Evidence for massive methane hydrate destabilization during the penultimate interglacial warming.

The stability of widespread methane hydrates in shallow subsurface sediments of the marine continental margins is sensitive to temperature increases experienced by upper intermediate waters. Destabilization of methane hydrates and ensuing release of methane would produce climatic feedbacks amplifying and accelerating global warming. Hence, improved assessment of ongoing intermediate water warming is crucially important, especially that resulting from a weakening of Atlantic meridional overturning circulation (AMOC). Our study provides an independent paleoclimatic perspective by reconstructing the thermal structure and imprint of methane oxidation throughout a water column of 1,300 m. We studied a sediment sequence from the eastern equatorial Atlantic (Gulf of Guinea), a region containing abundant shallow subsurface methane hydrates. We focused on the early part of the penultimate interglacial and present a hitherto undocumented and remarkably large intermediate water warming of 6.8 °C in response to a brief episode of meltwater-induced, modest AMOC weakening centered at 126,000 to 125,000 y ago. The warming of intermediate waters to 14 °C significantly exceeds the stability field of methane hydrates. In conjunction with this warming, our study reveals an anomalously low δ13C spike throughout the entire water column, recorded as primary signatures in single and pooled shells of multitaxa foraminifers. This extremely negative δ13C excursion was almost certainly the result of massive destabilization of methane hydrates. This study documents and connects a sequence of climatic events and climatic feedback processes associated with and triggered by the penultimate climate warming that can serve as a paleoanalog for modern ongoing warming.

Response of dissolved inorganic carbon dynamics to simulated tidal hydrological processes in coastal wetlands.

To clarify the impacts of tidal hydrological process shifts caused by sea level rise on the blue carbon cycle, a typical coastal wetland in Jiaozhou Bay was selected for this study. The soils of Suaeda salsa (SS) and Phragmites australis (PA) wetlands were collected to simulate the effects of three types of tidal hydrological processes (Neap tide group, NT; Middle tide group, MT; Spring tide group, ST) on the soil-water dissolved inorganic carbon (DIC) dynamic. The results showed that the concentration of water dissolved inorganic carbon (WDIC) increased rapidly (115% higher) at early stage (days 0-4) under the influence of the tidal hydrological processes. Significant differences were found in WDIC concentration during different tidal hydrological processes (P < 0.05), which were expressed as MT (52.7 ± 13.3 mg L-1) > ST (52.5 ± 12.9 mg L-1) > NT (48.4 ± 10.1 mg L-1). After experiencing the tidal hydrological processes, the soil DIC content showed a net accumulation (55.1 ± 1.29 mg L-1vs. 46.7 ± 1.76 mg L-1, P < 0.001), whereas the soil inorganic carbon (SIC) decreased (2.73 ± 1.64 mg L-1vs. 4.61 ± 1.71 mg L-1), which may be attributed to the dissolution of SIC caused by the uptake of CO2 to form DIC. The accumulation of soil DIC was directly related to the SIC (λ = 1.03, P < 0.01), and indirectly related to soil nutrients (SOC substrate, λ = -0.003) and microbes (microbial biomass, λ = -0.10), and was mainly dominated by abiotic processes (abiotic: 58.1 ± 1.8% to 82.7 ± 2.46% vs. biotic: 17.4 ± 2.46% to 41.9 ± 1.76%). The increase of tidal frequency generally inhibited the accumulation of soil DIC content and promoted the output of WDIC. However, the response of soil DIC in different wetland types to tidal frequency was divergent, which was mainly regulated by the trade-off between soil nutrients and SIC content. Taken together, tidal hydrological processes and their frequency changes reshaped DIC dynamics, promoted the dissolution of SIC and the potential uptake of CO2. These findings enhance the comprehension of the inorganic carbon cycle within coastal wetlands, particularly amidst the backdrop of climate change and the rising sea levels.

A Face Detection and Standardized Mask-Wearing Recognition Algorithm.

In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods.

Divergent response of blue carbon components to wetland types and hydrological effects in typical estuarine wetlands of Jiaozhou Bay, China.

As ecosystems subject to periodic tides, estuarine wetlands have a significant capacity to sequester carbon over time. Understanding the distribution patterns of soil carbon components and identifying the key factors influencing these patterns are key to gaining insight into the function of "blue carbon" in coastal wetlands. To clarify the response of soil carbon components to wetland types and hydrological effects in estuarine wetlands, the typical estuarine wetlands in Jiaozhou Bay, China were selected as the study area, and the soil organic carbon (SOC), dissolved organic carbon (DOC), microbial biomass carbon (MBC), soil inorganic carbon (SIC) and dissolved inorganic carbon (DIC) under different wetland types and hydrological effects were investigated. The results showed that the SOC, SIC, and MBC contents were significantly influenced by the wetland types. The SOC and MBC contents were as follows: mudflat (GT) > Phragmites australis wetland (PA) > Suaeda salsa wetland (SS). The overall content of SIC was highest in PA, followed by GT and SS. Hydrological effects had significant influence on the soil MBC, DOC and DIC contents. With the increase hydrological effects, the soil MBC content decreased by 38.89%-72.22%, while the DOC and DIC contents increased by 15.13%-19.89% and 13.41%-86.70%, respectively. The results of the correlation analysis and structural equation model indicated that wetland types and hydrological effects directly or indirectly (through changes in soil pH, bulk density, water content, and salinity) drove the changes in soil carbon contents in estuarine wetlands. Altogether, our findings implied that the alterations of wetland types and hydrological effects will affect the blue carbon function of estuarine wetlands. In the future, for accurate assessment of a blue carbon budget for estuarine wetlands, the differences in wetland types and hydrological effects of different areas should be considered.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion.

In order to solve the problems of infrared target detection (i.e., the large models and numerous parameters), a lightweight detection network, MSIA-Net, is proposed. Firstly, a feature extraction module named MSIA, which is based on asymmetric convolution, is proposed, and it can greatly reduce the number of parameters and improve the detection performance by reusing information. In addition, we propose a down-sampling module named DPP to reduce the information loss caused by pooling down-sampling. Finally, we propose a feature fusion structure named LIR-FPN that can shorten the information transmission path and effectively reduce the noise in the process of feature fusion. In order to improve the ability of the network to focus on the target, we introduce coordinate attention (CA) into the LIR-FPN; this integrates the location information of the target into the channel so as to obtain more expressive feature information. Finally, a comparative experiment with other SOTA methods was completed on the FLIR on-board infrared image dataset, which proved the powerful detection performance of MSIA-Net.

An Efficient Lightweight SAR Ship Target Detection Network with Improved Regression Loss Function and Enhanced Feature Information Expression.

It is difficult to identify the ship images obtained by a synthetic aperture radar (SAR) due to the influence of dense ships, complex background and small target size, so a deep learning-based target detection algorithm was introduced to obtain better detection performance. However, in order to achieve excellent performance, most of the current target detection algorithms focus on building deep and high-width neural networks, resulting in bloated network structure and reduced detection speed, which is not conducive to the practical application of target detection algorithms. Thereby, an efficient lightweight network Efficient-YOLO for ship detection in complex situations is proposed in the present work. Firstly, a new regression loss function ECIOU is proposed to enhance the detection boxes localization accuracy and model convergence speed. Secondly, We propose the SCUPA module to enhance the multiplexing of picture feature information and the model generalization performance. Thirdly, The GCHE module is proposed to strengthen the network's ability to extract feature information. At last, the effectiveness of our method is tested on the specialized ship dataset: SSDD and HRSID datasets. The results show that Efficient-YOLO outperforms other state-of-the-art algorithms in accuracy, recall and detection speed, with smaller model complexity and model size.

BRefine: Achieving High-Quality Instance Segmentation.

Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4.

To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.

Exogenous CLASP2 protein treatment enhances wound healing in vitro and in vivo.

Proliferative and migratory abilities of fibroblasts are essential for wound healing at the skin surface. Cytoplasmic linker-associated protein-2 (CLASP2) was originally found to interact with cytoplasmic linker protein (CLIP)-170. CLASP2 plays an important role in microtubule stabilization and the microtubule-stabilizing activity of CLASP2 depends on its interactions with end binding (EB)-1 and CLIP-170. Although the microtubule-stabilizing role of CLASP2 is well established, the effects of CLASP2 on the migration and proliferation of fibroblasts remain unclear in the context of wound healing. Therefore, we tested the utilization of CLASP2 as a directly applied protein drug to improve wound healing by promoting the migration of effector cells, including skin fibroblasts, to the site of repair or injury using an in vivo excisional wound mouse model and in vitro Hs27 skin fibroblast model. Epidermal growth factor, which is a recognized contributor to cell proliferation and migration, was used as positive control. In vitro and in vivo, CLASP2 treatment significantly enhanced cell migration and accelerated wound closure. Furthermore, in vivo, the CLASP2-treated animal group displayed enhanced epidermal repair and collagen deposition. Next, we studied the mechanism of CLASP2 for wound healing. Increasing the abundance of intracellular free CLASP2 in skin fibroblasts by supplying exogenous CLASP2 seemed to stabilize microtubules through an interaction between CLASP2 and CLIP-170, as well as EB1. Exogenous CLASP2 also showed direct binding with IQGAP1, increasing both cyclic adenosine monophosphate activity and phosphorylation of glycogen synthase kinase 3ß, which in turn reinstated the binding between free CLASP2 and IQGAP1. In summary, exogenous CLASP2 increased Hs27 skin fibroblast migration by interacting with IQGAP1 and other cytoskeletal linker proteins, such as CLIP-170 and EB1. Our results strongly suggest that CLASP2 can be developed in wound healing drugs for skin repair and/or regenerating cosmetic products.

Morphology Changes of Plasma Electrolytic Oxidized Ti-6Al-4V Alloy in the Electrolytes Containing Sr and Si Ions.

In this study, morphology changes of plasma electrolytic oxidized (PEO) Ti-6Al-4V alloy in the electrolytes containing Sr and Si ions were researched using field-emission scanning electron microscope, Image J program thin film X-ray diffraction, and energy dispersive X-ray spectroscopy. Pulsed DC power supplied Ti-6Al-4V alloy was used at a 280 V for 3 minutes in the electrolyte containing Sr and Si ions. To determine cell growth, human embryonic kidney cells 293 were grown at a density of 2 × 105 cells per 1.0 ml in well plates. The pore size decreased, as the content of Sr ion increased, whereas, the number of pores per area increased. The Ca/P ratio of PEO treated in electrolyte containing high Sr concentration showed the higher than those of in electrolyte containing low Sr concentration. The peak of HA is shifted to left side, as the concentration of Sr increased. Also, the number of cells increased, as the concentration of Sr increased.

3D-printed polyether-ether-ketone/n-TiO2 composite enhances the cytocompatibility and osteogenic differentiation of MC3T3-E1 cells by downregulating miR-154-5p.

The object was to enhance the bioactivity of pure polyether-ether-ketone (PEEK) by incorporating nano-TiO2 (n-TiO2) and investigate its potential mechanism. PEEK/n-TiO2 composite was manufactured using a 3D PEEK printer and characterized by scanning electron microscopy (SEM), 3D profiler, energy-dispersive spectroscopy, and Fourier-transform infrared (FT-IR) analyses. Cytocompatibility was tested using SEM, fluorescence, and cell counting kit-8 assays. Osteogenic differentiation was evaluated by osteogenic gene and mineralized nodule levels. The expression of the candidate miRNAs were detected in composite group, and its role in osteogenic differentiation was studied. As a results the 3D-printed PEEK/n-TiO2 composite (Φ = 25 mm, H = 2 mm) was successfully fabricated, and the TiO2 nanoparticles were well distributed and retained the nanoscale size of the powder. The Ra value of the composite surface was 2.69 ± 0.29, and Ti accounted for 22.29 ± 12.09% (in weight), and FT-IR analysis confirmed the characteristic peaks of TiO2. The cells in the composite group possessed better proliferation and osteogenic differentiation abilities than those in the PEEK group. miR-154-5p expression was decreased in the composite group, and the inhibition of miR-154-5p significantly enhanced the proliferation and osteogenic differentiation abilities. In conclusion, 3D-printed PEEK/n-TiO2 composite enhanced cytocompatibility and osteogenic induction ability by downregulating miR-154-5p, which provides a promising solution for improving the osteointegration of PEEK.

Dynamic gesture recognition based on 2D convolutional neural network and feature fusion.

Gesture recognition is one of the most popular techniques in the field of computer vision today. In recent years, many algorithms for gesture recognition have been proposed, but most of them do not have a good balance between recognition efficiency and accuracy. Therefore, proposing a dynamic gesture recognition algorithm that balances efficiency and accuracy is still a meaningful work. Currently, most of the commonly used dynamic gesture recognition algorithms are based on 3D convolutional neural networks. Although 3D convolutional neural networks consider both spatial and temporal features, the networks are too complex, which is the main reason for the low efficiency of the algorithms. To improve this problem, we propose a recognition method based on a strategy combining 2D convolutional neural networks with feature fusion. The original keyframes and optical flow keyframes are used to represent spatial and temporal features respectively, which are then sent to the 2D convolutional neural network for feature fusion and final recognition. To ensure the quality of the extracted optical flow graph without increasing the complexity of the network, we use the fractional-order method to extract the optical flow graph, creatively combine fractional calculus and deep learning. Finally, we use Cambridge Hand Gesture dataset and Northwestern University Hand Gesture dataset to verify the effectiveness of our algorithm. The experimental results show that our algorithm has a high accuracy while ensuring low network complexity.

Deglacial Subantarctic CO2 outgassing driven by a weakened solubility pump.

The Subantarctic Southern Ocean has long been thought to be an important contributor to increases in atmospheric carbon dioxide partial pressure (pCO2) during glacial-interglacial transitions. Extensive studies suggest that a weakened biological pump, a process associated with nutrient utilization efficiency, drove up surface-water pCO2 in this region during deglaciations. By contrast, regional influences of the solubility pump, a process mainly linked to temperature variations, have been largely overlooked. Here, we evaluate relative roles of the biological and solubility pumps in determining surface-water pCO2 variabilities in the Subantarctic Southern Ocean during the last deglaciation, based on paired reconstructions of surface-water pCO2, temperature, and nutrient utilization efficiency. We show that compared to the biological pump, the solubility pump imposed a strong impact on deglacial Subantarctic surface-water pCO2 variabilities. Our findings therefore reveal a previously underappreciated role of the solubility pump in modulating deglacial Subantarctic CO2 release and possibly past atmospheric pCO2 fluctuations.

Hydrological control of river and seawater lithium isotopes.

Seawater lithium isotopes (δ7Li) record changes over Earth history, including a ∼9‰ increase during the Cenozoic interpreted as reflecting either a change in continental silicate weathering rate or weathering feedback strength, associated with tectonic uplift. However, mechanisms controlling the dissolved δ7Li remain debated. Here we report time-series δ7Li measurements from Tibetan and Pamir rivers, and combine them with published seasonal data, covering small (<102 km2) to large rivers (>106 km2). We find seasonal changes in δ7Li across all latitudes: dry seasons consistently have higher δ7Li than wet seasons, by -0.3‰ to 16.4‰ (mean 5.0 ± 2.5‰). A globally negative correlation between δ7Li and annual runoff reflects the hydrological intensity operating in catchments, regulating water residence time and δ7Li values. This hydrological control on δ7Li is consistent across climate events back to ~445 Ma. We propose that hydrological changes result in shifts in river δ7Li and urge reconsideration of its use to examine past weathering intensity and flux, opening a new window to reconstruct hydrological conditions.

Global reorganization of deep-sea circulation and carbon storage after the last ice age.

Using new and published marine fossil radiocarbon (14C/C) measurements, a tracer uniquely sensitive to circulation and air-sea gas exchange, we establish several benchmarks for Atlantic, Southern, and Pacific deep-sea circulation and ventilation since the last ice age. We find the most 14C-depleted water in glacial Pacific bottom depths, rather than the mid-depths as they are today, which is best explained by a slowdown in glacial deep-sea overturning in addition to a "flipped" glacial Pacific overturning configuration. These observations cannot be produced by changes in air-sea gas exchange alone, and they underscore the major role for changes in the overturning circulation for glacial deep-sea carbon storage in the vast Pacific abyss and the concomitant drawdown of atmospheric CO2.

Measurements of natural carbonate rare earth elements in femtogram quantities by inductive coupled plasma sector field mass spectrometry.

A rapid and precise standard-bracketing method has been developed for measuring femtogram quantity rare earth element (REE) levels in natural carbonate samples by inductively coupled plasma sector field mass spectrometry that does not require chemical separation steps. A desolvation nebulization system was used to effectively reduce polyatomic interference and enhance sensitivity. REE/Ca ratios are calculated directly from the intensities of the ion beams of (46)Ca, (139)La, (140)Ce, (141)Pr, (146)Nd, (147)Sm, (153)Eu, (160)Gd, (159)Tb, (163)Dy, (165)Ho, (166)Er, (169)Tm, (172)Yb, and (175)Lu using external matrix-matched synthetic standards to correct for instrumental ratio drifting and mass discrimination. A routine measurement time of 3 min is typical for one sample containing 20-40 ppm Ca. Replicate measurements made on natural coral and foraminiferal samples with REE/Ca ratios of 2-242 nmol/mol show that external precisions of 1.9-6.5% (2 RSD) can be achieved with only 10-1000 fg of REEs in 10-20 µg of carbonate. We show that different sources for monthly resolved coral ultratrace REE variability can be distinguished using this method. For natural slow growth-rate carbonate materials, such as sclerosponges, tufa, and speleothems, the high sample throughput, high precision, and high temporal resolution REE records that can be produced with this procedure have the potential to provide valuable time-series records to advance our understanding of paleoclimatic and paleoenvironmental dynamics on different time scales.

Sea level and deep-sea temperature reconstructions suggest quasi-stable states and critical transitions over the past 40 million years.

Sea level and deep-sea temperature variations are key indicators of global climate changes. For continuous records over millions of years, deep-sea carbonate microfossil-based δ18O (δc) records are indispensable because they reflect changes in both deep-sea temperature and seawater δ18O (δw); the latter are related to ice volume and, thus, to sea level changes. Deep-sea temperature is usually resolved using elemental ratios in the same benthic microfossil shells used for δc, with linear scaling of residual δw to sea level changes. Uncertainties are large and the linear-scaling assumption remains untested. Here, we present a new process-based approach to assess relationships between changes in sea level, mean ice sheet δ18O, and both deep-sea δw and temperature and find distinct nonlinearity between sea level and δw changes. Application to δc records over the past 40 million years suggests that Earth's climate system has complex dynamical behavior, with threshold-like adjustments (critical transitions) that separate quasi-stable deep-sea temperature and ice-volume states.

An image super-resolution reconstruction model based on fractional-order anisotropic diffusion equation.

The image denoising model based on anisotropic diffusion equation often appears the staircase effect while image denoising, and the traditional super-resolution reconstruction algorithm can not effectively suppress the noise in the image in the case of blur and serious noise. To tackle this problem, a novel model is proposed in this paper. Based on the original diffusion equation, we propose a new method for calculating the adaptive fidelity term and its coefficients, which is based on the relationship between the image gradient and the diffusion function. It is realized that the diffusion speed can be slowed down by adaptively changing the coefficient of the fidelity term, and it is proved mathematically that the proposed fractional adaptive fidelity term will not change the existence and uniqueness of the solution of the original model. At the same time, washout filter is introduced as the control item of the model, and a new model of image super-resolution reconstruction and image denoising is constructed. In the proposed model, the order of fractional differential will be determined adaptively by the local variance of the image. And we give the numerical calculation method of the new model in the frequency domain by the method of Fourier transform. The experimental results show that the proposed algorithm can better prevent the staircase effect and achieve better visual effect. And by introducing washout filter to act as the control of the model, the stability of the system can be improved and the system can converge to a stable state quickly.

Mechanical Properties of Dental Alloys According to Manufacturing Process.

The purpose of this study is to investigate the effect of the fabrication method of dental prosthesis on the mechanical properties. Casting was produced using the lost wax casting method, and milling was designed using a CAD/CAM program. The 3D printing method used the SLS technique to create a three-dimensional structure by sintering metal powder with a laser. When making the specimen, the specimen was oriented at 0, 30, 60, and 90 degrees. All test specimens complied with the requirements of the international standard ISO 22674 for dental alloys. Tensile strength was measured for yield strength, modulus of elasticity and elongation by applying a load until fracture of the specimen at a crosshead speed of 1.5 ± 0.5 mm/min (n = 6, modulus of elasticity n = 3). After the tensile test, the cross section of the fractured specimen was observed with a scanning electron microscope, and the statistics of the data were analyzed with a statistical program SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY, USA: IBM Corp.) and using Anova and multiple comparison post-tests (scheffe method). The yield strength was the highest at 1042 MPa at an angle of 0 degrees in the specimen produced by 3D printing method, and the elongation was the highest at 14% at an angle of 90 degrees in the specimen produced by 3D printing method. The modulus of elasticity was the highest at 235 GPa in the milled specimen. In particular, the 3D printing group showed a difference in yield strength and elongation according to the build direction. The introduction of various advanced technologies and digital equipment is expected to bring high prospects for the growth of the dental market.

Characterization of rare earth elements present in coal ash by sequential extraction.

Although it has recently been reported that notable amounts of rare earth elements (REEs) are present in the residual coal ash, little is currently known regarding the association of these elements with the coal ash matrix, thereby limiting the potential for extraction of REEs from coal ash. In this study, we analyzed the binding characteristics of REEs within coal ash via sequential extraction and examined REE recovery during a coal ash recycling process. Major components of coal ash were found to be mineral oxides, mainly composed of Si, Fe, Al, and Ca, and residual carbons. Bottom and fly ashes were found to contain 185.8 mg/kg and 179.2 mg/kg of REEs, respectively. Tessier sequential extraction confirmed that 85 % of REEs are included in the residual fraction of both bottom and fly ashes. Furthermore, BCR sequential extraction revealed that 60-70 % of REEs are contained within the residual fraction, thereby indicating that REEs are strongly bound in both bottom and fly ashes and the use of very strong acids is required for the thorough extraction of REEs from coal ash. Additionally, it was found that 46.3 % of REEs can be recovered from the wastewaters produced during the process of coal ash-derived zeolite synthesis.