Search Results (341)

Ocean energy is an abundant, eco-friendly, and renewable energy resource that is useful for powering sensor networks connected to the maritime Internet of Things (MIoT). These sensor networks can be used to measure different marine environmental parameters that affect ocean infrastructure integrity and harm marine ecosystems. This ocean energy can be harnessed through hybrid nanogenerators that combine triboelectric nanogenerators, electromagnetic generators, piezoelectric nanogenerators, and pyroelectric generators. These nanogenerators have advantages such as high-power density, robust design, easy operating principle, and cost-effective fabrication. However, the performance of these nanogenerators can be affected by the wear of their main components, reduction of wave frequency and amplitude, extreme corrosion, and sea storms. To address these challenges, future research on hybrid nanogenerators must improve their mechanical strength, including materials and packages with anti-corrosion coatings. Herein, we present recent advances in the performance of different hybrid nanogenerators to harvest ocean energy, including various transduction mechanisms. Furthermore, this review reports potential applications of hybrid nanogenerators to power devices in marine infrastructure or serve as self-powered MIoT monitoring sensor networks. This review discusses key challenges that must be addressed to achieve the commercial success of these nanogenerators, regarding design strategies with advanced simulation models or digital twins. Also, these strategies must incorporate new materials that improve the performance, reliability, and integration of future nanogenerator array systems. Thus, optimized hybrid nanogenerators can represent a promising technology for ocean energy harvesting with application in the maritime industry. Full article

This study presents the design, fabrication, and performance assessment of a novel, small-scale (30–70 W), hybrid ocean energy system that captures energy from wave-induced heave motion using a point-absorber buoy and from ocean currents via a vertical axis water turbine (VAWT). Key innovations include a custom designed and built dual-rotor generator that accepts independent mechanical input from both subsystems without requiring complex mechanical coupling and a bi-directional mechanical motion rectifier with an overdrive. Numerical simulations using ANSYS AQWA (2024R2) and QBLADE(2.0.4) guided the design optimization of the buoy and turbine, respectively. Wave resource assessment for the Khobar coastline, Saudi Arabia, was conducted using both historical data and field measurements. The prototype was designed and built using readily available 3D-printed components, ensuring cost-effective construction. This mechanically simple system was tested in both laboratory and outdoor conditions. Results showed reliable operation and stable power generation under simultaneous wave and current input. The performance is comparable to that of existing hybrid ocean wave–current energy converters that employ more complex flywheel or dual degree-of-freedom systems. This work provides a validated pathway for low-cost, compact, and modular hybrid ocean energy systems suited for remote coastal applications or distributed marine sensing platforms. Full article

Excessive reliance on traditional energy sources such as coal, petroleum, and gas leads to a decrease in natural resources and contributes to global warming. Consequently, the adoption of renewable energy sources in power systems is experiencing swift expansion worldwide, especially in offshore areas. Floating solar photovoltaic (FPV) technology is gaining recognition as an innovative renewable energy option, presenting benefits like minimized land requirements, improved cooling effects, and possible collaborations with hydropower. This study aims to assess the levelized cost of electricity (LCOE) associated with floating solar initiatives in offshore and onshore environments. Furthermore, the LCOE is assessed for initiatives that utilize floating solar PV modules within aquaculture farms, as well as for the integration of various renewable energy sources, including wind, wave, and hydropower. The LCOE for FPV technology exhibits considerable variation, ranging from 28.47 EUR/MWh to 1737 EUR/MWh, depending on the technologies utilized within the farm as well as its geographical setting. The implementation of FPV technology in aquaculture farms revealed a notable increase in the LCOE, ranging from 138.74 EUR/MWh to 2306 EUR/MWh. Implementation involving additional renewable energy sources results in a reduction in the LCOE, ranging from 3.6 EUR/MWh to 315.33 EUR/MWh. The integration of floating photovoltaic (FPV) systems into green hydrogen production represents an emerging direction that is relatively little explored but has high potential in reducing costs. The conversion of this energy into hydrogen involves high final costs, with the LCOH ranging from 1.06 EUR/kg to over 26.79 EUR/kg depending on the complexity of the system. Full article

This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions. Full article

This study identifies the optimal location for an offshore energy island to supply sustainable power to desalination plants along the Red Sea coast. As demand for clean energy in water production grows, integrating renewables into desalination systems becomes increasingly essential. A decision-making framework was developed to assess site feasibility based on renewable energy potential (solar, wind, and wave), marine traffic, site suitability, planned developments, and proximity to desalination facilities. Data was sourced from platforms such as Windguru and RETScreen, and spatial analysis was conducted using Inverse Distance Weighting (IDW) and Multi-Criteria Decision Analysis (MCDA). Results indicate that the central Red Sea region offers the most favorable conditions, combining high renewable resource availability with existing infrastructure. The estimated regional desalination energy demand of 2.1 million kW can be met using available renewable sources. Integrating these sources is expected to reduce local CO₂ emissions by up to 43.17% and global desalination-related emissions by 9.5%. Spatial constraints for offshore installations were also identified, with land-based solar energy proposed as a complementary solution. The study underscores the need for further research into wave energy potential in the Red Sea, due to limited real-time data and the absence of a dedicated wave energy atlas. Full article

Recent studies have shown benefits of using tidal stream and wave energy in the electricity generation mix to improve supply–demand balancing on annual/subannual timeframes. This paper investigates this further by considering the variability of solar photovoltaic, onshore and offshore wind, wave, and tidal stream over multiple years. It also considers their ability to match with electricity demand when combined. Variability of demand and generation can have a significant impact on results. Over the sample of five years considered (2015–2019), demand varied by around 3%, and the availability of each renewable technology differed by up to 9%. This highlights the importance of considering multiple years of input data when assessing power system impacts, instead of relying on an ‘average’ year. It is also key that weather related correlations between renewable resources and with demand can be maintained in the data. Results from an economic dispatch model of Great Britain’s power system in 2030 are even more sensitive to the input data year, with costs and carbon emissions varying by up to 21% and 45%, respectively. Using wave or tidal stream as part of the future energy mix was seen to have a positive impact in all cases considered; 1 GW of wave and tidal (0.57% of total capacity) reduces annual dispatch cost by 0.2–1.3% and annual carbon emissions by 2.3–3.5%. These results lead to recommended best practises for modelling high renewable power systems, and will be of interest to modellers and policy makers. Full article

This paper presents a comprehensive review of handover management and load balancing optimization (LBO) in ultra-dense 5G and emerging 6G cellular networks. With the increasing deployment of small cells and the rapid growth of data traffic, these networks face significant challenges in ensuring seamless mobility and efficient resource allocation. Traditional handover and load balancing techniques, primarily designed for 4G systems, are no longer sufficient to address the complexity of heterogeneous network environments that incorporate millimeter-wave communication, Internet of Things (IoT) devices, and unmanned aerial vehicles (UAVs). The review focuses on how recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), are being applied to improve predictive handover decisions and enable real-time, adaptive load distribution. AI-driven solutions can significantly reduce handover failures, latency, and network congestion, while improving overall user experience and quality of service (QoS). This paper surveys state-of-the-art research on these techniques, categorizing them according to their application domains and evaluating their performance benefits and limitations. Furthermore, the paper discusses the integration of intelligent handover and load balancing methods in smart city scenarios, where ultra-dense networks must support diverse services with high reliability and low latency. Key research gaps are also identified, including the need for standardized datasets, energy-efficient AI models, and context-aware mobility strategies. Overall, this review aims to guide future research and development in designing robust, AI-assisted mobility and resource management frameworks for next-generation wireless systems. Full article

Returning to work after extended holidays poses significant challenges to employees’ psychological adjustment, yet this phenomenon remains underexplored in organizational research. Drawing on the Conservation of Resources (COR) theory, this study develops and tests a moderated mediation model to examine how pre-holiday work-related irritation influences post-holiday workplace vigor through heightened need for recovery, and how perceived organizational support buffers this process. Data were collected through a four-wave time-lagged design surrounding the Chinese Spring Festival, with a final sample of 349 employees across diverse industries. Results show that pre-holiday emotional strain increases employees’ recovery needs, which in turn undermines their workplace vigor. Moreover, boundary strength at home and perceived organizational support buffer the indirect negative pathway, highlighting the critical roles of both personal and organizational resources in the recovery process. By shifting attention from burnout to positive energy states such as vigor, this study advances theoretical understanding of post-holiday adjustment dynamics and offers practical insights for organizations seeking to foster employee resilience and sustained engagement after structured breaks. Full article

This study investigates an aerodynamic optimization framework inspired by marine biological morphology, utilizing the sailfish profile as a basis for airfoil configuration. Through Latin hypercube experimental design combined with optimization algorithms, four key geometric variables governing the airfoil’s hydrodynamic characteristics were systematically analyzed. Parametric studies revealed that pivotal factors including installation angle significantly influenced the fluid dynamic performance metrics of lift generation and pressure drag. Response surface methodology was employed to establish predictive models for these critical performance indicators, effectively reducing computational resource consumption and experimental validation costs. The refined bio-inspired configuration demonstrated multi-objective performance improvements compared to the baseline configuration, validating the computational framework’s effectiveness for hydrodynamic profile optimization studies. Furthermore, a coaxial dual-rotor vertical axis turbine configuration was developed, integrating centrifugal and axial-flow energy conversion mechanisms through a shared drivetrain system. The centrifugal rotor component harnessed tidal current kinetic energy while the axial-flow rotor module captured wave-induced potential energy. Transient numerical simulations employing dynamic mesh techniques and user-defined functions within the Fluent environment were conducted to analyze rotor interactions. Results indicated the centrifugal subsystem demonstrated peak hydrodynamic efficiency at a 25° installation angle, whereas the axial-flow module achieves optimal performance at 35° blade orientation. Parametric optimization revealed maximum energy extraction efficiency for the centrifugal rotor occurs at λ = 1.25 tip-speed ratio under Re = 1.3 × 10⁵ flow conditions, while the axial-flow counterpart attained optimal performance at λ = 1.5 with Re = 5.5 × 10⁴. This synergistic configuration demonstrated complementary operational characteristics under marine energy conversion scenarios. Full article

Climate change is a pressing global issue with severe consequences for the planet and human health. The Earth’s temperature has risen by 2 °C from 1901 to 2023, and this warming trend is expected to continue, causing potentially dangerous shifts in climate. Climate change impacts are already visible, with more frequent and severe heat waves, droughts, intense rain, and floods becoming increasingly common. Therefore, hydropower can contribute to addressing the global climate change issue and help to achieve global energy transition and stabilize global energy security. A Long Short-Term Memory (LSTM)-based model implemented in Python for global and regional hydropower forecasting was developed for a study period of 2023 to 2060 by taking the input data from 1980 to 2022. The results revealed that Asian countries have greater hydropower potential, which is expected to increase from 1926.51 TWh in 2023 to 2318.78 TWh in 2030, 2772.06 TWh in 2040, 2811.41 TWh in 2050, and 3195.79 TWh in 2060, as compared with the other regions of the world like the Middle East, Africa, Asia, Common Wealth of Independent State (CIS), Europe, North America, and South and Central America. The global hydropower potential is also expected to increase from 4350.12 TWh in 2023 to 4806.26 TWh in 2030, 5393.80 TWh in 2040, 6003.53 TWh in 2050, and 6644.06 TWh in 2060, which is sufficient for achieving energy transition and energy security goals. Furthermore, the performance and accuracy of the LSTM-based model were found to be 98%. This study will help in the efficient scheduling and management of hydropower resources, reducing uncertainties caused by environmental variability such as precipitation and runoff. The proposed model contributes to the energy transition and security that is needed to meet the global climate targets. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

Hybrid beamforming plays a critical role in evaluating wireless communication technology, particularly for millimeter-wave (mmWave) multiple-input multiple-out (MIMO) communication. Several hybrid beamforming systems are investigated for millimeter-wave multiple-input multiple-output (MIMO) communication. The deployment of huge grant-free transmission in the millimeter-wave (mmWave) band is required due to the growing demands for spectrum resources in upcoming enormous machine-type communication applications. Ultra-high data speed, reduced latency, and improved connection are all promised by the development of 5G mmWave networks. Yet, due to severe route loss and directional communication requirements, there are substantial obstacles to transmission reliability and energy efficiency. To address this limitation in this research we present an intelligent transmission control scheme tailored to 5G mmWave networks. Transport control protocol (TCP) performance over mmWave links can be enhanced for network protocols by utilizing the mmWave scalable (mmS)-TCP. To ensure that users have the stronger average power, we suggest a novel method called row compression two-stage learning-based accurate multi-path processing network with received signal strength indicator-based association strategy (RCTS-AMP-RSSI-AS) for an estimate of both the direct and indirect channels. To change user scenarios and maintain effective communication constantly, we utilize the innovative method known as multi-user scenario-based MATD3 (Mu-MATD3). To improve performance, we introduce the novel method of “digital and analog beam training with long-short term memory (DAH-BT-LSTM)”. Finally, as optimizing network performance requires bottleneck-aware congestion reduction, the low-latency congestion control schemes (LLCCS) are proposed. The overall proposed method improves the performance of 5G mmWave networks. Full article

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. Full article

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article

As society continues to develop, the demand for, and dependence on, energy for production and daily life activities are constantly increasing. Driven by environmental awareness and limited land resource, people have begun to reduce their dependence on fossil fuels and turn to the ocean for energy. Oceans contain vast and abundant energy resources, such as waves, tides, temperature differences and salinity gradients, all of which can be used for power generation. These resources are clean, efficient, renewable and inexhaustible, making them reliable “blue energy sources”. In addition, they are also generally not limited by land use areas, meeting the need for sustainable energy development. This article summarizes the technical characteristics of ocean energy, such as wave, tidal curre1nt, tidal, temperature difference and salinity gradient energies, and combs through the technological forms of different ocean energies, respectively. It also summarizes the development status of the ocean energy industry, and analyzes the industrial maturity of wave energy, tidal energy, etc, predicts future ocean energy development trends, and highlights the influence of ocean energy on sustainable development. We hope that this article provides a reference for scholars and institutions that dedicated to the research and development of ocean energy. Full article