Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock propert...Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.展开更多
Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To ov...Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To overcome the challenges of subjectivity,low efficiency,and high expertise requirements in describing rock thin sections,we design a multimodal mapping network,ThinGPT,which aligns the feature spaces of the contrastive language-image pre-training(CLIP)and Generative Pre-trained(GPT-2)through network training.Given the high frequency of keywords and the structured sentence patterns in thin-section descriptions,we introduce a tokenization method tailored for rock thin sections.This approach enhances GPT-2's ability to effectively encode text and produce text feature vectors.We conducted comparative experiments using ThinGPT and other models on common sedimentary rocks.The results demonstrate that ThinGPT exhibits excellent potential in generating thin-section feature descriptions of rocks.Based on the geological expert evaluation criteria proposed in this study,ThinGPT achieved a score of 1.62 on the test set.For model complexity,ThinGPT avoids heavy initial training of large language models(LLMs).This training strategy makes the model lighter and improves the efficiency of rock thin section descriptions.As an innovative application of a LLMs within a lightweight architecture for rock thin section description,ThinGPT has significant implications for intelligent geology,geophysics,and petroleum exploration.展开更多
目的利用机器学习算法构建新型冠状病毒肺炎(COVID-19)患者临床结局的预测模型,并探索结局相关因子。方法收集2020年2月5日至4月15日武汉市火神山医院及华中科技大学同济医学院附属同济医院光谷院区收治的COVID-19患者的临床指标与结局...目的利用机器学习算法构建新型冠状病毒肺炎(COVID-19)患者临床结局的预测模型,并探索结局相关因子。方法收集2020年2月5日至4月15日武汉市火神山医院及华中科技大学同济医学院附属同济医院光谷院区收治的COVID-19患者的临床指标与结局(院内死亡和院内接受气管插管治疗),利用人工神经网络(ANN)、朴素贝叶斯、logistic回归、随机森林4种机器学习算法构建患者临床结局的预测模型。结果共纳入4 804例COVID-19患者,其中发生院内死亡100例(2.08%)、接受气管插管治疗87例(1.81%)。与院内死亡相关性最强的变量为白细胞计数、白蛋白、钙离子、血尿素氮、心肌型肌酸激酶同工酶和年龄,与院内接受气管插管治疗相关性最强的变量为白细胞计数、淋巴细胞绝对值、超敏CRP、总胆红素、钙离子和年龄,分别利用以上变量、基于4种机器学习算法构建院内死亡和院内接受气管插管治疗预测模型。4种预测模型中,相较于基于ANN、logistic回归、随机森林算法构建的模型[预测院内死亡的AUC值(95% CI)分别为0.938(0.882~0.993)、0.926(0.865~0.987)、0.867(0.780~0.954),预测院内接受气管插管治疗的AUC值(95% CI)分别为0.932(0.814~0.980)、0.935(0.817~0.981)、0.936(0.921~0.972)],基于朴素贝叶斯算法构建的模型在预测COVID-19患者院内死亡(AUC=0.952,95% CI 0.925~0.979)和接受气管插管治疗(AUC=0.948,95% CI 0.896~0.965)方面性能均最佳。结论 4种机器学习算法在预测COVID-19患者临床结局方面性能良好,其中以基于朴素贝叶斯算法构建的预测模型最佳。白细胞计数、白蛋白、钙离子、血尿素氮、心肌型肌酸激酶同工酶和年龄可以用来预测COVID-19患者院内死亡,白细胞计数、淋巴细胞绝对值、超敏CRP、总胆红素、钙离子和年龄可以用来预测患者院内是否接受气管插管治疗。展开更多
通过温室盆栽试验,研究构树(Broussonetia papyrifera)生长对重金属污染土壤酶活性和微生物群落结构的影响.结果表明,构树修复污染土壤中酶活性和微生物多样性明显提高.经270d培养后,构树生长土壤中蔗糖酶和酸性磷酸酶活性与种植土壤相...通过温室盆栽试验,研究构树(Broussonetia papyrifera)生长对重金属污染土壤酶活性和微生物群落结构的影响.结果表明,构树修复污染土壤中酶活性和微生物多样性明显提高.经270d培养后,构树生长土壤中蔗糖酶和酸性磷酸酶活性与种植土壤相比分别显著(P<0.05)提高3.12倍和2.29倍;土壤脱氢酶与有效态As、Cd、Pb、Zn和Cu含量,蔗糖酶与有效态Cd含量,以及磷酸酶与有效态Cd和Cu含量之间呈显著负相关(P<0.05).根据16S和18S r DNA PCR-DGGE分析表明,构树修复可提高污染土壤中细菌和丛枝菌根真菌多样性.上述结果表明,构树修复可有效改善重金属污染土壤的环境质量.然而,污染土壤中重金属有效态含量下降不明显,必须辅助物理和化学措施来强化构树对重金属污染土壤的生态修复潜力.展开更多
基金supported by the Shandong Provincial Natural Science Foundation(ZR2024MD116)National Natural Science Foundation of China(Grant Nos.42174143,42004098)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.
基金supported by a grant from the National Natural Science Foundation of China(Grant No.42174156).
文摘Rock thin section description is an essential method for examining lithology,structure,diagenesis,and sedimentary environment,playing a pivotal role in fields such as geology,geophysics,and petroleum exploration.To overcome the challenges of subjectivity,low efficiency,and high expertise requirements in describing rock thin sections,we design a multimodal mapping network,ThinGPT,which aligns the feature spaces of the contrastive language-image pre-training(CLIP)and Generative Pre-trained(GPT-2)through network training.Given the high frequency of keywords and the structured sentence patterns in thin-section descriptions,we introduce a tokenization method tailored for rock thin sections.This approach enhances GPT-2's ability to effectively encode text and produce text feature vectors.We conducted comparative experiments using ThinGPT and other models on common sedimentary rocks.The results demonstrate that ThinGPT exhibits excellent potential in generating thin-section feature descriptions of rocks.Based on the geological expert evaluation criteria proposed in this study,ThinGPT achieved a score of 1.62 on the test set.For model complexity,ThinGPT avoids heavy initial training of large language models(LLMs).This training strategy makes the model lighter and improves the efficiency of rock thin section descriptions.As an innovative application of a LLMs within a lightweight architecture for rock thin section description,ThinGPT has significant implications for intelligent geology,geophysics,and petroleum exploration.
文摘目的利用机器学习算法构建新型冠状病毒肺炎(COVID-19)患者临床结局的预测模型,并探索结局相关因子。方法收集2020年2月5日至4月15日武汉市火神山医院及华中科技大学同济医学院附属同济医院光谷院区收治的COVID-19患者的临床指标与结局(院内死亡和院内接受气管插管治疗),利用人工神经网络(ANN)、朴素贝叶斯、logistic回归、随机森林4种机器学习算法构建患者临床结局的预测模型。结果共纳入4 804例COVID-19患者,其中发生院内死亡100例(2.08%)、接受气管插管治疗87例(1.81%)。与院内死亡相关性最强的变量为白细胞计数、白蛋白、钙离子、血尿素氮、心肌型肌酸激酶同工酶和年龄,与院内接受气管插管治疗相关性最强的变量为白细胞计数、淋巴细胞绝对值、超敏CRP、总胆红素、钙离子和年龄,分别利用以上变量、基于4种机器学习算法构建院内死亡和院内接受气管插管治疗预测模型。4种预测模型中,相较于基于ANN、logistic回归、随机森林算法构建的模型[预测院内死亡的AUC值(95% CI)分别为0.938(0.882~0.993)、0.926(0.865~0.987)、0.867(0.780~0.954),预测院内接受气管插管治疗的AUC值(95% CI)分别为0.932(0.814~0.980)、0.935(0.817~0.981)、0.936(0.921~0.972)],基于朴素贝叶斯算法构建的模型在预测COVID-19患者院内死亡(AUC=0.952,95% CI 0.925~0.979)和接受气管插管治疗(AUC=0.948,95% CI 0.896~0.965)方面性能均最佳。结论 4种机器学习算法在预测COVID-19患者临床结局方面性能良好,其中以基于朴素贝叶斯算法构建的预测模型最佳。白细胞计数、白蛋白、钙离子、血尿素氮、心肌型肌酸激酶同工酶和年龄可以用来预测COVID-19患者院内死亡,白细胞计数、淋巴细胞绝对值、超敏CRP、总胆红素、钙离子和年龄可以用来预测患者院内是否接受气管插管治疗。
文摘通过温室盆栽试验,研究构树(Broussonetia papyrifera)生长对重金属污染土壤酶活性和微生物群落结构的影响.结果表明,构树修复污染土壤中酶活性和微生物多样性明显提高.经270d培养后,构树生长土壤中蔗糖酶和酸性磷酸酶活性与种植土壤相比分别显著(P<0.05)提高3.12倍和2.29倍;土壤脱氢酶与有效态As、Cd、Pb、Zn和Cu含量,蔗糖酶与有效态Cd含量,以及磷酸酶与有效态Cd和Cu含量之间呈显著负相关(P<0.05).根据16S和18S r DNA PCR-DGGE分析表明,构树修复可提高污染土壤中细菌和丛枝菌根真菌多样性.上述结果表明,构树修复可有效改善重金属污染土壤的环境质量.然而,污染土壤中重金属有效态含量下降不明显,必须辅助物理和化学措施来强化构树对重金属污染土壤的生态修复潜力.
