Quantitative characterization of nanopore structure is of great importance for both accurate assessment of gas reservoir capacity and gas migration behaviors. In this work, atomic force microscopy ...(AFM) experiments were performed on coal and coal-bearing shale samples, attempting to gain clearer insights into the nanopore characteristics and surface roughness. Scanning electron microscopy (SEM) and low pressure N2 gas adsorption (LP-N2GA) were used for qualitative and quantitative comparative analysis, respectively. SEM results are found in good accordance with AFM surface topographies. The values of Ra are 18.04, 7.24 and 14.17 nm for Coal-1, Coal-2 and shale, respectively. The obtained similar nanopore distribution curves via LP-N2GA and AFM indicate that the nanoporosity determined by AFM is reliable. However, the differences between these two methods can not be neglected. The percentage of nanopores with diameter <4 nm generated from LP-N2GA are higher than that rendered from AFM for both coal and shale samples, due to the gas adsorption-induced swelling, but it is reverse for macropore (>200 nm). AFM is a high-resolution tool for nanopore characterization, and it is expected to be widely used for quantitative analysis of porous media.
•The surface 3-D topographies of coal and shale were provided by AFM.•Both qualitative and quantitative comparative analysis were made for nanopores.•SEM results are in good accordance with AFM surface topographies.•The similar pore distributions indicate that the nanoporosity by AFM is reliable.
Liquid carbon dioxide phase change fracturing (LCPCF) technology can effectively increase the coal permeability. In order to evaluate the influence of LCPCF on pore structure and permeability ...characteristics of coal, mercury intrusion porosimetry (MIP) analyses and permeability tests were used in this study. The experimental results show that LCPCF has less effect on transition pores, but it indeed has significant influence on pores (> 100 nm) and fracture structure of coal, further affecting the gas permeability characteristics within coal. This influence is heavily dependent on the distance from the fracturing borehole. When 1.26 L of liquid CO2 (weighing 1.25 kg–1.40 kg) was used to conduct physical blasting, according to the change trends of pore/fracture and gas permeability within coal, the influence degree of LCPCF on coal can be divided into three stages at the distance of 0.2–1.0 m, 1.0–6.0 m and > 6.0 m, respectively. For the first stage, the influence of LCPCF is strengthened, and mesopores within coal are reduced and shift to the larger pores under the effect of high-energy gas and shock wave, leading to the increase in the number of macropores and microfractures, which in turn improves the gas permeability of coal to a large degree. At the second stage, due to the energy attenuation of shock wave and high-pressure CO2 gas, the fracturing effect of LCPCF is reduced with the distance increasing from 1.0 m to 6.0 m, and the increase of coal permeability is rapidly diminished. At the third stage, both the change rates of pore structure and permeability characteristics of coal tend to be stable over 6.0 m away from the fracturing borehole, indicating that the influence scope of LCPCF is approximately 6.0 m for a single fracturing borehole.
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•The change rate of mesopore volume demonstrates a U-shape curve with distance.•The ΔP reduced rate is linearly reduced with the distance at 0.2–6.0 m.•The influence degree of LCPCF on coal was divided into three stages.•The maximum increase of coal permeability is enhanced by 1.2–1.7 times.
Acid based fracturing fluid (AFF) with appropriate hydrochloric acid (HCl) content is not only conducive to the exploitation of coalbed methane (ECBM), but also can reduce the cost and the risk of ...environmental pollution. The optimum HCl content can be obtained by FTIR, MIP, and XRD test of the treated coal samples. The results show that the lag zone reaches the minimum value of 0.09 when the HCl content is about 3%, indicating that the connectivity of coal is the best. The effect of AFF on the adsorption pore structure is sensitive to HCl content. AFF with high HCl tends to increase the amount of adsorption pores by more than 9.7%, which enhances the gas adsorption capacity and is thus not conducive to ECBM. The functional group content initially increases with increasing HCl and then decreases. When the content of HCl is about 3%, they reache the maximum value, increasing by 26.5%, 16%, 38.5%, and 48.2%. The infrared indexes show the same trend of an initial increase and then decrease with increasing HCl content, whereas the aliphatic chain length shows a weakly positive trend. The results indicate that AFF with 3%–5% HCl is the most conducive for ECBM.
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•With the increase of HCl the functional groups initially increase and then decrease.•Low and high HCl generates different effects on the adsorption pores.•Chemical reaction and ion exchange lead to the development and expansion of pores.•AFF with 3–5% HCl is the most conducive for ECBM.
Over the past ten years, the use of medical imaging and electronic medical records (EPR) in the medical sector has been exponential in sharing and exchanging patient information between networked ...hospitals and related medical centers. Various digital watermarking techniques are being deployed to ensure confidentiality and security of medical images and information, which are stored or distributed in the medical department. The concept of this paper embeds EPR information on medical images to reduce storage space and transmission overhead, greatly reducing guaranteed data sharing techniques. In this paper, the ROI (region of interest) Lossless Medical Image Watermarking (MIW) technique and EPR are used to embed the medical image coefficients in the discrete cosine transform (DCT) median frequency band. Fuzzy C means (FCM) segmentation method is used to extract ROI. Before embedding, the patient’s information, a binary logo watermark, is improved by convolutional error correction coding (ECC) and M-ary modulation, which improves the robustness of the algorithm. The success of this approach has been demonstrated and validated by a variety of medical imaging methods: magnetic resonance angiography, magnetic resonance imaging, Radiology, and CT, and the results show no significant difference between the original watermark images. This watermarking method is robust to common attacks such as JPEG encoding, noise adding, histogram equalization, filtering, blurring, rotation and cropping. The improvement in robustness is demonstrated in the case of bit error rates in JPEG compression operations, using convolutional encoder coding calculations.
Study of the adsorption property and mechanism about the environmental functional material can provide theoretical guidance for its actual application. The sulfhydryl attapulgite (SH-ATP) was ...prepared by solution blending method. The saturated adsorption capacity to Pb2+ and Cd2+ in the experiment were 65.57 and 22.71 mg/g, which increased by 57.74% and 31.96% comparing with the attapulgite (ATP), respectively. The adsorption kinetics experiments illustrated that the adsorption of Pb2+ and Cd2+ onto SH-ATP accorded with multiple kinetic models. Among them, the pseudo second-order model had the best correlation, the correlation coefficients were all above 0.998. The adsorption thermodynamics experiments testified that the adsorption of Pb2+ and Cd2+ onto SH-ATP conformed to the Langmuir, Tempkin and Dubinin-Radushkevich models, those models indicated that the adsorption processes belonged to the monolayer adsorption, the adsorbent surface was homogeneous, and the micropore distribution of the adsorbent was also relatively uniform. Various thermodynamic parameters (ΔH > 0, ΔS > 0, ΔG < 0) under 25-45℃ indicated that the adsorption processes of Pb2+ and Cd2+ onto SH-ATP were endothermic and spontaneous.
Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to ...significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.
The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex ...performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.
The water sorption and desorption properties of solid adsorbent materials are crucial in rotary dehumidification systems. Metal organic frameworks (MOFs) and hydrogels are mostly at the laboratory ...stage due to factors like the synthesis process and yield. In this study, we utilized an eco-friendly and large-scale synthesis method to prepare polyacrylamide (PAM) hydrogels (yielding approximately 500 mL from a single polymerization). Subsequently, PAM was then coated onto glass fiber paper (GFP), which serves as a commonly employed substrate in desiccant wheels. By incorporating the hygroscopic salt LiCl and optimizing the content of each component, the water sorption performance of the composite was notably improved. The water sorption and desorption performances, as well as cycling stability, were evaluated and compared with composites containing aluminum fumarate, LiCl, and GFP (AlFum-LiCl&GFP). The results revealed that PAM-LiCl&GFP outperformed AlFum-LiCl&GFP in terms of sorption capacity throughout various relative humidity (RH) levels. It achieved a water uptake of 1.06 g·g−1 at 25 °C and 30% RH, corresponding to a water sorption rate coefficient K of 15.32 × 10−4 s−1. Furthermore, the lower desorption temperature (60 °C) resulting in a desorption ratio of 82.6%, along with the excellent cycling stability and effective performance as a desiccant wheel module, provide evidence for the potential application of PAM-LiCl&GFP in desiccant wheels.
Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample ...size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.
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