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Abstract:Shale gas plays an important role in reducing pollution and adjusting the structure of world energy. Gas content estimation is particularly significant in shale gas resource evaluation. There exist various estimation methods, such as first principle methods and empirical models. However, resource evaluation presents many challenges, especially the insufficient accuracy of existing models and the high cost resulting from time-consuming adsorption experiments. In this research, a low-cost and high-accuracy model based on geological parameters is constructed through statistical learning methods to estimate adsorbed shale gas content. The new model consists of two components, which are used to estimate Langmuir pressure (PL) and Langmuir volume (VL) based on their quantitative relationships with geological parameters. To increase the accuracy of the model, a "big data" set that consists of 301 data entries was compiled and utilized. Data outliers were detected by the K-Nearest Neighbor (K-NN) algorithm, and the model performance was evaluated by the leave-one-out algorithm. The proposed model was compared with four existing models. The results show that the novel model has better estimation accuracy than the previous ones. Furthermore, because all variables in the new model are not dependent on any time-consuming experimental methods, the new model has low cost and is highly efficient for approximate overall estimation of shale gas reservoirs. Finally, the proposed model was employed to estimate adsorbed gas content for nine shale gas reservoirs in China, Germany, and the U.S.A.

Mini-PCNL offers a significantly higher SFR than RIRS, for lower pole renal stones, the advantage of mini-PCNL is more obvious. However, RIRS is associated with shorter hospital stay and less hemoglobin drop. For ultramini-PCNL and micro-PCNL, tract size is smaller than mini-PCNL, and the SFR is similar to RIRS. In terms of the evidence at present, we recommend mini-PCNL for patients focusing more on the high SFR.

Chronic obstructive pulmonary disease (COPD), characterized by persistent and not fully reversible airflow restrictions, is currently one of the most widespread chronic lung diseases in the world. The most common symptoms of COPD are cough, expectoration, and exertional dyspnea. Although various strategies have been developed during the last few decades, current medical treatment for COPD only focuses on the relief of symptoms, and the reversal of lung function deterioration and improvement in patient's quality of life are very limited. Consequently, development of novel effective therapeutic strategies for COPD is urgently needed. Stem cells were known to differentiate into a variety of cell types and used to regenerate lung parenchyma and airway structures. Stem cell therapy is a promising therapeutic strategy that has the potential to restore the lung function and improve the quality of life in patients with COPD. This review summarizes the current state of knowledge regarding the clinical research on the treatment of COPD with mesenchymal stem cells (MSCs) and aims to update the understanding of the role of MSCs in COPD treatment, which may be helpful for developing effective therapeutic strategies in clinical settings.