Risk Assessment of Pipeline Engineering Geological Disaster Based on GIS and WOE-GA-BP Models

Oil and gas pipelines are part of long-distance transportation projects which pass through areas with complex geological conditions and which are prone to geological disasters. Geological disasters significantly affect the safety of pipeline operations. Therefore, it is essential to conduct geological disaster risk assessments in areas along pipelines to ensure efficient pipeline operation, and to provide theoretical support for early warning and forecasting of geological disasters. In this study, the pipeline routes of the Sichuan-Chongqing and Western Hubei management offices of the Sichuan-East Gas Transmission Project were studied. Seven topographic factors—surface elevation, topographic slope, topographic aspect, plane curvature, stratum lithology, rainfall, and vegetation coverage index—were superimposed using the laying method with a total of eight evaluation indicators. The quantitative relationships between the factors and geological disasters were obtained using the geographic information system (GIS) and weight of evidence (WOE). The backpropagation neural network (BP) was optimised using a genetic algorithm (GA) to obtain the weight of each evaluation index. The quantified index was then utilized to identify the geological hazard risk zone along the pipeline. The results showed that the laying method, stratum lithology, and normalised difference vegetation index were the factors influencing hazards

Evaluation of Drifting Snow Susceptibility Based on GIS and GA-BP Algorithms

Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in drifting snow disasters. Drifting snow disasters frequently occur in the high latitudes of northwest China. At present, most scholars are committed to studying the prevention and control measures of drifting snow, but the prerequisite for prevention is to effectively evaluate the susceptibility of drifting snow along railways and highways to identify areas with a high risk of occurrence. Taking the Xinjiang Afukuzhun Railway as an example, this study uses a geographic information system (GIS) combined with on-site monitoring and surveys to establish a drifting snow susceptibility evaluation index system. The drifting snow susceptibility index (DSSI) is calculated through the weight of an evidence (WOE) model, and a genetic algorithm backpropagation (GA-BP) algorithm is used to obtain optimised evaluation index weights to improve the accuracy of model evaluation. The results show that the accuracies of the WOE model, WOE backpropagation (WOE-BP) model, and weight of evidence genetic algorithm backpropagation (WOE-GA-BP) model are 0.747, 0.748, and 0.785, respectively, indicating that the method can be effectively applied to evaluate drifting snow susceptibility

Evaluation of Drifting Snow Susceptibility Based on GIS and GA-BP Algorithms

Drifting snow, the flow of dispersed snow particles near ground level under the action of wind, is a major form of snow damage. When drifting snow occurs on railways, highways, and other transportation lines, it seriously affects their operational safety and results in drifting snow disasters. Drifting snow disasters frequently occur in the high latitudes of northwest China. At present, most scholars are committed to studying the prevention and control measures of drifting snow, but the prerequisite for prevention is to effectively evaluate the susceptibility of drifting snow along railways and highways to identify areas with a high risk of occurrence. Taking the Xinjiang Afukuzhun Railway as an example, this study uses a geographic information system (GIS) combined with on-site monitoring and surveys to establish a drifting snow susceptibility evaluation index system. The drifting snow susceptibility index (DSSI) is calculated through the weight of an evidence (WOE) model, and a genetic algorithm backpropagation (GA-BP) algorithm is used to obtain optimised evaluation index weights to improve the accuracy of model evaluation. The results show that the accuracies of the WOE model, WOE backpropagation (WOE-BP) model, and weight of evidence genetic algorithm backpropagation (WOE-GA-BP) model are 0.747, 0.748, and 0.785, respectively, indicating that the method can be effectively applied to evaluate drifting snow susceptibility

New generation artificial intelligence-driven intelligent manufacturing (NGAIIM)

Abstract In order to embrace new generation artificial intelligence to upgrade manufacturing industry, we propose the concept of new generation artificial intelligence-driven intelligent manufacturing (NGAIIM), which is a new manufacturing paradigm integrating human/machine/environment/information into product lifecycle activities. First, we introduce new generation artificial intelligence. Second, we present NGAIIM connotation, NGAIIM architecture and its technology system. Then, we examine a NGAIIM use case — CASICloud. Finally, we provide suggestions for directing the NGAIIM development

Unraveling Mechanisms and Impact of Microbial Recruitment on Oilseed Rape (Brassica napus L.) and the Rhizosphere Mediated by Plant Growth-Promoting Rhizobacteria

Plant growth-promoting rhizobacteria (PGPR) are noticeably applied to enhance plant nutrient acquisition and improve plant growth and health. However, limited information is available on the compositional dynamics of rhizobacteria communities with PGPR inoculation. In this study, we investigated the effects of three PGPR strains, Stenotrophomonas rhizophila, Rhodobacter sphaeroides, and Bacillus amyloliquefaciens on the ecophysiological properties of Oilseed rape (Brassica napus L.), rhizosphere, and bulk soil; moreover, we assessed rhizobacterial community composition using high-throughput Illumina sequencing of 16S rRNA genes. Inoculation with S. rhizophila, R. sphaeroides, and B. amyloliquefaciens, significantly increased the plant total N (TN) (p < 0.01) content. R. sphaeroides and B. amyloliquefaciens selectively enhanced the growth of Pseudomonadacea and Flavobacteriaceae, whereas S. rhizophila could recruit diazotrophic rhizobacteria, members of Cyanobacteria and Actinobacteria, whose abundance was positively correlated with inoculation, and improved the transformation of organic nitrogen into inorganic nitrogen through the promotion of ammonification. Initial colonization by PGPR in the rhizosphere affected the rhizobacterial community composition throughout the plant life cycle. Network analysis indicated that PGPR had species-dependent effects on niche competition in the rhizosphere. These results provide a better understanding of PGPR-plant-rhizobacteria interactions, which is necessary to develop the application of PGPR