The interfacial adhesion between asphalt and steel slag aggregate is a decisive factor in the formation of an asphalt-steel slag mixture and significantly affects the quality stability of steel ...slag-asphalt mixtures. In this study, the adhesion between an asphalt and steel slag aggregate, the interfacial microstructure, the adsorption and desorption characteristics, and chemical reactions were, respectively, explored by a PosiTestAT-A adhesion puller, a scanning electron microscope, a net adsorption test, an infrared spectrometer, and a dynamic shear rheometer. The mechanism of adhesion between the asphalt and steel slag aggregate was analyzed from the perspectives of physical adsorption and chemical reactions. The results showed that different factors had different effects on the adhesion of asphalt-steel slag aggregate interface. The freeze-thaw cycle and steel slag aggregate particle size had significant effects on interfacial adhesion, while the asphalt heating temperature, water bath time, and stirring time had relatively weak effects on interfacial adhesion. Compared to a limestone aggregate, the steel slag-asphalt mixture had greater adhesion and better adhesion performance because the pits and textures on the surface of the steel slag aggregate produced a skeleton-like effect that strengthened the phase strength of the asphalt-slag aggregate interface, thereby improving the adhesion and increasing the physical adsorption between the asphalt and steel slag aggregate. In addition, due to the N-H stretching vibrations of the amines and amides, as well as SiO-H stretching vibrations, a chemical reaction occurred between the asphalt and steel slag aggregate, thus improving the adhesion performance between the asphalt and steel slag. Based on the shape of the adsorption isotherm, it was determined that the adsorption type was multi-molecular layer adsorption, indicating that the adhesion between the asphalt and steel slag mainly involved physical adsorption.
Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and ...the complex structure of retinal fundus images, this makes accurate segmentation of blood vessels from retinal images still challenging. In this paper, we propose an effective framework for retinal vascular segmentation, which is innovative mainly in the retinal image pre-processing stage and segmentation stage. First, we perform image enhancement on three publicly available fundus datasets based on the multiscale retinex with color restoration (MSRCR) method, which effectively suppresses noise and highlights the vessel structure creating a good basis for the segmentation phase. The processed fundus images are then fed into an effective Reverse Fusion Attention Residual Network (RFARN) for training to achieve more accurate retinal vessel segmentation. In the RFARN, we use Reverse Channel Attention Module (RCAM) and Reverse Spatial Attention Module (RSAM) to highlight the shallow details of the channel and spatial dimensions. And RCAM and RSAM are used to achieve effective fusion of deep local features with shallow global features to ensure the continuity and integrity of the segmented vessels. In the experimental results for the DRIVE, STARE and CHASE datasets, the evaluation metrics were 0.9712, 0.9822 and 0.9780 for accuracy (Acc), 0.8788, 0.8874 and 0.8352 for sensitivity (Se), 0.9803, 0.9891 and 0.9890 for specificity (Sp), area under the ROC curve(AUC) was 0.9910, 0.9952 and 0.9904, and the F1-Score was 0.8453, 0.8707 and 0.8185. In comparison with existing retinal image segmentation methods, e.g. UNet, R2UNet, DUNet, HAnet, Sine-Net, FANet, etc., our method in three fundus datasets achieved better vessel segmentation performance and results.
Steel slag is an industrial solid waste with the largest output in the world. It has the characteristics of wear resistance, good particle shape, large porosity, etc. At the same time, it has good ...adhesion characteristics with asphalt. If steel slag is used in asphalt pavement, it not only solves the problem of insufficient quality aggregates in asphalt concrete, but can also give full play to the high hardness and high wear resistance of steel slag to improve the performance of asphalt pavement. In this study, a steel slag aggregate was mixed with road petroleum asphalt to prepare a permeable steel slag–asphalt mixture, which was then compared with the permeable limestone–asphalt mixture. According to the Technical Regulations for Permeable Asphalt Pavement (CJJT 190-2012), the permeability, water stability, and Marshall stability of the prepared asphalt mixtures were tested and analyzed. In addition, the high-temperature stability and expansibility were analyzed according to the Experimental Regulations for Highway Engineering Asphalt and Asphalt Mixture (JTG E20-2011). The chemical composition of the steel slag was tested and analyzed by X-ray fluorescence spectrometer (XRF). The mineral composition of the steel slag was tested and analyzed by X-ray diffractometer (XRD). The asphalt was analyzed by Fourier transform infrared spectroscopy (FTIR). The results show that the steel slag asphalt permeable mixture had good permeability, water stability, and Marshall stability, as well as good high-temperature stability and a low expansion rate. The main mineral composition was ferroferric oxide, the RO phase (RO phase is a broad solid solution formed by melting FeO, MgO, and other divalent metal oxides such as MnO), dicalcium silicate, and tricalcium silicate. In the main chemical composition of steel slag, there was no chemical reaction between aluminum oxide, calcium oxide, silicon dioxide, and asphalt, while ferric oxide chemically reacted with asphalt and formed new organosilicon compounds. The main mineral composition of the steel slag (i.e., triiron tetroxide, dicalcium silicate, and tricalcium silicate) reacted chemically with the asphalt and produced new substances. There was no chemical reaction between the RO phase and asphalt.
In this paper, a permeable steel-slag–bitumen mixture (PSSBM) was first prepared according to the designed mixture ratio. Then, the interaction characteristics between steel slag and bitumen were ...studied. The chemical interaction between bitumen and steel slag was explored with a Fourier-transform infrared spectrometer (FT-IR). The influence of steel-slag chemistry, mineral composition, and bitumen reaction on phase angle, complex shear modulus (CSM), and rutting factor was explored with dynamic shear rheological (DSR) tests. The PSSBM had better properties, including high permeability, water stability, Marshall stability, high-temperature (HT) stability, and low volume-expansion rate. Bitumen-coated steel slag can prevent heavy-metal ions from leaching. In the infrared spectra of the mixture of a chemical component of steel slag (calcium oxide) and bitumen, a new absorption peak at 3645 cm−1 was ascribed to the SiO–H stretching vibration, indicating that new organic silicon compounds were produced in the chemical reaction between calcium oxide and bitumen. SiO–H had an obvious enhancement effect on the interfacial adhesion and high-temperature rheological property of the mixture. In the mineral components of steel slag, dicalcium and tricalcium silicate reacted with bitumen and generated new substances. Chemical reactions between tricalcium silicate and bitumen were significant and had obvious enhancement effects on interfacial adhesion and high-temperature rheological properties of the mixture. The results of FT-IR and DSR were basically consistent, which revealed the chemical-reaction mechanism between steel-slag microcomponents and bitumen at the interface. SEM results showed that pits and grooves on the surface of the steel-slag aggregate, and the textural characteristics provide a framework-like function, thus strengthening the strength and adhesion of the steel-slag–bitumen aggregate interface.
Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause ...blindness. Most segmentation methods based on deep convolutional neural networks can effectively extract features. However, convolution and pooling operations also filter out some useful information, and the final segmented retinal vessels have problems such as low classification accuracy. In this paper, we propose a multi-scale residual attention network called MRA-UNet. Multi-scale inputs enable the network to learn features at different scales, which increases the robustness of the network. In the encoding phase, we reduce the negative influence of the background and eliminate noise by using the residual attention module. We use the bottom reconstruction module to aggregate the feature information under different receptive fields, so that the model can extract the information of different thicknesses of blood vessels. Finally, the spatial activation module is used to process the up-sampled image to further increase the difference between blood vessels and background, which promotes the recovery of small blood vessels at the edges. Our method was verified on the DRIVE, CHASE, and STARE datasets. Respectively, the segmentation accuracy rates reached 96.98%, 97.58%, and 97.63%; the specificity reached 98.28%, 98.54%, and 98.73%; and the F-measure scores reached 82.93%, 81.27%, and 84.22%. We compared the experimental results with some state-of-art methods, such as U-Net, R2U-Net, and AG-UNet in terms of accuracy, sensitivity, specificity, F-measure, and AUCROC. Particularly, MRA-UNet outperformed U-Net by 1.51%, 3.44%, and 0.49% on DRIVE, CHASE, and STARE datasets, respectively.
Phosphogypsum (PG) is solid waste generated by the phosphate fertilizer industry. However, by high-temperature thermal treatment, it can be effectively decomposed into byproducts like SO2 and CaO, ...which are valuable raw materials for the production of sulfuric acid and construction materials, respectively. In this study, we investigated the thermal decomposition properties of suspended PG, as well as the factors governing the PG decomposition procedure. The results show that the PG decomposition rate was influenced by several parameters, with temperature being the most significant, followed by time, atmosphere, and charcoal powder dosing. Similarly, the desulfurization rate was primarily influenced by time, followed by temperature, atmosphere, and charcoal powder dosing. The PG exhibited maximum decomposition and desulfurization rates of 97.73% and 97.2%, respectively, at a calcination temperature of 1180 °C, a calcination period of 15 min, a carbon powder dosage of 4%, and a CO concentration of 6%. Kinetic studies revealed that the activation energy of the PG decomposition reaction decreased from 303.45 to 254.28 kJ/mol as the CO concentration increased from 4% to 6% upon addition of charcoal powder as a reducing agent. Furthermore, higher CO concentrations had a more pronounced effect on lowering the activation energy of PG pyrolysis. When the contents of SiO2 and Al2O3 impurities in the PG content were higher, they reacted with CaO in the decomposition products to produce silicate and aluminate minerals, respectively, decreasing both the CaO content in the product and the PG decomposition rate.
The structural features, chemical properties, and methods of inactivation of the amorphous Ti species in TS-1 are investigated. The formation of the amorphous Ti species, which is composed of ...fragments of Ti–O–Ti chains, is confirmed by the Raman band at 700cm−1. The presence of the amorphous Ti species severely decreases the catalytic performance of TS-1 by causing byproduct formation and H2O2 decomposition. The sulfosalt impregnation method can easily reduce the adverse effects of the amorphous Ti species. Display omitted
► New Raman feature at 700cm−1 was assigned to the amorphous Ti species in TS-1. ► The amorphous Ti species compose by hexacoordinated Ti–O–Ti linkages. ► The amorphous Ti species enhancing the acidity and causing the H2O2 decomposition. ► The sulfosalt impregnation can reduce the adverse effect of the amorphous Ti species.
The nature of the amorphous Ti species in titanium silicalite-1 (TS-1) was investigated by UV Raman spectroscopy (UV Raman), UV–vis diffuse reflectance spectroscopy (UVDRS), infrared spectroscopy (IR), and Hammett indicator titration methods. A new Raman feature at 700cm−1, which is closely related to the UVDRS band at 260–280nm, is first assigned to a totally symmetric vibration of hexacoordinated Ti–O–Ti linkages of the amorphous Ti species. The amorphous Ti species in TS-1 can be formed easily under the synthesis conditions in the presence of Na+ or after treatment in ammonia solution. The results reveal that the amorphous Ti species is the main factor in the enhanced acidity and H2O2 decomposition ability of TS-1. A sulfosalt impregnation method has been proved effective in reducing the adverse effect of the amorphous Ti species.
The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information ...extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.
With the increasing power generation capacity of circulating fluidized bed boilers and electrolytic aluminum production worldwide, the emissions of fluidized bed coal combustion fly ash (FBCF) and ...bayer red mud (BRM) have increased dramatically, resulting in extremely severe environmental issues as it is difficult to utilize these as resources. To address this issue, this study uses industrial solid wastes such as BRM, FBCF, and silica fume as mineral admixtures to develop a green and low-carbon early strength multi-solid waste composite grouting material (ESMSWCGM) for semi-flexible pavements (SFPs). According to the findings of this study, 18 % of the cement in the ESMSWCGM could be replaced with the three solid waste mineral admixtures. With the optimal ratio, the developed ESMSWCGM exhibits initial and 20-min flow-cone fluidities of 12 s and 14 s, respectively, and initial and final setting times of 66 min and 69 min, respectively. The compressive strengths at 3 h, 1 d, 7 d, and 28 d are 17.05 MPa, 22.95 MPa, 29.55 MPa, and 30.72 MPa, respectively, and the dry shrinkage rate at 28 d is 0.12 %. The performance of the SFP material obtained through grouting is excellent. When the grouting saturation is 95 %, the SFP has a dynamic stability of 28911, a rutting depth of 1.19 mm, and a splitting strength of 1.91 MPa.
Retinal vessel segmentation has high value for the research on the diagnosis of diabetic retinopathy, hypertension, and cardiovascular and cerebrovascular diseases. Most methods based on deep ...convolutional neural networks (DCNN) do not have large receptive fields or rich spatial information and cannot capture global context information of the larger areas. Therefore, it is difficult to identify the lesion area, and the segmentation efficiency is poor. This paper presents a butterfly fully convolutional neural network (BFCN). First, in view of the low contrast between blood vessels and the background in retinal blood vessel images, this paper uses automatic color enhancement (ACE) technology to increase the contrast between blood vessels and the background. Second, using the multiscale information extraction (MSIE) module in the backbone network can capture the global contextual information in a larger area to reduce the loss of feature information. At the same time, using the transfer layer (T_Layer) can not only alleviate gradient vanishing problem and repair the information loss in the downsampling process but also obtain rich spatial information. Finally, for the first time in the paper, the segmentation image is postprocessed, and the Laplacian sharpening method is used to improve the accuracy of vessel segmentation. The method mentioned in this paper has been verified by the DRIVE, STARE, and CHASE datasets, with the accuracy of 0.9627, 0.9735, and 0.9688, respectively.
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