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We formulate physics-informed neural networks (PINNs) for full-field reconstruction of rotational flow beneath nonlinear periodic water waves using a small amount of measurement data, coined WaveNets. The WaveNets have two NNs to, respectively, predict the water surface, and velocity/pressure fields. The Euler equation and other prior knowledge of the wave problem are included in WaveNets loss function. We also propose a novel method to dynamically update the sampling points in residual evaluation as the free surface is gradually formed during model training. High-fidelity data sets are obtained using the numerical continuation method which is able to solve nonlinear waves close to the largest height. Model training and validation results in cases of both one-layer and two-layer rotational flows show that WaveNets can reconstruct wave surface and flow field with few data either on the surface or in the flow. Accuracy in vorticity estimate can be improved by adding a redundant physical constraint according to the prior information on the vorticity distribution.

Hybrid nanomaterials with controllable structures and diverting components have attracted significant interest in the functional materials field. Here, we develop a solvent evaporation-induced self-assembly (EISA) strategy to synthesize nanosheet-assembled phosphomolybdic acid (H3PMo)-alumina hybrid hollow spheres. The resulting nanoflowers display a high surface area (up to 697 m2g–1), adjustable diameter, high chemical/thermal stability, and especially molecularly dispersed H3PMo species. By employing various microscopic and spectroscopic techniques, the formation mechanism is elucidated, revealing the simultaneous control of the morphology by heteropoly acids and water through the water-induced Kirkendall effect. The versatility of the synthesis method is demonstrated by varying surfactants, heteropoly acids, and metal oxide precursors for the facile synthesis of hybrid metal oxides. Spherical hybrid alumina serves as an attractive support material for constructing metal–acid bifunctional catalysts owing to its advantageous surface area, acidity, and mesoporous microenvironment. Pt-loaded hollow flowers exhibit excellent catalytic performance and exceptional stability in the hydrodeoxygenation of vanillin with recyclability for up to 10 cycles. This research presents an innovative strategy for the controllable synthesis of hybrid metal oxide nanospheres and hollow nanoflowers, providing a multifunctional platform for diverse applications.

Introduction: Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy. Materials and methods: Using a prospectively recruited cohort of patients with/without AAA (n= 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers. Results: Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86. Conclusions: Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.

Background: Thrombolysis for arteriovenous grafts (AVG) yields high technical success rates, however, long-term outcomes are unclear. We conducted a multicenter retrospective cohort study to analyze 5-year patency rates following AVG thrombolysis.Methods: All patients who underwent AVG thrombolysis between 2005 and 2015 at three academic hospitals were included. Prospectively maintained institutional nephrology and radiology databases were used to record demographic, clinical, and AVG characteristics. The primary outcome was primary patency, defined as AVG access survival without re-intervention including angioplasty ± stent with/without re-thrombolysis. Secondary outcomes were assisted primary patency and cumulative patency, defined as AVG access survival until re-thrombosis requiring re-thrombolysis or abandonment, respectively. Technical success was defined as restoration of flow with <30% residual stenosis. Patients were followed until 2017. Patency rates were assessed using Kaplan–Meier survival analysis and Cox proportional hazards were calculated to determine associations between covariates and patency loss.Results: Seventy-four patients underwent AVG thrombolysis during the study period with a median follow-up period of 21.4 (IQR 8.3–42.8) months. The average age was 58.6 years with a high rate of comorbidities, including hypertension (82.4%) and diabetes (54.1%). Thrombolysis technical success was 96%. There were 147 re-interventions in 46 patients, of which 98 were re-thrombolysis (mean re-intervention rate of 1.27/patient/year). Primary patency at 1, 3, and 5 years were 43.2%, 20.2%, and 7.7%. Assisted primary patency at 1, 3, and 5 years were 47.5%, 20.2%, and 7.7%. Cumulative patency at 1, 3, and 5 years were 75.0%, 38.8%, and 22.6%. Cox proportional hazards analysis demonstrated no associations between demographic, clinical, and procedural characteristics and patency rates.Conclusions: Despite a high technical success rate, thrombolysis for AVG dysfunction is associated with poor long-term patency. Future studies are needed to determine risk factors for re-thrombosis to identify patients who will benefit from AVG thrombolysis in the long-term.

Thermoset epoxy resin materials can be endowed with unique dynamic properties by introducing dynamic covalent bonds (DCB) into the cross‐link network of the epoxy resin. However, it remains a challenge to obtain epoxy materials with high mechanical, thermodynamic, and dynamic properties. In this work, a series of epoxy vitrimers with double DCBs were prepared by using a curing agent with imine bonds (PAMD) and an epoxy resin with disulfide bonds (BGPDS). PAMD and BGPDS are cross‐linked and form a uniform aromatic framework that ensures the mechanical and thermal properties of the material. High‐density DCBs ensure the dynamic performance of epoxy vitrimer. In addition, tetra‐needle‐like ZnO whiskers (T‐ZnOw) were added to the epoxy vitrimer for further reinforcement. The results demonstrated that epoxy vitrimer composites with T‐ZnOw content of 10 wt.% exhibit high tensile strength, high Tg, high healing efficiency, shape reconfiguration, reprocessing and rapid degradation. The degradation products can recycle and produce new epoxy vitrimer, leading to a closed‐loop recycling which reduced the environmental pollution and resource waste.

To solve the problem of insufficiently high sensitivity of surface plasmon resonance (SPR) sensor, CeF $_{\mathbf {{3}}}$ material is applied to fiber SPR sensor in this study for the first time, and a CeF $_{\mathbf {{3}}}$ -assisted SPR refractive index (RI) sensor on the basis of multimode–single-mode–multimode (MSM) fiber structure has been proposed and designed. The theoretical model of sensor with novel structure is presented. Both simulations and experiments are included in this study. The consequences of numerical simulation demonstrate that CeF $_{\mathbf {{3}}}$ can greatly enhance the sensitivity. It can be seen that the sensitivity of structure in the RI range of 1.33–1.37 can reach up to 8678.83 nm/RIU according to the experimental results. When comparing with the Au film SPR sensor structure, the sensitivity is significantly increased, reaching 4.08 times of traditional Au film SPR sensor. The temperature sensitivity test and repeatability test show that the sensor is not responsive to temperature between 20 °C and 60 °C, which is suitable for use in a wide range of environmental conditions and has good repeatability. CeF $_{\mathbf {{3}}}$ has the advantages of good biocompatibility and no damage to biological tissues, so the novel sensor that has been proposed has a broad and potential future in biological detection.

Recently, tensor singular value decomposition (t-SVD), based on the tensor-tensor product (t-product), has become a powerful tool for processing third-order tensor data. However, constrained by the fact that the basic element is the fiber (i.e., vector) in the t-product, higher-order tensor data (i.e., order $d>3$) are usually unfolded into third-order tensors to satisfy the classical t-product setting, which leads to the destroying of high-dimensional structure. By revisiting the basic element in the t-product, we suggest a generalized t-product called element-based tensor-tensor product (elt-product) as an alternative of the classic t-product, where the basic element is a $(d-2)$th-order tensor instead of a vector. The benefit of the elt-product is that it can better preserve high-dimensional structures and that it can explore more complex interactions via higher-order convolution instead of first-order convolution in classic t-product. Starting from the elt-product, we develop new tensor-SVD and low-rank tensor metrics (e.g., rank and nuclear norm). Equipped with the suggested metrics, we present a tensor completion model for high-order tensor data and prove the exact recovery guarantees. To harness the resulting nonconvex optimization problem, we apply an alternating direction method of the multiplier (ADMM) algorithm with a theoretical convergence guarantee. Extensive experimental results on the simulated and real-world data (color videos, light-field images, light-field videos, and traffic data) demonstrate the superiority of the proposed model against the state-of-the-art baseline models.

Accurate low-contrast defect detection has become a common bottleneck to further improve the performance of automated visual inspection (AVI) instruments. Inspired by visual crypsis, a novel concept of camouflaged defect has been proposed to assist surface defect detection, and then, a camouflaged defect detection network (CDDNet) was proposed. To be specific, a new inception dynamic texture enhanced module (IDTEM) was proposed to aggressively strengthen the indefinable boundaries and deceptive textures. To further explore spatial information over long distance, a lightweight recurrent decoupled fully connected attention (RDFCA) is designed with cost-effective computation. Finally, a new adaptive scale-equalizing pyramid convolution (ASEPC) was designed to achieve cross-scale feature fusion by exploiting the inter-layer feature correlation. The proposed CDDNet obtained competitive mean average precision (mAP) of 84.2%, 96.7%, and 67.1%, respectively, on three public datasets of NEU-DET, DAGM, and CAMO, when compared with state-of-the-arts.

Deep learning (DL) has demonstrated remarkable performance in the classification of hyperspectral images (HSIs) by leveraging its powerful ability to automatically learn deep spectral–spatial features over the years. Nevertheless, the limited supervisory signals along with a vast number of parameters in deep models still pose critical challenges when utilizing a restricted number of samples for training deep networks. To better handle this issue, this article proposes an end-to-end framework called guided group contrastive learning (GGCL) that adaptively integrates unsupervised information into a supervised contrastive learning framework. The proposed method employs a similarity-guided module that measures the spectral–spatial similarity of unsupervised samples based on supervised signals and effectively groups them. Then, the similarity signals of both supervised and unsupervised data are combined with contrastive learning to achieve intragroup feature aggregation and intergroup feature separation with guided group contrastive loss (GGCLoss). The pivotal characteristic of the proposed method lies in the end-to-end incorporation of unsupervised information with supervised signals for contrastive learning. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than existing state-of-the-art (SOTA) methods. For ease of reproducibility, the code of the proposed GGCL will be publicly available at https://github.com/fanerlight/GGCL_HSI.

To get rid of the dilemma between the speed and the resolution of optical fiber Fabry–Perot (F-P) magnetic field sensor (MFS) demodulation algorithm and to address the problem of difficult detection in the low magnetic field area, the compact parallel dual neural network (PDNN) with wavelet decomposition (WD) was proposed for the first time to demodulate the magnetic field. PDNN consists of the prediction neural network (PNN) and the error compensation neural network (ECNN). PNN is used to predict the true magnet field and ECNN is designed to predict the error between the true value and predictive value. With the combination of the dual networks, PDNN achieves a higher accuracy and resolution compared with traditional series single networks. In addition, to reduce the computational cost of PDNN, a dimensionality reduction method based on WD is developed. It is demonstrated according to simulation and experiments that PDNN obtains stronger anti-interference capability, less computational cost, and higher demodulation resolution after the process of WD. Three-layer WD is used to extract the features of F-P MFS spectral signal, which can keep 13.8% signal energy and reduce the spectral data points by about eight times. PDNN with WD achieves 0.004 Gs demodulation resolution with 1 kHz in low magnetic field, which is applicable to all fields requiring high speed and resolution.

SiGe/Si multilayer is the core structure of the active area of gate-all-around field-effect transistors and semiconductor quantum computing devices. In this paper, high-quality SiGe/Si multilayers have been grown by a reduced-pressure chemical vapor deposition system. The effects of temperature, pressure, interface processing (dichlorosilane (SiH2Cl2, DCS) and hydrogen chloride (HCl)) on improving the transition thickness of SiGe to Si interfaces were investigated. The interface quality was characterized by transmission electron microscopy/atomic force microscopy/high-resolution X-ray diffraction methods. It was observed that limiting the migration of Ge atoms in the interface was critical for optimizing a sharp interface, and the addition of DCS was found to decrease the interface transition thickness. The change of the interfacial transition layer is not significant in the short treatment time of HCl. When the processing time of HCl is increased, the internal interface is optimized to a certain extent but the corresponding film thickness is also reduced. This study provides technical support for the acquisition of an abrupt interface and will have a very favorable influence on the performance improvement of miniaturized devices in the future.

Ralstonia solanacearumcauses a lethal bacterial wilt disease in many crops, leading to huge losses in crop production every year. Understanding of plant–R. solanacearuminteractions will aid to develop efficient strategies to control the disease. As a soilborne pathogen, R. solanacearumnaturally infects plants viaroots. A huge limitation in studying plant–R. solanacearuminteractions is the large variation of R. solanacearuminfection assay due to the variable soil conditions and uneven inoculum exposure. Here, we developed a robust and reliable Petri-dish inoculation method which allows consistent and stable infection in young plant seedlings. This method is easy to use, takes about only 10 days from seed germination to the completion of inoculation assay, and requires less inoculum of bacteria as well as growth chamber space. We proved the efficacy of the seedling Petri-dish inoculation method by analyzing plant defense primed by molecular patterns, resistance of defense-related plant mutants, and virulence of R. solanacearummutants. Furthermore, we demonstrated that the seedling Petri-dish inoculation method can be applied to other host plants such as tobacco and has great potential for high-throughput screening of resistant plant germplasms to bacterial wilt in the future.

Optical metasurfaces are widely used in the field of sense due to their small size, freedom of design, and easy manipulation of the light field. At present, researchers usually focus on either sensitivity or full-width at half-maximum (FWHM) to pursue better sensor performance. Metal materials are used to excite the surface plasmon resonance (SPR) to improve the sensitivity, or all-dielectric materials are used to reduce the FWHM, but they are mutually constraining. To solve this problem, a double rectangular structure of the metasurface is proposed. Through structural parameter design, the metasurface can generate three types of peaks: guided mode resonance (GMR), Fano resonance, and quasi-bound states in the continuums (Q-BIC). The surface electric field intensity of the metasurface is enhanced after coating tungsten disulfide (WS2), which is combined with the characteristics of Q-BIC high-quality factor and achieves good results in refractive index sensing. Through simulation calculation, the surface electric field intensity of the sensor is increased by about three times, and the sensitivity is increased to 107.5 nm/RIU, which is 2.5 times more than without WS2. The FWHM remains at less than 0.01 nm and the figure of merit (FOM) of the sensor increased by 1.9 times. The sensor effectively limits the increase of FWHM while improving the sensitivity; it has great potential in the detection of trace substances and biosensing.

The synthesis of polymeric materials that simultaneously possess multiple outstanding mechanical properties and self-healability is always a great challenge. Herein, we report the synthesis of a robust and self-healing polyurea material, which contains a dual metal coordination structure of Zn(II)-urea and Zn(II)-diamidepyridine as well as dense hydrogen bonds. Due to the synergistic enhancement of dynamic coordination bonds and hydrogen bonds in the supramolecular network, the obtained polyurea materials possess a combination of excellent mechanical properties and high-efficiency self-healing properties, which are adjustable by varying the content of Zn(II) ions. The optimal PU-L/Zn-3 sample has a tensile strength of ∼22.82 MPa, while the elongation at break is ∼1312% and the toughness is ∼222.68 MJ/m3. In addition, the fully cut sample exhibits a high healing efficiency of 95.7% based on toughness after treatment at 50 °C for 4 h. Multi-dynamic cross-linked polymers might provide a new way to obtain robust self-healing materials for protection materials and wearable electronics.

Mismatch repair (MMR) deficiency has been linked to thiopurine resistance and hypermutation in relapsed acute lymphoblastic leukemia (ALL). However, the repair mechanism of thiopurine-induced DNA damage in the absence of MMR remains unclear. Here, we provide evidence that DNA polymerase β (POLB) of base excision repair (BER) pathway plays a critical role in the survival and thiopurine resistance of MMR-deficient ALL cells. In these aggressive resistant ALL cells, POLB depletion and its inhibitor oleanolic acid (OA) treatment result in synthetic lethality with MMR deficiency through increased cellular apurinic/apyrimidinic (AP) sites, DNA strand breaks and apoptosis. POLB depletion increases thiopurine sensitivities of resistant cells, and OA synergizes with thiopurine to kill these cells in ALL cell lines, patient-derived xenograft (PDX) cells and xenograft mouse models. Our findings suggest BER and POLB’s roles in the process of repairing thiopurine-induced DNA damage in MMR-deficient ALL cells, and implicate their potentials as therapeutic targets against aggressive ALL progression.

The phase equilibria of the Cu—Nb—Si system were investigated via a combination of key equilibrated alloys, thermodynamic modeling and first-principles calculations. Sixteen ternary alloys were prepared to determine the isothermal sections at 600 and 700 °C, by means of X-ray diffraction (XRD) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM−EDS). The three- and two-phase regions were determined. The existence of ternary compound τ1(Cu4Nb5Si4) was confirmed. The solubilities of Cu in the NbSi2and Nb5Si3phases were measured. Based on the experimental equilibria data from the literature and the present work, a thermodynamic description of the Cu—Nb—Si system was carried out by using the calculation of phase diagrams (CALPHAD) method supported by first-principles calculations. The substitutional model and sublattice model were employed to describe the solution phases and intermediate phases, respectively. A set of self-consistent thermodynamic parameters of the Cu—Nb—Si system were conclusively obtained. Most of the reliable experimental data were reproduced by the present thermodynamic modeling.

Grinding with cubic boron nitride (CBN) superabrasive is a widely used method of machining superalloy in aerospace industries. However, there are some issues, such as poor grinding quality and severe tool wear, in grinding of powder metallurgy superalloy FGH96. In addition, abrasive wheel wear is the significant factor that hinders the further application of CBN abrasive wheels. In this case, the experiment of grinding FGH96 with single CBN abrasive grain using different parameters was carried out. The wear characteristics of CBN abrasive grain were analyzed by experiment and simulation. The material removal behavior affected by CBN abrasive wear was also studied by discussing the pile-up ratio during grinding process. It shows that morphological characteristics of CBN abrasive grain and grinding infeed direction affect the CBN abrasive wear seriously by simulation analysis. Attrition wear, micro break, and macro fracture had an important impact on material removal characteristics. Besides, compared with the single cutting edge, higher pile-up ratio was obtained by multiple cutting edges, which reduced the removal efficiency of the material. Therefore, weakening multiple cutting edge grinding on abrasive grains in the industrial production, such as applying suitable dressing strategy, is an available method to improve the grinding quality and efficiency.

Previous studies have demonstrated significant sex differences in vascular surgery outcomes. We assessed stroke or death rates following carotid endarterectomy (CEA) in women versus men.

Objective: Numerous schizophrenic patients are suffering from obesity primarily attributed to antipsychotic medication and poor dietary habits. This study investigated the progressive deterioration of olanzapine-induced metabolic disorders in the presence of a high-fat diet (HFD) and explored the involvement of endoplasmic reticulum (ER) stress. Methods: Female Sprague-Dawley rats fed on a standard chow diet or HFD were treated with olanzapine (3 mg/kg/day) and the ER stress inhibitor 4-phenylbutyric acid (4-PBA, 1 and 0.5 g/kg/day) for 8 days. Changes in body weight, food intake, and plasma lipids were assessed. Hepatic fat accumulation was evaluated using oil red O staining. Western blotting and immunofluorescence assays were employed to examine the expression of ER stress markers, NOD-like receptor pyrin domain-containing protein 3 (NLRP3), and proopiomelanocortin (POMC) in the hypothalamus or liver. Results: Compared to olanzapine alone, olanzapine+HFD induced greater weight gain, increased hyperlipidemia, and enhanced hepatic fat accumulation (P&lt;0.05). Co-treatment with 4-PBA exhibited a dose-dependent inhibition of these effects (P&lt;0.05). Further mechanistic investigations revealed that olanzapine alone activated ER stress, upregulated NLRP3 expression in the hypothalamus and liver, and downregulated hypothalamic POMC expression. The HFD exacerbated these effects by 50%–100%. Moreover, co-administration of 4-PBA dose-dependently attenuated the olanzapine+HFD-induced alterations in ER stress, NLRP3, and POMC expression in the hypothalamus and liver (P&lt;0.05). Conclusion: HFD worsened olanzapine-induced weight gain and lipid metabolic disorders, possibly through ER stress-POMC and ER stress-NLRP3 signaling. ER stress inhibitors could be effective in preventing olanzapine+HFD-induced metabolic disorders.

The rapid growth of online car hailing provides an excellent opportunity to provide convenient travel services. However, with the tremendous increase of users and online taxis, online car-hailing prediction systems face several challenges: 1) the difficulty of modeling nonlinear spatiotemporal interactions between users and vehicles, 2) the difficulty of incorporating context information and multimodal attribute enhancement data, and 3) the problems of data sparsity. To cope with these challenges, we propose a novel multimodal fusion graph convolutional network (MFGCN) for online car-hailing prediction. The model consists of a multimodal origin destination graph convolutional network module that contains three graph convolutional networks to extract spatial patterns from geography, semantics, and functional correlation; a multimodal attribute enhancement module that incorporates weather and temporal activity patterns; and a temporal attention skip-long short-term memory module that captures the periodic variations. Extensive experiments conducted on real-world taxi demand datasets show that MFGCN outperforms the state-of-the-art methods.

This study proposes a novel approach to derive the mining-induced goaf deformation, named the fully refined deformation extraction method (FRDEM). In this method, the probability integral method (PIM) and the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique are geographically integrated in a low-cost way to derive the fully refined goaf deformation and, therefore, to improve the detection ability for mining subsidence with fast deformation rate and large gradient. To achieve this, we first established a functional relationship model to connect the 3-D parameters of PIM with SBAS-InSAR. Then, an improved genetic algorithm (IGA) was presented for parameter inversion, and thus, the optimal parameter set for PIM and the predicted displacements of the mining goaf was achieved economically. Afterward, we developed a geographically weighted data fusion model by presenting a data fusion strategy to eliminate the spatial heterogeneity in goaf boundary areas, and the fully refined goaf deformation field for the working face 2302 in the Guotun coal mining area was finally derived. Results demonstrate that FRDEM-derived displacements are highly consistent with field measurements, with root-mean-square error (RMSE) decreasing to 0.053, 0.057, and 0.044 m, respectively, for the entire goaf, goaf center, and goaf boundary field, compared with those of $\sim $ 0.092, $\sim $ 0.095, and $\sim $ 0.084 m for PIM and $\sim $ 0.578, $\sim $ 0.696, and $\sim $ 0.046 m for SBAS-InSAR, respectively. This implies that the proposed FRDEM has significantly improved the detection ability in deriving the mining-induced deformation and, thus, can be a very promising tool to forecast and evaluate potential geohazards in the coal mining area.

How to more effectively and comprehensively utilize recycled aggregate resources to prepare a richer new low-carbon concrete material system is the key path to achieve low-carbon development in China’s construction field. Based on the material properties of graphite tailings (GT), this article explores the organic combination of GT to replace sand and recycled aggregates (RA) to replace natural stones, in order to propose an efficient recycling path for multiple solid wastes and a new and cost-effective recycled aggregate concrete (RAC). This article focuses on investigating the influences of GT (0–40%) and RA (0–40%) on the workability, water absorption, and mechanical properties of GT reinforced recycled aggregate concrete (GTRAC). Simultaneously, the hydration products, microstructure morphology, key chemical bonds, and pore structure characteristics were analyzed by a combination of microscopic tests such as X-ray diffraction, scanning electron microscope, Fourier transform infrared and mercury intrusion porosimetry to propose the key factors affecting the macroscopic performance of GTRAC. The results show that incorporating 10–30% GT has significant positive effects on water absorption, surface water content, compressive strength, and elastic modulus of RAC. RAC’s micro-crack density, hydration, and pore structure distribution can be maximally improved by appropriate GT (20%). However, the high content of GT will lead to the degradation of its mechanical properties. Therefore, 20% GT and 30% recycled aggregate are the optimum combinations of the concrete material system. However, it might be changed when the ratio of water to binder varies. Finally, this article also comprehensively analyzes and evaluates the performance and price of different types of RACs, which provides theoretical support for evaluating and predicting the practical engineering application value of GTRAC. In summary, the research results in this study provide theoretical guidance for understanding the mechanism of GT on the enhancement of mechanical properties of RAC and exploring new ways of practical engineering application of it.

Between the two major arsenic-containing salts in natural water, arsenite (As(III)) is far more harmful to human and the environment than arsenate (As(V)) due to its high toxicity and transportability. Therefore, preoxidation of As(III) to As(V) is considered to be an effective means to reduce the toxicity of arsenic and to promote the removal efficiency of arsenic. Due to their high catalytic activity and arsenic affinity, iron-based functional materials can quickly oxidize As(III) to As(V) in heterogeneous Fenton-like systems, and then remove As(V) from water through adsorption and surface coprecipitation. In this review, the effects of different iron-based functional materials such as zero-valent iron and iron (hydroxy) oxides on arsenic removal are compared, and the catalytic oxidation mechanism of As(III) in heterogeneous Fenton process is further clarified. Finally, the main challenges and opportunities faced by iron-based As(III) oxidation functional materials are prospected.

•A relation between two different sets of dimensionless governing equations is found.•Two combined methods (IPS-CSM & ACM-CSM) are developed for RBC.•The scaling correlation of Nu and Ra is revealed for laminar RB convection.