Fractal pore and its impact on gas adsorption capacity of outburst coal: Geological significance to coalbed gas occurrence and outburst

Pore structure and methane adsorption of coal reservoir are closely correlated to the coalbed gas occurrence and outburst. Full-scale pore structure and its fractal heterogeneity of coal samples were quantitatively characterized using low-pressure N2 gas adsorption (LP-N2GA) and high-pressure mercury intrusion porosimetry (HP-MIP). Fractal pore structure and adsorption capacities between outburst and nonoutburst coals were compared, and their geological significance to gas occurrence and outburst was discussed. The results show that pore volume (PV) is mainly contributed by macropores ( \u3e 1000 nm) and mesopores (100-1000 nm), while specific surface area (SSA) is dominated by micropores ( \u3c 10 nm) and transition pores (10 - 100 nm). On average, the PV and SSA of outburst coal samples are 4.56 times and 5.77 times those of nonoutburst coal samples, respectively, which provide sufficient place for gas adsorption and storage. The pore shape is dominated by semiclosed pores in the nonoutburst coal, whereas open pores and inkbottle pores are prevailing in the outburst coal. The pore size is widely distributed in the outburst coal, in which not only micropores are dominant, but also, transition pores and mesopores are developed to a certain extent. Based on the data from HP-MIP and LP-N2GA, pore spatial structure and surface are of fractal characteristics with fractal dimensions Dm1 (2.81 - 2.97) and Dn (2.50 - 2.73) calculated by Menger model and Frenkel-Halsey-Hill (FHH) model, respectively. The pore structure in the outburst coal is more heterogeneous as its Dn and Dm1 are generally larger than those of the nonoutburst coal. The maximum methane adsorption capacities (VL: 15.34 - 20.86 cm 3 / g) of the outburst coal are larger than those of the nonoutburst coal (VL : 9.97-13.51cm 3 / g). The adsorptivity of coal samples is governed by the micropores, transition pores, and Dn because they are positively correlated with the SSA. The outburst coal belongs to tectonically deformed coal (TDC) characterized by weak strength, rich microporosity, complex pore structure, strong adsorption capacity, but poor pore connectivity because of inkbottle pores. Therefore, the area of TDC is at high risk for gas outburst as there is a high-pressure gas sealing zone with abundant gas enrichment but limited gas migration and extraction

Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images

This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller sizes and larger quantities per image, increasing the difficulty of manual annotation. Meanwhile, the advanced SSOD technique can train superior detectors by leveraging limited labeled data and massive unlabeled data, saving annotation costs. However, as an understudied task in aerial images, SSOD suffers from a drastic performance drop when facing a large proportion of small objects. By analyzing the predictions between small and large objects, we identify three imbalance issues caused by the scale bias, i.e., pseudo-label imbalance, label assignment imbalance, and negative learning imbalance. To tackle these issues, we propose a novel Scale-discriminative Semi-Supervised Object Detection (S^3OD) learning pipeline for aerial images. In our S^3OD, three key components, Size-aware Adaptive Thresholding (SAT), Size-rebalanced Label Assignment (SLA), and Teacher-guided Negative Learning (TNL), are proposed to warrant scale unbiased learning. Specifically, SAT adaptively selects appropriate thresholds to filter pseudo-labels for objects at different scales. SLA balances positive samples of objects at different scales through resampling and reweighting. TNL alleviates the imbalance in negative samples by leveraging information generated by a teacher model. Extensive experiments conducted on the DOTA-v1.5 benchmark demonstrate the superiority of our proposed methods over state-of-the-art competitors. Codes will be released soon

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

PurposeImage segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.MethodsForty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.ResultsIn geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).ConclusionsCNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods

Relativistic quantum transport theory of hadronic matter: the coupled nucleon, delta and pion system

We derive the relativistic quantum transport equation for the pion distribution function based on an effective Lagrangian of the QHD-II model. The closed time-path Green's function technique, the semi-classical, quasi-particle and Born approximation are employed in the derivation. Both the mean field and collision term are derived from the same Lagrangian and presented analytically. The dynamical equation for the pions is consistent with that for the nucleons and deltas which we developed before. Thus, we obtain a relativistic transport model which describes the hadronic matter with $N$, $\Delta$ and $\pi$ degrees of freedom simultaneously. Within this approach, we investigate the medium effects on the pion dispersion relation as well as the pion absorption and pion production channels in cold nuclear matter. In contrast to the results of the non-relativistic model, the pion dispersion relation becomes harder at low momenta and softer at high momenta as compared to the free one, which is mainly caused by the relativistic kinetics. The theoretically predicted free $\pi N \to \Delta$ cross section is in agreement with the experimental data. Medium effects on the $\pi N \to \Delta$ cross section and momentum-dependent $\Delta$-decay width are shown to be substantial.Comment: 66 pages, Latex, 12 PostScript figures included; replaced by the revised version, to appear in Phys. Rev.

Experimental investigations of CO2 adsorption behavior in shales: Implication for CO2 geological storage

Injecting CO2 into shale reservoirs has dual benefits for enhancing gas recovery and CO2 geological sequestration, which is of great significance to ensuring energy security and achieving the “Carbon Neutrality” for China. The CO2 adsorption behavior in shales largely determined the geological sequestration potential but remained uncharted. In this study, the combination of isothermal adsorption measurement and basic petro-physical characterization methods were performed to investigate CO2 adsorption mechanism in shales. Results show that the CO2 sorption capacity increase gradually with injection pressure before reaching an asymptotic maximum magnitude, which can be described equally well by the Langmuir model. TOC content is the most significant control factor on CO2 sorption capacity, and the other secondary factors include vitrinite reflectance, clay content, and brittle mineral content. The pore structure parameter of BET-specific surface area is a more direct factor affecting CO2 adsorption of shale than BJH pore volume. Langmuir CO2 adsorption capacity positive correlated with the surface fractal dimension (D1), but a significant correlation is not found with pore structure fractal dimension (D2). By introducing the Carbon Sequestration Leaders Forum and Department of Energy methods, the research results presented in this study can be extended to the future application for CO2 geological storage potential evaluation in shales

Learning and Memory Alterations Are Associated with Hippocampal N-acetylaspartate in a Rat Model of Depression as Measured by 1H-MRS

It is generally accepted that cognitive processes, such as learning and memory, are affected in depression. The present study used a rat model of depression, chronic unpredictable mild stress (CUMS), to determine whether hippocampal volume and neurochemical changes were involved in learning and memory alterations. A further aim was to determine whether these effects could be ameliorated by escitalopram treatment, as assessed with the non-invasive techniques of structural magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). Our results demonstrated that CUMS had a dramatic influence on spatial cognitive performance in the Morris water maze task, and CUMS reduced the concentration of neuronal marker N-acetylaspartate (NAA) in the hippocampus. These effects could be significantly reversed by repeated administration of escitalopram. However, neither chronic stress nor escitalopram treatment influenced hippocampal volume. Of note, the learning and memory alterations of the rats were associated with right hippocampal NAA concentration. Our results indicate that in depression, NAA may be a more sensitive measure of cognitive function than hippocampal volume

Genome-Wide Identification of <i>bHLH</i> Transcription Factor in <i>Medicago sativa</i> in Response to Cold Stress

Alfalfa represents one of the most important legume forages, and it is also applied as an organic fertilizer to improve soil quality. However, this perennial plant is native to warmer temperate regions, and its valuable cold-acclimation-related regulatory mechanisms are still less known. In higher plants, the bHLH transcription factors play pleiotropic regulatory roles in response to abiotic stresses. The recently released whole genome sequencing data of alfalfa allowed us to identify 469 MsbHLHs by multi-step homolog search. Herein, we primarily identified 65 MsbHLH genes that significantly upregulated under cold stress, and such bHLHs were classified into six clades according to their expression patterns. Interestingly, the phylogenetic analysis and conserved motif screening of the cold-induced MsbHLHs showed that the expression pattern is relatively varied in each bHLH subfamily, this result indicating that the 65 MsbHLHs may be involved in a complex cold-responsive regulatory network. Hence, we analyzed the TFBSs at promoter regions that unraveled a relatively conserved TFBS distribution with genes exhibiting similar expression patterns. Eventually, to verify the core components involved in long-term cold acclimation, we examined transcriptome data from a freezing-tolerant species (cv. Zhaodong) in the field and compared the expression of cold-sensitive/tolerant subspecies of alfalfa, giving 11 bHLH as candidates, which could be important for further cold-tolerance enhancement and molecular breeding through genetic engineering in alfalfa