A review of shale pore structure evolution characteristics with increasing thermal maturities

   Pore structure has a significant effect on the occurrence state of shale hydrocarbons and the hydrocarbon storage capability of shale reservoirs. Consequently, it is quite meaningful to clarify the shale pore structure evolution characteristics for understanding the migration and enrichment mechanisms of hydrocarbons within shale reservoirs during different geological stages. The abundant existence of organic matter within shales complicates the shale pore structure evolution process by hydrocarbon generation, migration and cracking. Many studies have been conducted to reveal the shale pore structure evolution characteristics and the controlling factors. Basically, these studies could be divided into two categories based on the sample source: comparing the pore structure of natural shale samples with different thermal maturities; obtaining shale samples with different thermal maturities by conducting thermal simulation experiments on low-mature shale samples and comparing the pore structure of these simulated shale samples. However, no consistent viewpoint on shale pore structure evolution has been reached. This review presents the state of the art of shale pore structure evolution studies. It is widely recognized in the literature that both the inorganic and organic diagenesis control the shale pore structure evolution process. However, it is found that the shale pore structure evolution models proposed in the literature were largely dependent on the samples used. And it is recommended to conduct the two categories of studies simultaneously in order to obtain more reliable shale pore structure evolution characteristics in future investigations.Cited as: Gao, Z., Fan, Y., Xuan, Q., Zheng, G. A review of shale pore structure evolution characteristics with increasing thermal maturities. Advances in Geo-Energy Research, 2020, 4(3): 247-259, doi: 10.46690/ager.2020.03.0

Comparative study of bin and bulk microphysical schemes in simulating a heavy snowfall event that occurred in Beijing during the 2022 Winter Olympic Games

A heavy snowfall event that struck Beijing during February 12-13, 2022, affected some of the training sessions and events of the Winter Olympic Games. This heavy snowfall event was simulated using the Advanced Research Weather Research and Forecasting Model with both the two-moment bulk scheme (BULK) and the spectral bin microphysics scheme (BIN), and the differences in surface precipitation, radar reflectivity, and cloud microphysics processes were compared and analyzed. It was found that surface precipitation was dominated by solid precipitation particles. The 24-h accumulated precipitation of the BULK simulation was larger than that of the BIN simulation, but both were smaller than that observed. The BIN simulation was closer to the observations in terms of the trends of variation in precipitation rate and radar reflectivity during the period of heavy precipitation. The maximum and minimum vertical velocities of the BIN simulation were notably higher than those of the BULK simulation, and the water vapor content of the BIN scheme at the heights of the −10 to −20°C levels and above the −38°C level was substantially higher than that of the BULK scheme. The contents of cloud water and snow simulated by the BIN scheme were much higher than those simulated by the BULK scheme. The nucleation of ice crystals in the middle and high layers of the BULK scheme was obvious, whereas such a process was not evident in the BIN scheme. The net production rate of ice crystals and snow simulated by the BULK scheme was stronger near the surface than that simulated by the BIN scheme, and a second peak in the conversion rate existed at heights very close to the surface below 1 km, which might account for the greater intensity of precipitation in the BULK scheme. The latent heat simulated by the BULK scheme was larger (smaller) than that simulated by the BIN scheme below (above) the height of 2 km

The Typhoon Wind Hazard Assessment Considering the Correlation among the Key Random Variables Using the Copula Method

The probability distribution of typhoon key parameters is commonly incorporated into typhoon models to estimate the typhoon-induced wind speeds associated with certain return periods in typhoon-prone regions. In most studies that focus on the typhoon wind hazards of the southeast coastline of China, the typhoon key parameters are assumed to be independent. This paper develops a copula-based joint probability distribution for the typhoon key parameters to investigate its potential influence on the typhoon wind hazard on the southeast coastline of China. To this end, the best track typhoon data from the China meteorological administration are used to extract the key parameters of the typhoon. The analyses show that the observed correlation coefficients among the parameters could be larger than 0.4 at some locations on the considered coastline. The C-vine copula is then employed to establish the joint probabilistic model of these key parameters. Comparison between the observed and modeled joint probability distributions suggests the adequacy of the copula method-based probability distribution model. Then, a local track model and a typhoon wind field model are assembled to simulate the history of the typhoon-induced surface wind given the typhoon key parameters. Finally, Monte Carlo simulation is adopted to estimate the wind speed associated with 50- and 100-year return periods. Results show that neglecting the correlation among the typhoon key parameters could cause a relative difference of up to 7% at some locations on the coastline

A novel lysosome-related gene signature coupled with gleason score for prognosis prediction in prostate cancer

Background: Prostate cancer (PCa) is highly heterogeneous, which makes it difficult to precisely distinguish the clinical stages and histological grades of tumor lesions, thereby leading to large amounts of under- and over-treatment. Thus, we expect the development of novel prediction approaches for the prevention of inadequate therapies. The emerging evidence demonstrates the pivotal role of lysosome-related mechanisms in the prognosis of PCa. In this study, we aimed to identify a lysosome-related prognostic predictor in PCa for future therapies.Methods: The PCa samples involved in this study were gathered from The Cancer Genome Atlas database (TCGA) (n = 552) and cBioPortal database (n = 82). During screening, we categorized PCa patients into two immune groups based on median ssGSEA scores. Then, the Gleason score and lysosome-related genes were included and screened out by using a univariate Cox regression analysis and the least absolute shrinkage and selection operation (LASSO) analysis. Following further analysis, the probability of progression free interval (PFI) was modeled by using unadjusted Kaplan–Meier estimation curves and a multivariable Cox regression analysis. A receiver operating characteristic (ROC) curve, nomogram and calibration curve were used to examine the predictive value of this model in discriminating progression events from non-events. The model was trained and repeatedly validated by creating a training set (n = 400), an internal validation set (n = 100) and an external validation (n = 82) from the cohort.Results: Following grouping by ssGSEA score, the Gleason score and two LRGs—neutrophil cytosolic factor 1 (NCF1) and gamma-interferon-inducible lysosomal thiol reductase (IFI30)—were screened out to differentiate patients with or without progression (1-year AUC = 0.787; 3-year AUC = 0.798; 5-year AUC = 0.772; 10-year AUC = 0.832). Patients with a higher risk showed poorer outcomes (p < 0.0001) and a higher cumulative hazard (p < 0.0001). Besides this, our risk model combined LRGs with the Gleason score and presented a more accurate prediction of PCa prognosis than the Gleason score alone. In three validation sets, our model still achieved high prediction rates.Conclusion: In conclusion, this novel lysosome-related gene signature, coupled with the Gleason score, works well in PCa for prognosis prediction

Semi-supervised learning for forest fire segmentation using UAV imagery

Unmanned aerial vehicles (UAVs) are an efficient tool for monitoring forest fire due to its advantages, e.g., cost-saving, lightweight, flexible, etc. Semantic segmentation can provide a model aircraft to rapidly and accurately determine the location of a forest fire. However, training a semantic segmentation model requires a large number of labeled images, which is labor-intensive and time-consuming to generate. To address the lack of labeled images, we propose, in this paper, a semi-supervised learning-based segmentation network, SemiFSNet. By taking into account the unique characteristics of UAV-acquired imagery of forest fire, the proposed method first uses occlusion-aware data augmentation for labeled data to increase the robustness of the trained model. In SemiFSNet, a dynamic encoder network replaces the ordinary convolution with dynamic convolution, thus enabling the learned feature to better represent the fire feature with varying size and shape. To mitigate the impact of complex scene background, we also propose a feature refinement module by integrating an attention mechanism to highlight the salient feature information, thus improving the performance of the segmentation network. Additionally, consistency regularization is introduced to exploit the rich information that unlabeled data contain, thus aiding the semi-supervised learning. To validate the effectiveness of the proposed method, extensive experiments were conducted on the Flame dataset and Corsican dataset. The experimental results show that the proposed model outperforms state-of-the-art methods and is competitive to its fully supervised learning counterpart

Hidden Sp(2s+1)- or SO(2s+1)-symmetry and new exactly solvable models in ultracold atomic systems

The high spin ultracold atom models with a special form of contact interactions, i.e., the scattering lengthes in the total spin-$2,4 \cdots$ channels are equal but may be different from that in the spin-0 channel, is studied. It is found that those models have either $Sp(2s+1)$-symmetry for the fermions or $SO(2s+1)$-symmetry for the bosons in the spin sector. Based on the symmetry analysis, a new class of exactly solvable models is proposed and solved via the Bethe ansatz. The ground states for repulsive fermions are also discussed.Comment: 6 pages, 2 figure

CHURP: Dynamic-Committee Proactive Secret Sharing

We introduce CHURP (CHUrn-Robust Proactive secret sharing). CHURP enables secure secret-sharing in dynamic settings, where the committee of nodes storing a secret changes over time. Designed for blockchains, CHURP has lower communication complexity than previous schemes: $O(n)$ on-chain and $O(n^2)$ off-chain in the optimistic case of no node failures. CHURP includes several technical innovations: An efficient new proactivization scheme of independent interest, a technique (using asymmetric bivariate polynomials) for efficiently changing secret-sharing thresholds, and a hedge against setup failures in an efficient polynomial commitment scheme. We also introduce a general new technique for inexpensive off-chain communication across the peer-to-peer networks of permissionless blockchains. We formally prove the security of CHURP, report on an implementation, and present performance measurements

Insight-HXMT on-orbit thermal control status and thermal deformation impact analysis

Purpose: The Hard X-ray Modulation Telescope is China's first X-ray astronomy satellite launched on June 15th, 2017, dubbed Insight-HXMT. Active and passive thermal control measures are employed to keep devices at suitable temperatures. In this paper, we analyzed the on-orbit thermal monitoring data of the first 5 years and investigated the effect of thermal deformation on the point spread function (PSF) of the telescopes. Methods: We examined the data of the on-orbit temperatures measured using 157 thermistors placed on the collimators, detectors and their support structures and compared the results with the thermal control requirements. The thermal deformation was evaluated by the relative orientation of the two star sensors installed on the main support structure. its effect was estimated with evolution of the PSF obtained with calibration scanning observations of the Crab nebula. Conclusion: The on-orbit temperatures met the thermal control requirements thus far, and the effect of thermal deformation on the PSF was negligible after the on-orbit pointing calibration.Comment: 25 pages, 35 figures, submitte