinforcement-learning framework, in which dynamic constraints, safety-separation requirements, and formation-cooperation objectives are incorporated into a partially observable Markov decision process. To improve training effectiveness, prioritized experience replay is introduced to increase the utilization of informative samples, an adaptive exploration-noise strategy is designed to regulate exploration intensity, and a multi-head attention mechanism is embedded in the Critic network to enhance the representation of inter-agent interactions. Simulation results in a three-dimensional urban inspection scenario show that PE-MADDPG outperforms the selected benchmark methods in task completion rate, formation maintenance, flight efficiency, and energy consumption. These results provide an effective solution for urban low-altitude inspection tasks. Full article

Hydrocarbon-dependent economies face a critical challenge: sustaining economic growth while reducing carbon emissions. This study examines whether structural energy transition has begun to weaken the growth–emissions relationship in four Gulf Cooperation Council (GCC) economies: Saudi Arabia, United Arab Emirates, Qatar, and Bahrain, over the period 2008–2023. The analysis integrates three complementary approaches: Tapio elasticity-based decoupling analysis, a composite Energy Transition Performance Index (ETPI), and fixed-effects panel regression. This multi-method framework distinguishes between short-term cyclical decoupling and longer-term structural transition dynamics. The results show that strong decoupling is concentrated during crisis periods (2009 and 2020), indicating that emissions reductions are often cyclical rather than structural. More consistent, though moderate, weak decoupling emerges after 2015, coinciding with gradual improvements in renewable energy adoption and carbon efficiency. However, persistent fossil fuel dependence and rising electricity demand continue to constrain bigger structural change. The ETPI reveals significant cross-country variation, with the UAE demonstrating relatively stronger transition performance. Panel regression results indicate that renewable energy expansion is associated with lower carbon intensity, but its impact remains constrained by fossil-based energy systems and demand-side pressures. Overall, the findings suggest that energy transition in GCC economies is progressing but remains partial and uneven, requiring deeper structural reforms to achieve sustained decoupling. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article