The total organic carbon (TOC) content is the most important geochemical parameter of a shale reservoir; thus, the methods used for its prediction require further study. The ΔlogR method is currently ...the only available TOC calculation method based on a rigorous petrophysical model derivation. However, because the ΔlogR method does not consider the complex mineral compositions of shales or the effects of hole enlargement, the applicability of the ΔlogR method to the evaluation of an actual shale reservoir is obviously inadequate. In this paper, a targeted and fundamental improvement is made to the ΔlogR method, and a dual-difference ΔlogR (DDΔlogR) method is proposed. First, a dynamic theoretical relation curve is derived, meaning that the ΔlogR method considers the characteristics of a variable theoretical relation curve. Second, two ΔlogR values are calculated using both the acoustic curve with TOC information and the theoretical acoustic curve without TOC information, and the difference between the two is used to characterize the TOC content. Finally, the radioactivity of organic matter is characterized using a log curve that excludes the influence of the clay content. Through the processing of actual data from the Sichuan Basin, the DDΔlogR method effectively improves the ΔlogR calculation accuracy in consideration of rock mineral composition variations and hole enlargement effects, and the prediction results are better than those obtained using machine learning algorithms. The DDΔlogR method proposed in this paper greatly expands the applicability of the ΔlogR method and can effectively aid in the exploration and development of shale reservoirs.
•An improved model (Dual-difference ΔlogR, DDΔlogR) was proposed to estimate the TOC contents.•DDΔlogR can eliminate the effect of mineral composition or the hole enlargement of shale wells.•The dynamic theoretical relation curve for ΔlogR was proposed.•The DDΔlogR method can improve the effect of the ΔlogR method even if the ΔlogR method is further improved later.
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Effective and rapid assessment of pork freshness is significant for monitoring pork quality. However, a traditional sensory evaluation method is subjective and physicochemical analysis is ...time-consuming. In this study, the near-infrared spectroscopy (NIRS) technique, a fast and non-destructive analysis method, is employed to determine pork freshness. Considering that commonly used statistical modeling methods require preprocessing data for satisfactory performance, this paper presents a one-dimensional squeeze-and-excitation residual network (1D-SE-ResNet) to construct the complex relationship between pork freshness and NIRS. The developed model enhances the one-dimensional residual network (1D-ResNet) with squeeze-and-excitation (SE) blocks. As a deep learning model, the proposed method is capable of extracting features from the input spectra automatically and can be used as an end-to-end model to simplify the modeling process. A comparison between the proposed method and five popular classification models indicates that the 1D-SE-ResNet achieves the best performance, with a classification accuracy of 93.72%. The research demonstrates that the NIRS analysis technique based on deep learning provides a promising tool for pork freshness detection and therefore is helpful for ensuring food safety.
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The contact-type displacement and angular sensors were improved and utilized in weld seam trajectory detection. A detection–compensation–tracking system was developed. The mechanical part of this ...system was installed and independent of the robot, which can realize the detection of right-left deviation and up–down offset of the weld path. In the experiment, the position coordinates of the detection point in weld groove were calculated and weld seam tracking was carried out simultaneously owing to its single control system. When the absolute interpolation algorithm was adopted, the average error of width deviation and depth deviation were 0.1817 mm and 0.1449 mm, respectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The total organic carbon content usually determines the hydrocarbon generation potential of a formation. A higher total organic carbon content often corresponds to a greater possibility of generating ...large amounts of oil or gas. Hence, accurately calculating the total organic carbon content in a formation is very important. Present research is focused on precisely calculating the total organic carbon content based on machine learning. At present, many machine learning methods, including backpropagation neural networks, support vector regression, random forests, extreme learning machines, and deep learning, are employed to evaluate the total organic carbon content. However, the principles and perspectives of various machine learning algorithms are quite different. This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems. Of various machine learning algorithms used for TOC content predication, two algorithms, the backpropagation neural network and support vector regression are the most commonly used, and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results. Additionally, combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction. The prediction by backpropagation neural network may be better than that by support vector regression; nevertheless, using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block. According to some published literature, the determination coefficient (R2) can be increased by up to 0.46 after using machine learning. Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content. Evaluating the total organic carbon content based on machine learning is of great significance.
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•TOC content evaluation method based on machine learning algorithms.•Different machine learning algorithms may yield different evaluation results.•BPNN and SVR are currently the most widely used algorithms for TOC content evaluation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Under the dispute between China and the United States, the international field of public opinion is dominated by increasing tensions, and the network rumors triggered by the COVID-19 epidemic are ...intensifying. In view of the above-mentioned context, this paper focuses on the development and the evolution process of public opinions. Since the evolution of public opinion is often accompanied by the spread and diffusion of information, this paper combines the process of information diffusion with the development process of polarization behavior, and brings in the dynamic network and the timeliness factor of public opinion dissemination, so as to better explore the polarization process of public opinion under the dynamic network. Then, this paper focuses on the analysis of the parameters of the model and through the dynamic adjustment of parameters, finding out the main factors that affect the trend and development of network public opinion. In addition, this paper introduces an actual case, and takes the actual case data as the support to demonstrate the reliability and practical application value of the model. Finally, based on the simulation results and analysis of actual cases, this paper puts forward the corresponding preventive measures to alleviate the polarization behavior of the group.
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Since both ethanol and acetone are the main components in many alternative fuels, research on the burning characteristics of ethanol-acetone blends is important to understand the combustion phenomena ...of these alternative fuels. In the present study, the burning characteristics of ethanol-acetone fuel blends are investigated at a temperature of 358 K and pressure of 0.1 MPa with equivalence ratios ranging from 0.7 to 1.4. Ethanol at 100% vol., 25% vol. ethanol/75% vol. acetone, 50% vol. ethanol/50% vol. acetone, 75% vol. ethanol/25% vol. acetone, and 100% vol. acetone are studied by the constant volume combustion chamber (CVCC) method. The results show that the laminar burning velocities of the fuel blends are between that of 100% vol. acetone and 100% vol. ethanol. As the ethanol content increases, the laminar burning velocities of the mixed fuels increase. Furthermore, a detailed chemical kinetic mechanism (AramcoMech 3.0) is used for simulating the burning characteristics of the mixtures. The directed relation graph (DRG), DRG with error propagation (DRGEP), sensitivity analysis (SA), and full species sensitivity analysis (FSSA) are used for mechanism reduction. The flame structure of the skeletal mechanism does not change significantly, and the concentration of each species remains basically the same value after the reaction. The numbers of reactions and species are reduced by 90% compared to the detailed mechanism. Sensitivity and reaction pathway analyses of the burning characteristics of the mixtures indicate that the reaction C2H2+H(+M)<=>C2H3(+M) is the key reaction.
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The equivalent medium theory of metamaterials provides a way to obtain their effective constitutive parameters. However, because of its non-reciprocity, the complexity of the electromagnetic ...coupling, and a metallic bottom layer, it has been challenging to retrieve them from a metamaterial absorber. In this paper, we propose a method without any approximation to obtain them, in which the non-reciprocity and the strong electromagnetic coupling are included. Compared with the three methods such as symmetric metamaterial method, asymmetric metamaterial method and metasurface method, our method can reveal the metamaterial absorber’s electrical and magnetic resonance and show its electromagnetic coupling coefficients. To deal with a metamaterial absorber with a metallic bottom layer, four corners of the metallic bottom layer in the unit cell are removed, making it possible to retrieve the electromagnetic parameters. Surprisingly, these results show that the metamaterial absorber with a metallic bottom layer in our example operates in a negative refraction state at the half absorption frequencies, which helps further understand the absorbing mechanism of these metamaterial absorbers.
The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core ...reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.
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Nanoscale fluorescence emitters are efficient for measuring biomolecular interactions, but their utility for applications requiring single-unit observations is constrained by the need for large ...numerical aperture objectives, fluorescence intermittency, and poor photon collection efficiency resulting from omnidirectional emission. Photonic crystal (PC) structures hold promise to address the aforementioned challenges in fluorescence enhancement. In this review, we provide a broad overview of PCs by explaining their structures, design strategies, fabrication techniques, and sensing principles. Furthermore, we discuss recent applications of PC-enhanced fluorescence-based biosensors incorporated with emerging technologies, including nucleic acids sensing, protein detection, and steroid monitoring. Finally, we discuss current challenges associated with PC-enhanced fluorescence and provide an outlook for fluorescence enhancement with photonic-plasmonics coupling and their promise for point-of-care biosensing as well monitoring analytes of biological and environmental relevance. The review presents the transdisciplinary applications of PCs in the broad arena of fluorescence spectroscopy with broad applications in photo-plasmonics, life science research, materials chemistry, cancer diagnostics, and internet of things.
In recent years, the biosensor research community has made rapid progress in the development of nanostructured materials capable of amplifying the interaction between light and biological matter. A ...common objective is to concentrate the electromagnetic energy associated with light into nanometer-scale volumes that, in many cases, can extend below the conventional Abbé diffraction limit. Dating back to the first application of surface plasmon resonance (SPR) for label-free detection of biomolecular interactions, resonant optical structures, including waveguides, ring resonators, and photonic crystals, have proven to be effective conduits for a wide range of optical enhancement effects that include enhanced excitation of photon emitters (such as quantum dots, organic dyes, and fluorescent proteins), enhanced extraction from photon emitters, enhanced optical absorption, and enhanced optical scattering (such as from Raman-scatterers and nanoparticles). The application of photonic metamaterials as a means for enhancing contrast in microscopy is a recent technological development. Through their ability to generate surface-localized and resonantly enhanced electromagnetic fields, photonic metamaterials are an effective surface for magnifying absorption, photon emission, and scattering associated with biological materials while an imaging system records spatial and temporal patterns. By replacing the conventional glass microscope slide with a photonic metamaterial, new forms of contrast and enhanced signal-to-noise are obtained for applications that include cancer diagnostics, infectious disease diagnostics, cell membrane imaging, biomolecular interaction analysis, and drug discovery. This paper will review the current state of the art in which photonic metamaterial surfaces are utilized in the context of microscopy.
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