Extensive research has demonstrated the advantageous utilization of medium–low temperature fast pyrolysis (FP) for biomass, yielding high–grade liquid–phase chemicals or fuels. However, the field of ...FP–based high–performance solid biochar research still presents several gaps. Herein, a one–step versus two–step method for biomass H3PO4 activation under FP was comparatively analyzed for the first time, and efficiently activated carbons (ACs) for dye removal were successfully synthesized at a low temperature (723 K). Investigation of methylene blue (MB) adsorption revealed that the one–step sample P–H–0.5, possessing a specific surface area of 1004 m2·g−1, exhibited a remarkable adsorption capacity of 695.54 mg·g−1 with an ultra–high removal rate (99.94%, C0 = 150 mg·L−1). The two–step sample P–2–H–2, a modified byproduct of FP, achieved efficient dye adsorption in the shortest time (2 min, 383.91 mg·g−1). This originated from the well–developed surface macropores and elevated group content derived from phosphorus (P)—modification. Both adsorption data were well–fitted with pseudo–second–order kinetics and the Langmuir model, revealing the presence of chemical effects and the dominance of monolayer adsorption. A more detailed kinetic study suggested intrapore transport primarily governed the adsorption process on P–H–0.5, whereas P–2–H–2 relied on surface diffusion. FTIR and XPS revealed notable differences in the active sites between the two methods. Aside from –OH, –COOH with C–O–P, the P elements of P–H–0.5 were classified as C–P–O3 and C2–O–P2, demonstrating the ability of one–step FP to introduce heteroatoms into carbon defects. The basic interactions of ACs with MB were π–π stacking and hydrogen bonding established by –OH–containing groups. At a suitable pH (>5), most H+ was removed from the surface, and the electrostatic attraction became the strongest linking force. Both ACs exhibited exceptional reusability, with removal rates surpassing 90% of the initial rate after four cycles of regeneration.
To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state ...assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.
Wettability is one of the key controlling parameters for multiphase flow in porous media, and paramount for various geoscience applications. While a general awareness of the importance of wettability ...was established decades ago, our fundamental understanding of how wettability influences transport and of how to characterize wettability has improved tremendously in recent years through breakthroughs in imaging technology and modeling techniques. Numerical modeling studies clearly show not only that macroscopic two-phase flow is influenced by the average wettability, but also that the spatial distribution of wetting significantly impacts the macroscopic parameters. Herein, we explore the thermodynamics for porous multiphase systems, and recent breakthroughs in wettability characterization. Our view is that bridging the multiscale characterization of wetting must consider two fundamental perspectives: geometry and energy. Advancing the overall description requires an improved understanding of the operative mechanisms that dominate at various scales, and the development of quantitative approaches to capture these effects. We take a multistage approach, looking at these fundamental perspectives from the sub-pore-to-pore length scales, followed by the pore-to-core length scales using various analytical techniques and numerical simulations. Within this context, there remain many open-ended questions, and we therefore highlight these issues to provide guidance on future research directions. Our overall aim is to provide comprehensive guidance on the multiscale characterization of wettability in porous media, in order to facilitate novel research.
The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric ...differences between bi-temporal images, and a shortage of labelled samples. Traditional supervised deep learning requires abundant labelled data, which is expensive to obtain for BCD. By contrast, there is ample unlabelled remote sensing imagery available. Self-supervised learning (SSL) offers a solution, allowing learning from unlabelled data without explicit labels. Inspired by SSL, we employed the SimSiam algorithm to acquire domain-specific knowledge from remote sensing data. Then, these well-initialised weight parameters were transferred to BCD tasks, achieving optimal accuracy. A novel framework for BCD was developed using self-supervised contrastive pre-training and historical geographic information system (GIS) vector maps (HGVMs). We introduced the improved MS-ResUNet network for the extraction of buildings from new temporal satellite images, incorporating multi-scale pyramid image inputs and multi-layer attention modules. In addition, we pioneered a novel spatial analysis rule for detecting changes in building vectors in bi-temporal images. This rule enabled automatic BCD by harnessing domain knowledge from HGVMs and building upon the spatial analysis of building vectors in bi-temporal images. We applied this method to two extensive datasets in Liuzhou, China, to assess its effectiveness in both urban and suburban areas. The experimental results demonstrated that our proposed approach offers a competitive quantitative and qualitative performance, surpassing existing state-of-the-art methods. Combining HGVMs and high-resolution remote sensing imagery from the corresponding years is useful for building updates.
Eliminating conventional pulsed B
-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the ...Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.
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For a wide range of subsurface engineering processes, such as geological carbon sequestration and enhanced oil recovery, it is critical to understand multiphase flow at a fundamental ...level. To this end, geomaterial microfluidic devices provide visual data that can be quantified to explain the physics of multiphase flow at the length scale of individual pores in realistic rock structures. For surfactant enhanced oil recovery, it is the underlying geometrical states of the capillary trapped oil that dictates the recovery process and the degree to which oil is recovered through either mobilization or solubilization during in situ emulsification.
A novel geomaterial microfluidic device is fabricated and its integrity is checked using light microscopy and X-ray micro-computed tomography (μ-CT) imaging. Subsequently, alkaline surfactant (AS) flooding of an oil saturated device is studied for enhanced recovery. The recovery process is analyzed by collecting 2D radiographic projections of the device during water flooding and in situ emulsification. 3D μ-CT images are also collected to quantify the geometrical states of the fluids after each flooding sequence.
Our study reveals the processes of oil cluster mobilization and solubilization in porous media. After water flooding there are numerous oil clusters that are relatively large, extending over multiple pores, forming various loop-like structures. These clusters are mobile under AS flooding accounting for 75% of the recovered oil. The less mobile smaller clusters, isolated to single pores, forming no loop-like structures are immobile. These clusters are solubilized during AS flooding accounting for 25% of the recovered oil. The mobilized clusters coalesce forming an oil bank prior to total solubilization. The remaining oil clusters after AS flooding are highly non-wetting, as indicated by contact angle measurements and would only be recoverable after further solubilization.
Radar observation is an effective way to understand subsurface structures in terms of the dielectric constant, whose controlling factors include chemical composition, packing density, and water/ice ...content. Recently, laboratory measurements have shown that the dielectric constant of lunar regolith simulants also depends on the temperature, which has never been evaluated from remote sensing data. In this study, we estimated the dielectric constant from the Miniature Radio Frequency (Mini-RF) data on a lunar crater floor in the north polar region at two different local times (i.e., different surface temperatures). We calculated the dielectric constant using the inversion method and obtained the bolometric surface temperature from the Diviner Lunar Radiometer Experiment (Diviner) data. The histograms of the estimated dielectric constant values are different between the two local times. This could be interpreted as a result of the temperature dependence of the dielectric constant, while further evaluation of the influence of topography on the incidence angle and small surface roughness is needed. Nevertheless, our result suggests that the temperature dependence of the dielectric constant should be considered when interpreting S-band radar observations of the Moon and other celestial bodies with large surface temperature differences.
In complex data environments, rational handling of unbalanced datasets is key to improving the reliability of early disease prediction. Early warning of disease risk in both temporal and spatial ...terms, contributes to disease prevention and treatment. To this end, a bi-dimensional substratum information mining model based on Association Rule Digging with Dynamic Thresholding and Weight Optimization (ARDdtwo) was proposed for the early diagnosis of thyroid cancer. It is an integrated assessment framework consisting of association rule digging by constructing a dynamic threshold model (ADRcdt) for qualitative analysis, and a self-optimizing component importance measurement model (SoCIM) for quantitative analysis. ARDcdt incorporates temporal and spatial features of sparse data to address the distributional bias problem. Moreover, new importance diagnostic calculations were designed to further identify high-risk low-frequency (HRLF). The SoCIM can determine the relative weight of each component by assessing its level of risk in the overall system based on the Risk Enhancement Level (REL) and Risk Reduction Level (RRL), realizing the self-adjustment and optimization of the weight setting. Finally, the model was validated through an empirical analysis. The evaluation of the research work shows that improved results were achieved, such as accuracy, f1-score, and precision, with optimized values of 36.04%,56.57%, and 53.89%, respectively. The overall area under the curve for the model was 0.882. This proves the validity of the proposed model for practical applications. For patients, it can simplify the pathological process and reduce the examination costs.
Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, ...which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning.
