Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules

Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)modelling.However,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored.Adopting Xunwu County,China,as an example,the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions,after which different landslide inventory sample missing conditions are simulated by random sampling.It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%,20%,30%,40%and 50%,as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation.Then,five machine learning models,namely,Random Forest(RF),and Support Vector Machine(SVM),are used to perform LSP.Finally,the LSP results are evaluated to analyze the LSP uncertainties under various conditions.In addition,this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions.Results show that(1)randomly missing landslide inventory samples at certain proportions(10%–50%)may affect the LSP results for local areas.(2)Aggregation of missing landslide inventory samples may cause significant biases in LSP,particularly in areas where samples are missing.(3)When 50%of landslide samples are missing(either randomly or aggregated),the changes in the decision basis of the RF model are mainly manifested in two aspects:first,the importance ranking of environmental factors slightly differs;second,in regard to LSP modelling in the same test grid unit,the weights of individual model factors may drastically vary.

Identifying and managing risks of AI-driven operations:A case study of automatic speech recognition for improving air traffic safety

In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience.The case study is performed to evaluate the AI-driven techniques and applications using objective metrics,in which several risks and technical facts are obtained to direct future research.Considering the safety–critical specificities of the air traffic control system,a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview.In this procedure,the potential risks obtained from the case study are confirmed,and the impacts on human working are considered.Both the case study and the evaluation of user experience provide compatible results and conclusions:(A)the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance;(B)the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers.Finally,a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.

Osteopontin Promotes Macrophage M1 Polarization by Activation of the JAK1/STAT1/HMGB1 Signaling Pathway in Nonalcoholic Fatty Liver Disease

Background and Aims:Osteopontin(OPN)is reported to be associated with the pathogenesis of nonalcoholic fatty liver disease(NAFLD).However,the function of OPN in NAFLD is still inconclusive.Therefore,our aim in this study was to evaluate the role of OPN in NAFLD and clarify the involved mechanisms.Methods:We analyzed the expression change of OPN in NAFLD by bioinformatic analysis,qRT-PCR,western blotting and immunofluorescence staining.To clarify the role of OPN in NAFLD,the effect of OPN from HepG2 cells on macrophage polarization and the involved mechanisms were examined by FACS and western blotting.Results:OPN was significantly upregulated in NAFLD patients compared with normal volunteers by microarray data,and the high expression of OPN was related with disease stage and progression.OPN level was also significantly increased in liver tissue samples of NAFLD from human and mouse,and in HepG2 cells treated with oleic acid(OA).Furthermore,the supernatants of OPN-treated HepG2 cells promoted the macrophage M1 polarization.Mechanistically,OPN activated the janus kinase 1(JAK1)/signal transducers and activators of transcription 1(STAT1)signaling pathway in HepG2 cells,and consequently HepG2 cells secreted more high-mobility group box 1(HMGB1),thereby promoting macrophage M1 polarization.Conclusions:OPN promoted macrophage M1 polarization by increasing JAK1/STAT1-induced HMGB1 secretion in hepatocytes.