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Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications.

With the rapid development of electric vehicles (EVs), vehicle-to-grid has become a common way to participate in grid regulation. However, in the traditional vehicle-to-grid strategy, the disorganized or coercive regulatory characteristics of EVs always affect the overall satisfaction of EV users and the safe and economic operation of the distribution network. It is challenging to balance the interests of road network subjects. For this reason, this paper proposes an orderly charging and discharging strategy for electric vehicles with integrated consideration of user and distribution grid benefits. First, a comprehensive EV user satisfaction model that considers the vehicle owner’s travel costs is established by considering the vehicle’s travel status and the road resistance characteristics of the road network. Further, the EV orderly charging and discharging model is established to optimize the operation cost of the distribution network, voltage deviation, and EV users’ comprehensive satisfaction, which takes into account the vehicle owner’s satisfaction and the stable operation of the distribution network. Finally, the proposed strategy is validated using the IEEE 33-node arithmetic example. The results show that the peak-to-valley load difference of the distribution network under the strategy of this paper is 29.52% lower than that under the EV non-participation regulation strategy. Compared with the EV non-participation strategy, it can effectively reduce the single-day operation cost of the system by 2.47%.

This paper takes stock of the impact the Maritime Spatial Planning Directive 2014/89/EU has had on developing maritime spatial planning (MSP) practice in Europe. Drawing on the practical experience of 22 Member States, it analyses how countries with varying political, planning and regional contexts, as well as varying MSP experience prior to 2014, have chosen to implement the Directive and what lessons they have learned in the process. A key lesson is that while the Directive provides a normative framework for approaching MSP, this has been variously adapted to national contexts. MSP in Europe is thus characterised by varied territorial coverage of plans, different national institutional arrangements for MSP, a variety of planning processes, and a variety of sectors covered by maritime spatial plans. We then examine four topics that are likely to remain prominent in future MSP, namely: • taking account of climate change, • applying the ecosystem approach, • considering social and community impact of MSP and • improving coherence. While planners identify a range of challenges associated with each of these topics, countries have also developed practical solutions, although these are constrained by the respective remit and capacity of MSP as a process. The sheer diversity of maritime spatial plans and approaches, differing overall visions for MSP and methodological challenges, such as cumulative or socio-economic impact assessment, feature among the key challenges for achieving greater coherence in MSP within sea basins and beyond. Jacek Zaucha and Kira Gee acknowledge the support received within the eMSP NBSR (Maritime Spatial Planning – Joining forces in the North and Baltic Seas) project (EMFF-MSP-2020–101035797). Emiliano Ramieri was supported by the “National Biodiversity Future Centre – NBFC” funded under the National Recovery and Resilience Plan, by the European Union Next Generation EU, project code CN_00000033. Leila Neimane recognizes the support received within the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101034309 in the framework of the SEAS (Shaping European Research Leaders for Marine Sustainability) programme. Sigrid Eskeland Schütz acknowledges the support received within the project Designing a Refined Legal Framework for Offshore Wind in the North Sea Basin (DeWindSea) funded by Akademiaavtalen. Peer reviewed

The vehicle-to-vehicle (V2V) charging mode of charging stations solves the problem of users being unable to charge immediately due to the absence of charging piles during peak charging times. However, in blockchain-based V2V power transactions, attackers collect private information such as the payment address and transaction amount of electric vehicle owners through ledger information. This makes the relationship between electric vehicle owners and the charging behavior the object of inference attacks, resulting in user privacy disclosure and unfair trading. To solve these problems, we propose a communication scheme with privacy protection in V2V power transactions based on a linkable ring signature. We use a linkable ring signature algorithm to sign EV account addresses and payment information, ensuring the non-traceability of V2V transactions. In addition, we design a stealth address algorithm to avoid inferential attacks in V2V power transactions due to the exposure of the actual account address. The theoretical analysis proves the scheme’s security, and the experiment shows that the scheme has lower computing costs, so it is more suitable for V2V scenarios with limited computing resources.

Although dynamic, energy-efficient container-supply hybrid flow shops have attracted increasing attention, most existing research overlooks how transportation within container production affects makespan, resilience, and sustainability. To bridge this gap, we frame a resilient, energy-efficient container-supply hybrid flow shop (TDEHFSP) scheduling model that utilizes vehicle transportation to maximize operational efficiency. To address the TDEHFSP model, the study proposes a Q-learning-based multi-swarm collaborative optimization algorithm (Q-MGCOA). The algorithm integrates a time gap left-shift scheduling strategy with a machine on–off control mechanism to construct an energy-saving optimization framework. Additionally, a predictive–reactive dynamic rescheduling model is introduced to address unexpected task disturbances. To validate the algorithm’s effectiveness, 36 benchmark test cases with varying scales are designed for horizontal comparison. Results show that the proposed Q-MGCOA outperforms benchmarks on convergence, diversity, and supply-chain resilience while lowering energy utilization. Moreover, it achieves about an 8% reduction in energy consumption compared to traditional algorithms. These findings reveal actionable insights for next-generation intelligent, low-carbon container production.

To reduce hydrogen consumption by hydrogen fuel cell vehicles (HFCVs), an adaptive power-following control strategy based on gated recurrent unit (GRU) neural network operating condition recognition was proposed. The future vehicle speed was predicted based on a GRU neural network and a driving cycle condition recognition model was established based on k-means cluster analysis. By predicting the speed over a specific time horizon, feature parameters were extracted and compared with those of typical operating conditions to determine the categories of the parameters, thus the adjustment of the power-following control strategy was realized. The simulation results indicate that the proposed control strategy reduces hydrogen consumption by hydrogen fuel cell vehicles (HFCVs) by 16.6% with the CLTC-P driving cycle and by 4.7% with the NEDC driving cycle, compared to the conventional power-following control strategy. Additionally, the proposed strategy effectively stabilizes the battery’s state of charge (SOC).

High-precision wave data serve as a foundation for investigating the wave characteristics of the East China Sea (ECS) and wave energy development. Based on the simulating waves nearshore (SWAN) model, this study uses the ERA5 (ECMWF Reanalysis v5) reanalysis wind field data and ETOPO1 bathymetric data to perform high-precision simulations at a resolution of 0.05° × 0.05° for the waves in the area of 25–35° N and 120–130° E in the ECS from 2009 to 2023. The simulation results indicate that the application of the whitecapping dissipation parameter Komen and the bottom friction parameter Collins yields an average RMSE of 0.374 m and 0.369 m when compared to satellite-measured data, demonstrating its superior suitability for wave simulation in shallow waters such as the ESC over the other whitecapping dissipation parameter, Westhuysen, and the other two bottom friction parameters, Jonswap and Madsen, in the SWAN model. The monthly average significant wave height (SWH) ranges from 0 to 3 m, exhibiting a trend that it is more important in autumn and winter than in spring and summer and gradually increases from the northwest to the southeast. Due to the influence of the Kuroshio current, topography, and events such as typhoons, areas with significant wave heights are found in the northwest of the Ryukyu Islands and north of the Taiwan Strait. The wave energy flux density in most areas of the ECS is >2 kW/m, particularly in the north of the Ryukyu Islands, where the annual average value remains above 8 kW/m. Because of the influence of climate events such as El Niño and extreme heatwaves, the wave energy flux density decreased significantly in some years (a 21% decrease in 2015). The coefficient of variation of wave energy in the East China Sea exhibits pronounced regional heterogeneity, which can be categorized into four distinct patterns: high mean wave energy with high variation coefficient, high mean wave energy with low variation coefficient, low mean wave energy with high variation coefficient, and low mean wave energy with low variation coefficient. This classification fundamentally reflects the intrinsic differences in dynamic environments across various maritime regions. These high-precision numerical simulation results provide methodological and theoretical support for exploring the spatiotemporal variation laws of waves in the ECS region, the development and utilization of wave resources, and marine engineering construction.

Abstract The ecological role, bloom extent and long-term dynamics of jellyfishes are mostly overlooked due to sampling limitations, leading to the lack of continuous long-term datasets. A rise in frequency and magnitude of jellyfish invasion around the world is shedding new light on these organisms. In this study, we estimate the current and future distribution of the introduced jellyfish Blackfordia virginica in the Baltic Sea. We determine the combination of favorable levels of temperature and salinity for this species by analyzing presence/absence data from areas outside the Baltic Sea and project the distribution of suitable habitat in the Baltic Sea across different scenarios with variable climate forcing and eutrophication levels. Our results show that suitability increases with rising temperature and optimal salinity range from 13 to 20 for this species. In addition, a relatively large area of the Baltic Sea represents favorable abiotic conditions for B. virginica, enhancing the concerns on its potential range expansion. Spatial analysis illustrates that the coastal areas of the southern Baltic Sea are particularly at risk for the invasion of the species. The observation of the projection of habitat suitability across time highlights that future Baltic Sea environmental conditions increase suitability levels for B. virginica and suggest a potential expansion of its distribution in the future.