Impact of Clay Stabilizer on the Methane Desorption Kinetics and Isotherms of Longmaxi Shale, China

Knowing methane desorption characteristics is essential to define the contribution of adsorbed gas to gas well production. To evaluate the synthetic effect of a clay stabilizer solution on methane desorption kinetics and isotherms pertaining to Longmaxi shale, an experimental setup was designed based on the volumetric method. The objective was to conduct experiments on methane adsorption and desorption kinetics and isotherms before and after clay stabilizer treatments. The experimental data were a good fit for both the intraparticle diffusion model and the Freundlich isotherm model. We analyzed the effect of the clay stabilizer on desorption kinetics and isotherms. Results show that clay stabilizer can obviously improve the diffusion rate constant and reduce the methane adsorption amount. Moreover, we analyzed the desorption efficiency before and after treatment as well as the adsorbed methane content. The results show that a higher desorption efficiency after treatment can be observed when the pressure is higher than 6.84 MPa. Meanwhile, the adsorbed methane content before and after treatment all increase when the pressure decreases, and clay stabilizer can obviously promote the adsorbed methane to free gas when the pressure is lower than 19 MPa. This can also be applied to the optimization formulation of slickwater and the design of gas well production

Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward\u27s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance

Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution

Current Scene text image super-resolution approaches primarily focus on extracting robust features, acquiring text information, and complex training strategies to generate super-resolution images. However, the upsampling module, which is crucial in the process of converting low-resolution images to high-resolution ones, has received little attention in existing works. To address this issue, we propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. Unlike previous graph attention mechanisms, our approach achieves 2-3 orders of magnitude improvement in efficiency and memory utilization by eliminating the dependency on sparse adjacency matrices and introducing a sliding window approach for efficient parallel computation. Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details. Comprehensive experiments on TextZoom demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy. For single-stage and multi-stage strategies, we achieved improvements of 0.7\% and 2.6\%, respectively, increasing the performance from 52.6\% and 53.7\% to 53.3\% and 56.3\%. The code is available at https://github.com/wenyu1009/RTSRN

Protective role for collectin‐11 in rheumatoid arthritis in mice

OBJECTIVE. Collectin-11 (CL-11) is a soluble C-type lectin, a mediator of innate immunity. Its role in autoimmune disorders is unknown. The goal of this study was to determine the role of CL-11 in a mouse model of rheumatoid arthritis (RA). METHODS. A murine collagen-induced arthritis (CIA) model, combining both gene deletion of Colec11 and recombinant (rCL-11) treatment approaches were employed. Joint inflammation and tissue destruction, circulating levels of inflammatory cytokines and adaptive immune responses were assessed in CIA mice. Splenic CD11c(+) cells were used to examine the influence of CL-11 on antigen presenting cell (APC) function. Serum levels of CL-11 in RA patients were also examined. RESULTS. Colec11(−/−) mice developed more severe arthritis than WT mice (as determined by disease incidence, clinical arthritis scores and histopathology; P<0.05). Disease severity is associated with significantly enhanced APC activation, Th1/Th17 responses, pathogenic IgG2a production and joint inflammation, as well as elevated circulating levels of inflammatory cytokines. In vitro analysis of CD11c(+) cells revealed that CL-11 is critical for suppression of APC activation and function. Pharmacological treatment of mice with rCL-11 reduced the severity of CIA in mice. Analysis of human blood samples revealed that serum levels of CL-11 was lower in RA patients (n=51) compared to healthy controls (n=53), a serum CL-11 reduction also displays a negative relationship with DAS28, ESR and CRP (P<0.05). CONCLUSION. Our findings demonstrate a novel role for CL-11 in protection against RA, suggesting the underlying mechanism involved suppression of APC activation and subsequent T cell responses

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A fault diagnosis method for rolling bearings of wind turbine generators based on MCGAN data enhancement

Abstract In view of the problems such as poor diagnostic capability and generalization ability of wind turbine generator bearing fault diagnosis methods caused by complex wind turbine generator bearing conditions and few fault samples under actual operating conditions, a wind turbine generator bearing vibration signal data enhancement method based on improved multiple fully convolutional generative adversarial neural networks (MCGAN) was proposed. Firstly, two-dimensional time-frequency features are extracted from the raw data using a Short-Time Fourier Transform (STFT). Secondly, by incorporating multiple CGANs of different scales and a hybrid loss function, the original GAN network was enhanced to learn the intrinsic distribution of bearing vibration signals and generate diverse vibration signals with distinct bearing fault characteristics, resulting in an expanded dataset. Finally, a comparative experiment was conducted using real wind turbine generator-bearing data. The results demonstrate that the augmented samples generated by MCGAN contain rolling bearing fault information while maintaining sample distribution and diversity. By utilizing the augmented dataset to train commonly used fault diagnostic classifiers, the diagnostic accuracy for the original vibration signals exceeds 80%, providing a theoretical basis for addressing the scarcity of fault samples in practical engineering scenarios