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Functional gradient materials (FGMs), as a modern group of materials, can provide multiple functions and are able to well mimic the hierarchical and gradient structure of natural systems. Because biomedical implants usually substitute the bone tissues and bone is an organic, natural FGM material, it seems quite reasonable to use the FGM concept in these applications. These FGMs have numerous advantages, including the ability to tailor the desired mechanical and biological response by producing various gradations, such as composition, porosity, and size; mitigating some limitations, such as stress-shielding effects; improving osseointegration; and enhancing electrochemical behavior and wear resistance. Although these are beneficial aspects, there is still a notable lack of comprehensive guidelines and standards. This paper aims to comprehensively review the current scenery of FGM metallic materials in the biomedical field, specifically its dental and orthopedic applications. It also introduces various processing methods, especially additive manufacturing methods that have a substantial impact on FGM production, mentioning its prospects and how FGMs can change the direction of both industry and biomedicine. Any improvement in FGM knowledge and technology can lead to big steps toward its industrialization and most notably for much better implant designs with more biocompatibility and similarity to natural tissues that enhance the quality of life for human beings.

Against the backdrop of increasingly depleted global non-renewable resources, research on renewable energy has become urgently critical. As a significant marine clean energy source, tidal current energy has attracted growing scholarly interest, effectively addressing global energy shortages and fossil fuel pollution. Semi-enclosed bay straits, with their geographically advantageous topography, offer substantial potential for tidal energy exploitation. China’s Bohai Strait exemplifies such a geomorphological feature. This study focuses on the Bohai Strait, employing the Delft3D model to establish a three-dimensional numerical simulation of tidal currents in the region. Combined with the Flux tidal energy assessment method, the tidal energy resources are evaluated, and exploitation recommendations are proposed. The results demonstrate that the Laotieshan Channel, particularly its northern section, contains the most abundant tidal energy reserves in the Bohai Strait. The Laotieshan Channel has an average power flux density of 50.83 W/m2, with a tidal energy potential of approximately 81,266.5 kW, of which about 12,189.97 kW is technically exploitable. Particularly in its northern section, favorable flow conditions exist—peak current speeds can reach 2 m/s, and the area offers substantial effective power generation hours. Annual durations with flow velocities exceeding 0.5 m/s total around 4500 h, making this zone highly suitable for deploying tidal turbines. To maximize the utilization of tidal energy resources, installation within the upper 20 m of the water layer is recommended. This study not only advances tidal energy research in semi-enclosed bay straits but also provides a critical reference for future studies, while establishing a foundational framework for practical tidal energy development in the Bohai Strait region.

Under the carbon neutrality framework, multiple coastal nuclear power plants in China have received construction approval. This development has drawn increased attention to the impact of thermal discharge on the marine environment. However, research on the diffusion effects caused by different thermal discharge configurations remains limited. This study focused on the Jinqimen Nuclear Power Plant. It employed the MIKE 3 (2014) three-dimensional numerical model, combined with field observations, to systematically investigate thermal plume dispersion. Specifically, it examined the effects of different jet angles at the discharge outlet (0°, 30°, 45°, 60°, 90°, and free diffusion conditions). The results indicate that the jet angle significantly influences the thermal rise envelope area and thermal stratification characteristics. Under free diffusion conditions (without jet velocity), the thermal rise area is the largest, with high-temperature zones concentrated near the surface. As the jet angle increases from 0° to 90°, the area of low-temperature rise gradually decreases, while the area of high-temperature rise expands. Among all tested configurations, the 30° jet angle exhibits the best overall performance. It demonstrates high thermal diffusion efficiency and strong heat dilution capacity. Moreover, it results in relatively smaller temperature rise areas at the surface, middle, and bottom layers. Additionally, tidal dynamics directly affect the thermal dispersion pattern. Smaller high-temperature rise areas are observed during peak flood and ebb tides. In contrast, heat accumulation is more likely to occur during slack tide periods. This study provides a scientific basis for optimizing the layout of nuclear power plant discharge outlets. It also serves as an important reference for mitigating thermal pollution and reducing ecological impacts of coastal nuclear power plants.

Surface wind is significant for ocean state climate, ocean mixing, and viability of wind energy techniques. However, surface wind simulated from the regional climate model generally features substantial bias from observation. For the first time, this study compares the performance of five bias correction techniques, (1) linear scaling, (2) variance scaling, (3) quantile mapping based on empirical distribution, (4) quantile mapping based on Weibull distribution, and (5) cumulative distribution functions transformation, in reducing the statistical bias of a regional climate model wind output, which was downscaled from a global climate model CNRM‐CM5 during 1991–2000. The surface wind of JRA55 reanalysis data is used as reference. Results show that all bias correction methods are consistent in reducing the climatological mean bias in spatial patterns and intensities. The linear scaling method always performs the worst among all methods in correcting higher‐order statistical biases such as skewness, kurtosis, and wind power density. The other four bias correction methods are generally similar in reducing the statistical biases of different measures based on spatial distribution maps. However, when it comes to spatial averaged mean of statistical measures over CORDEX‐East Asia in January and July, the quantile mapping based on Weibull distribution generally shows the best skills among all methods in bias reduction. Plain Language Summary In the current stage, global climate model or regional climate model simulations generally feature substantial bias relative to observations, leading to an inaccurate assessment of climate change or inaccurate inputs for impact models. For the first time, we have compared five bias correction methods using various statistical measures to find out the most robust method for correcting statistical properties of simulated winds from regional climate model. Results show that the linear scaling method always performs the worst among all methods in correcting higher‐order statistical biases of simulated winds. On average, the quantile mapping based on Weibull distribution shows the best skills among all methods in bias reduction in January and July. This study is of importance for climate change assessment of wind as well as deriving accurate wind forcing for driving ocean model. Key Points This study first assesses the skills of bias correction methods in correcting simulated winds All methods are consistent in reducing mean bias but vary in correcting higher order biases Weibull distribution‐based quantile mapping generally performs the best in bias reduction in January and July

The coastline of the Yellow River Delta has undergone continual alterations due to both natural forces and human activity. Studying these changes is crucial for promoting economic growth and preserving the region’s ecological balance. Based on imagery captured by the Landsat5 and Landsat8 satellites, this study uses statistical data from the Digital Shoreline Analysis System (DSAS), including Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR), to analyze the changes in the Yellow River Delta coastline from 2009 to 2019. This being the case, a hydrodynamic model under different shoreline conditions was established using Delft3D to compare and analyze the impact of shoreline changes on the tidal current field. From 2009 to 2019, the coastline in the study area exhibited an average movement distance of 1285m (NSM) and an annual change rate of 127.7m/a (LRR). The average increase in the area of the current estuary was 7.68km 2 /a, while the average decrease in the area of the old estuary was 4.91km 2 /a. Shoreline evolution is primarily influenced by the influx of water and sediment into the ocean. Following the implementation of water and sediment regulation, the existing estuary’s shoreline experienced a rapid initial accumulation of silt, which subsequently decelerated over time. Conversely, the former estuary has consistently undergone erosion. The greater the change in shoreline, the closer to the coast, and the greater the change in the tide. From 2009 to 2019, flow velocity and direction in the Yellow River Delta generally showed a decreasing trend, with changes ranging from 0.34% to 25.94%. The residual flow velocity near the current estuary gradually increased by about 2cm/s, while that near the abandoned old estuary gradually decreased by about 1cm/s, with no significant changes offshore. The sediment transported by the Yellow River is deposited at the current estuary, causing the coastline to move northeastward. In the abandoned estuary, there is erosion, causing the coastline to retreat.

In 2020, Yellow River runoff was more than twice as much as past years, and the proportion of strong winds was also higher than that in past years, which will inevitably lead to a change in salinity plume distribution in the Yellow River Estuary and Laizhou Bay. Based on FVCOM numerical modelling, this paper presents the spatial salinity distribution and dispersion of the Yellow River Estuary and Laizhou Bay during the wet and dry seasons in 2020. We used data from six tidal and current stations and two salinity stations to verify the model, and the results showed that the model can simulate the local hydrodynamic and salinity distribution well. The influence of river discharge and wind speed on salinity diffusion was then investigated. The simulation results showed that under the action of residual currents, fresh water from the Yellow River spread to Laizhou Bay, and the low salinity area of Laizhou Bay was mainly distributed in the northwest. The envelope area of 27 psu isohaline can account for about one-quarter of Laizhou Bay in the wet season, while the low-salinity area was only concentrated near the estuary of Yellow River in the dry season. River discharge mainly affects the diffusion area and depth of fresh water, and wind can change the diffusion structure and direction. In the wet season, with the increase in wind speed, the surface area of the plume decreased gradually, and the direction of the fresh water plume changed counterclockwise from south to north. During the dry season, the plume spread to the northwest along the nearshore. The increase in wind speed in the early stage increased the surface plume area, and the plume area decreased above a wind speed of 10 m/s due to the change in the turbulence structure. The model developed and the results from this study provide valuable information for establishing robust water resource regulations for the Yellow River. This is particularly important to ensure that the areas with low salinity in the Yellow River Estuary will not decrease and affect the reproduction of fish species.

Laizhou Bay, a semi-enclosed bay, is prone to storm surges from cold waves due to its geographic and environmental characteristics. This study uses satellite data, in situ measurements, and the MIKE numerical model to analyze storm surges along Laizhou Bay’s coast under no-dike conditions. It examines the surges caused by cold waves with different intensities and directions. This study provides the storm surge disaster risk levels along Laizhou Bay’s coast. The results show that the maximum sustained wind speed during cold waves is distributed between the NW and NE. The NE wind direction causes the most severe storm surge along Laizhou Bay. Under NE-directed cold waves with level 12 wind, the maximum risk areas for Level III and IV are approximately 1341 km2 and 1294 km2, respectively. Dongying, Shouguang, and Hanting exhibit large Level I and II risk zones. The maximum seawater intrusion distance along the Kenli coast is about 41 km. The coastal segment from Kenli to Changyi is most severely affected by storm surges. It is recommended to effectively maintain and heighten seawalls along this segment to mitigate storm surge disasters caused by strong NE winds.

When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice of these four key parameters, offering only a broad range for each one. In this paper, we investigate the applicability of tuning these four parameters within the XBeach model. We employ Generalized Likelihood Uncertainty Estimation (GLUE) to optimize the model settings. The Brier Skill Score (BSS) for each parameter combination is calculated to quantify the likelihood probability distribution of each parameter. The optimal parameter set (facua = 0.20, gamma = 0.50) was ultimately determined. Here, the facua parameter represents the degree of influence of wave skewness and asymmetry on the direction of sediment transport, while the gamma parameter represents the equivalent random wave in the wave dissipation model and is used to calculate the probability of wave breaking. Six profiles of the southern beach on Chudao Island are selected to validate the results, establishing the XBeach model based on profile measurement data before and after Typhoon “Lekima”. The results indicate that after parameter optimization, the simulation accuracy of XBeach is significantly improved, with the BSS increasing from 0.3 and 0.17 to 0.68 and 0.79 in P1 and P6 profiles, respectively. This paper provides a recommended range for parameter values for future research.

A 10-year (2003–2012) hindcast was conducted to study the wave field in the Zhe-Min coastal area (Key Area OE-W2) located off Zhejiang and Fujian provinces of China. Forced by the wind field from a weather research and forecasting model (WRF), high-resolution wave modelling using the SWAN was carried out in the study area. The simulated wave fields show a good agreement with observations. Using the simulation results, we conducted statistical analysis of wave power density in terms of spatial distribution and temporal variation. The effective duration of wave energy in the sea area was discussed, and the stability of wave energy was evaluated using the coefficient of variation of wave power density. Results indicate that the wave energy resource in the study area was about 4.11×10 6 kW. The distribution of wave energy tends to increase from the north (off Zhejiang coast) to the south (off Fujian coast), and from near-shore area to the open sea. The sea areas with wave power density greater than 2 kW/m are mostly distributed seaward of the 10-m isobath, and the contours of the wave power density are almost parallel to the shoreline. The sea areas around the islands that are far from the mainland are rich in wave energy, usually more than 6 kW/m, and therefore are of obvious advantages in planning wave energy development and utilization. The effective duration of wave energy in the offshore area shows an increasing trend from north (off Zhejiang coast) to south (off Fujian coast), with values of ∼3 500 h in the north and ∼4 450 h in the south. The coefficient of variation of wave energy in this region is mostly in the range of 1.5–3.0, and gradually decreases from the north to the south, suggesting that the wave energy in the south is more stable than that in the north.

Tidal phenomenon is a significant dynamical phenomenon in the ocean, and the accurate prediction of tide is an important task for various maritime activities. This paper proposes analysis method considering tidal periodicity and apply it to the actual tide prediction. The results prove that this method can solve the delay problem in tide prediction, improve the accuracy of prediction. Compared with the tidal harmonic analysis method, the prediction result of this method is more accurate and requires less data for short-term tidal forecast. Although this study can only provide an accurate forecast for 3 days, it is enough to deal with risks. How to improve the accuracy of long-term prediction is one of the future research directions.