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

The effects of sex hormones during the menstrual cycle on knee kinematics

The effects of the menstrual cycle and sex hormones on knee kinematics remain unclear. The purpose of the study was to investigate the effects of the menstrual cycle and serum sex hormone concentrations on knee kinematic parameters of the 90°cutting in female college soccer athletes. Three female college soccer teams (53 subjects) participated in the study. During the first menstrual cycle, a three-step method was used to exclude subjects with anovulatory and luteal phase–deficient (LPD) (12 subjects). The subjects’ menstrual cycle was divided into the menstrual phase, late-follicular phase, ovulatory phase, and mid-luteal phase (group 1, 2, 3, 4). In each phase of the second menstrual cycle, we used a portable motion analysis system to enter the teams and tested the sex hormones concentrations and knee kinematics parameters in three universities in turn. We found that subjects had a lower maximum knee valgus in group 4 compared with other groups. This meant that subjects had a lower biomechanical risk of non-contact anterior cruciate ligament (ACL) injury in the mid-luteal phase. There was no significant correlation between serum estrogen, progesterone concentration, and knee kinematic parameters. This meant that sex hormones did not have a protective effect. Future studies need to incorporate more factors (such as neuromuscular control, etc.) to investigate

Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis

Abstract Purpose The contouring of organs at risk (OARs) in head and neck cancer radiation treatment planning is a crucial, yet repetitive and time-consuming process. Recent studies have applied deep learning (DL) algorithms to automatically contour head and neck OARs. This study aims to conduct a systematic review and meta-analysis to summarize and analyze the performance of DL algorithms in contouring head and neck OARs. The objective is to assess the advantages and limitations of DL algorithms in contour planning of head and neck OARs. Methods This study conducted a literature search of Pubmed, Embase and Cochrane Library databases, to include studies related to DL contouring head and neck OARs, and the dice similarity coefficient (DSC) of four categories of OARs from the results of each study are selected as effect sizes for meta-analysis. Furthermore, this study conducted a subgroup analysis of OARs characterized by image modality and image type. Results 149 articles were retrieved, and 22 studies were included in the meta-analysis after excluding duplicate literature, primary screening, and re-screening. The combined effect sizes of DSC for brainstem, spinal cord, mandible, left eye, right eye, left optic nerve, right optic nerve, optic chiasm, left parotid, right parotid, left submandibular, and right submandibular are 0.87, 0.83, 0.92, 0.90, 0.90, 0.71, 0.74, 0.62, 0.85, 0.85, 0.82, and 0.82, respectively. For subgroup analysis, the combined effect sizes for segmentation of the brainstem, mandible, left optic nerve, and left parotid gland using CT and MRI images are 0.86/0.92, 0.92/0.90, 0.71/0.73, and 0.84/0.87, respectively. Pooled effect sizes using 2D and 3D images of the brainstem, mandible, left optic nerve, and left parotid gland for contouring are 0.88/0.87, 0.92/0.92, 0.75/0.71 and 0.87/0.85. Conclusions The use of automated contouring technology based on DL algorithms is an essential tool for contouring head and neck OARs, achieving high accuracy, reducing the workload of clinical radiation oncologists, and providing individualized, standardized, and refined treatment plans for implementing "precision radiotherapy". Improving DL performance requires the construction of high-quality data sets and enhancing algorithm optimization and innovation

Study of the Microstructural Characteristics of Low-Rank Coal under Different Degassing Pressures

Low-rank coal samples from the Xishanyao Formation in the southern Junggar basin of Xinjiang were processed under different negative pressures in order to examine the microstructural characteristics of low-rank coal reservoirs. The pore structures of low-rank coal under different negative pressures were tested using scanning electron microscopy, low-temperature nitrogen adsorption–desorption, and water saturation and centrifugal low-field NMR experiments. The results showed that for the low-rank coal samples from the study area, a high portion of the porosity and surface area came from micropores and small pores; the fractal dimension of the adsorption pores of the low-rank coal samples was divided into surface fractal dimension D1 and structural fractal dimension D2, which showed that the microstructure of the low-rank coal from the study area was complex. The transverse relaxation times T2 of the low-rank coal samples in the test were approximately 0.1~2.5, approximately 10, and greater than 100 ms; the T2 spectrum had basically three peak types. By combining scanning electron microscopy and nuclear magnetic resonance tests, it was concluded that the pore connectivity of the low-rank coal reservoirs in the study area was poor and the effective porosity was relatively low, which may be unfavorable for the exploration and development of coalbed methane

Study of the Microstructural Characteristics of Low-Rank Coal under Different Degassing Pressures

Low-rank coal samples from the Xishanyao Formation in the southern Junggar basin of Xinjiang were processed under different negative pressures in order to examine the microstructural characteristics of low-rank coal reservoirs. The pore structures of low-rank coal under different negative pressures were tested using scanning electron microscopy, low-temperature nitrogen adsorption–desorption, and water saturation and centrifugal low-field NMR experiments. The results showed that for the low-rank coal samples from the study area, a high portion of the porosity and surface area came from micropores and small pores; the fractal dimension of the adsorption pores of the low-rank coal samples was divided into surface fractal dimension D1 and structural fractal dimension D2, which showed that the microstructure of the low-rank coal from the study area was complex. The transverse relaxation times T2 of the low-rank coal samples in the test were approximately 0.1~2.5, approximately 10, and greater than 100 ms; the T2 spectrum had basically three peak types. By combining scanning electron microscopy and nuclear magnetic resonance tests, it was concluded that the pore connectivity of the low-rank coal reservoirs in the study area was poor and the effective porosity was relatively low, which may be unfavorable for the exploration and development of coalbed methane

CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

Abstract Background Early and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for detecting micro-nodules (diameter < 3 mm), which increases the radiologists’ workload and causes unnecessary anxiety for the patients. To decrease the false positive rate, we propose to use CNN models to discriminate between pulmonary micro-nodules and non-nodules from CT image patches. Methods A total of 13,179 micro-nodules and 21,315 non-nodules marked by radiologists are extracted with three different patch sizes (16 × 16, 32 × 32 and 64 × 64) from LIDC/IDRI database and used in the experiments. Three CNN models with different depths (1, 2 or 4 convolutional layers) are designed; their performances are evaluated by the fivefold cross-validation in term of the accuracy, area under the curve (AUC), F-score and sensitivity. The network parameters are also optimized. Results It is found that the performance of the CNN models is greatly dependent on the patches size and the number of convolutional layers. The CNN model with two convolutional layers presented the best performance in case of 32 × 32 patches size, achieving an accuracy of 88.28%, an AUC of 0.87, a F-score of 83.45% and a sensitivity of 83.82%. Conclusions The CNN models with appropriate depth and size of image patches can effectively discriminate between pulmonary micro-nodules and non-nodules, and reduce the false positives and help manage lung cancer precisely

Cooperative estimation algorithms for multi-sensor networks with imprecise measurements

A cooperative estimation algorithm is proposed for mutli-sensor networks with imprecise measurements caused by electromagnetic interferences, abnormal currents and other faults in the multi-sensor measurement process. Adaptive schemes based on a reference model are introduced to overcome the adverse effects of multiplicative interference on the estimated information. Then, rigorous theoretical proofs are developed to analyze the adaptive estimation algorithm. Finally, numerical simulation results are carried out to verify the effectiveness of the theoretical analysis

AutoCellANLS: An Automated Analysis System for Mycobacteria-Infected Cells Based on Unstained Micrograph

The detection of Mycobacterium tuberculosis (Mtb) infection plays an important role in the control of tuberculosis (TB), one of the leading infectious diseases in the world. Recent advances in artificial intelligence-aided cellular image processing and analytical techniques have shown great promises in automated Mtb detection. However, current cell imaging protocols often involve costly and time-consuming fluorescence staining, which has become a major bottleneck for procedural automation. To solve this problem, we have developed a novel automated system (AutoCellANLS) for cell detection and the recognition of morphological features in the phase-contrast micrographs by using unsupervised machine learning (UML) approaches and deep convolutional neural networks (CNNs). The detection algorithm can adaptively and automatically detect single cells in the cell population by the improved level set segmentation model with the circular Hough transform (CHT). Besides, we have designed a Cell-net by using the transfer learning strategies (TLS) to classify the virulence-specific cellular morphological changes that would otherwise be indistinguishable to the naked eye. The novel system can simultaneously classify and segment microscopic images of the cell populations and achieve an average accuracy of 95.13% for cell detection, 95.94% for morphological classification, 94.87% for sensitivity, and 96.61% for specificity. AutoCellANLS is able to detect significant morphological differences between the infected and uninfected mammalian cells throughout the infection period (2 hpi/12 hpi/24 hpi). Besides, it has overcome the drawback of manual intervention and increased the accuracy by more than 11% compared to our previous work, which used AI-aided imaging analysis to detect mycobacterial infection in macrophages. AutoCellANLS is also efficient and versatile when tailored to different cell lines datasets (RAW264.7 and THP-1 cell). This proof-of concept study provides a novel venue to investigate bacterial pathogenesis at a macroscopic level and offers great promise in the diagnosis of bacterial infections

Carbon Microspheres with Cr(VI) Adsorption Performance were Prepared by In-situ Hydrothermal Carbonization Method

Biochar material is a renewable adsorbent widely used for treating contaminated wastewater. The hydrothermal carbon (HTC) were prepared from low polymeric sugars and low concentration glucose under hydrothermal carbonization reactions without using dispersants. The composition and structure of the biochar produced were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), Raman spectroscopy (Raman), and N2 adsorption-desorption, indicating that amorphous graphitic carbon was obtained. Experimental results from the static adsorption of Cr(VI)-contaminated wastewater showed that HTCP-2 exhibited the highest adsorption capacity for Cr(VI), with a maximum adsorption capacity of 22.62 mg.g−1.The adsorption Cr(VI), MB, and RhB by the synthesized biochar all conformed to the pseudo-second-order kinetic model and Freundlich isotherm, suggesting a multilayer chemical adsorption process. Additionally, the synthesized HTC surface is enriched with a significant amount of oxygen-rich functional groups, which also has good adsorption performance for cationic dyes. Furthermore, the test results of fluorescence, photocurrent, and impedance indicate that HTCP-2 possesses the ability to generate and separate photoinduced charge carriers. This implied that HTCP-2 can be used for the preparation of adsorption photocatalysts, which effectively remove environmental pollutants through the synergistic effect of adsorption-photocatalysis. This study provides a research foundation for advancing water treatment technologies. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)

Diagnosis Value of the Detection of CYFRA21-1 in Non-small Cell Lung Cancer

Background and objective Cytokeratin-19 fragment (CYFRA21-1) is a soluble protein in serum, and may be a useful circulating tumor marker. The aim of this study is to investigate the diagnostic value of the peripheral blood CYFRA21-1 in non-small cell lung cancer (NSCLC). Methods The levels of peripheral blood CYFRA21-1 were detected in 107 patients with NSCLC and 51 patients with benign pulmonary diseases by enzyme linked immunosorbent assay, and ROC curve was used to analyse the results. Results Singificant difference of peripheral blood CYFRA21-1 levels was detected between the NSCLC group and benign pulmonary disease group (χ2=47.343, P &lt; 0.001). At the threshold of 3.3 ng/mL, sensitivity and specificity of CYFRA21-1 as a serologic marker were 74.77% and 76.47%, respectively for any cancer. ROC curve showed that the under-curve area (AUC) of CYFRA21-1 was 0.813 9. There was no significant difference of CYFRA21-1 between subtypes of NSCLC (χ2=0.450, P=0.799). The peripheral blood CYFRA21-1 level was elevated significantly in the patients with extensive disease (IIIb, IV) compared with patients with limited disase (I, II, IIIb) (χ2=7.057, P=0.008). Conclusion As a tumor marker CYFRA21-1 has relative high sensitivity and specificity for the diagnosis of NSCLC. Elevated peripheral blood CYFRA 21-1 levels were usually indicated extensive disease of NSCLC