Landslides are one of the most significant types of geological disasters worldwide.However,current landslide disaster recognition models generally face issues such as low training efficiency,reliance on large amounts ...Landslides are one of the most significant types of geological disasters worldwide.However,current landslide disaster recognition models generally face issues such as low training efficiency,reliance on large amounts of samples,and vague feature extraction.To address these problems,this paper proposes an enhanced intelligent recognition model based on an improved residual network.Taking remote sensing images from landslide-prone areas in Bijie City,Guizhou Province,China as the research subject,we implemented data augmentation techniques to expand the datasets,subsequently partitioning it into training and validation subsets at a73:27 ratio.During model training,a transfer learning strategy was employed to improve training efficiency through the utilization of ImageNet pre-trained weights.The Res Net50 network architecture was enhanced through the integration of channel attention and spatial attention mechanisms,enabling more effective extraction of critical landslide features.Comparative analysis indicates that after introducing the attention mechanisms,the enhanced model achieved an increase of 1.49% in accuracy,0.45% in precision,1.52% in F1 score,and 0.0297 in Kappa coefficient on the validation set.Notably,recall improved by 2.59%,indicating that the improved model has enhanced landslide disaster recognition capability and overall performance.This study successfully coupled the transfer learning strategy with a dual-attention mechanism into the ResNet50 architecture,allowing the rapid construction of an efficient recognition model under limited sample conditions,significantly improving the comprehensive performance and generalisation ability of the landslide recognition model.展开更多
目的好氧甲基营养菌在缺氧环境中的生存机制是当前微生物生态学的研究热点。本研究旨在探究好氧甲基营养菌——嗜甲基菌属(Methylophilus)菌株在缺氧条件下利用不溶性铁矿物(水铁矿)进行胞外电子转移(extracellular electron transfer,E...目的好氧甲基营养菌在缺氧环境中的生存机制是当前微生物生态学的研究热点。本研究旨在探究好氧甲基营养菌——嗜甲基菌属(Methylophilus)菌株在缺氧条件下利用不溶性铁矿物(水铁矿)进行胞外电子转移(extracellular electron transfer,EET)的机制,阐明外源与内源电子穿梭体在该过程中的协同作用。方法以分离自抚仙湖沉积物的好氧甲基营养菌Methylophilus sp.14为研究对象,在缺氧条件(初始O2为2%)下开展以甲醇为碳源、水铁矿为唯一终端电子受体的培养实验。通过铁还原动力学测定、电化学分析(差分脉冲伏安法、循环伏安法)及显微表征(扫描/透射电子显微镜)系统评估其铁呼吸能力,并探究外源穿梭体[腐殖质(humic substances,HS)、蒽醌-2,6-二磺酸(anthraquinone-2,6-disulfonate,AQDS)]与内源黄素(类)物质在电子传递中的功能。结果Methylophilus sp.14能够耦合甲醇氧化与水铁矿还原,在20 d内将Fe(II)浓度从0.49μmol/L提升至8.29μmol/L,并促使部分水铁矿转化为磁铁矿。外源添加腐殖质或AQDS可使Fe(II)产量进一步提高至10.73μmol/L和11.22μmol/L,电子转移效率提升约1.5倍。电化学分析表明菌体还原电位低于水铁矿,支持电子自发传递;可溶性AQDS可形成“导电微环境”加速电子传递。研究发现,该菌可合成并分泌黄素(类)物质,其浓度与EET速率显著正相关(r=0.94,P<0.001),且外源穿梭体能刺激总黄素分泌量增加30%-50%。黄素化合物作为关键“电子桥梁”,介导电子从胞内向外源穿梭体传递,形成协同电子链。结论本研究揭示了一种好氧甲基营养菌适应缺氧环境的新颖EET策略:外源电子穿梭体通过构建胞外导电微环境并刺激黄素(类)物质的分泌,形成内外源协同的电子传递机制,从而高效驱动固态铁矿物的还原。该发现深化了对好氧微生物代谢灵活性及其在氧化-缺氧界面生态功能的理解。展开更多
基金supported by the National Natural Science Foundation of China[NSFC,Grant Nos.U22A20597,42507217]the"Unveiling and Commanding"Project of Science and Technology Program of Tibet[Grant No.XZ202303ZY0006G]the"Key Research and Development Program"Project of Science and Technology Program of Tibet[Grant Nos.XZ202501ZY0104,XZ202501ZY0132]。
摘要Landslides are one of the most significant types of geological disasters worldwide.However,current landslide disaster recognition models generally face issues such as low training efficiency,reliance on large amounts of samples,and vague feature extraction.To address these problems,this paper proposes an enhanced intelligent recognition model based on an improved residual network.Taking remote sensing images from landslide-prone areas in Bijie City,Guizhou Province,China as the research subject,we implemented data augmentation techniques to expand the datasets,subsequently partitioning it into training and validation subsets at a73:27 ratio.During model training,a transfer learning strategy was employed to improve training efficiency through the utilization of ImageNet pre-trained weights.The Res Net50 network architecture was enhanced through the integration of channel attention and spatial attention mechanisms,enabling more effective extraction of critical landslide features.Comparative analysis indicates that after introducing the attention mechanisms,the enhanced model achieved an increase of 1.49% in accuracy,0.45% in precision,1.52% in F1 score,and 0.0297 in Kappa coefficient on the validation set.Notably,recall improved by 2.59%,indicating that the improved model has enhanced landslide disaster recognition capability and overall performance.This study successfully coupled the transfer learning strategy with a dual-attention mechanism into the ResNet50 architecture,allowing the rapid construction of an efficient recognition model under limited sample conditions,significantly improving the comprehensive performance and generalisation ability of the landslide recognition model.