How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Liquefaction susceptibility and deformation characteristics of saturated coral sandy soils subjected to cyclic loadings-a critical review

Coral sandy soils widely exist in coral island reefs and seashores in tropical and subtropical regions.Due to the unique marine depositional environment of coral sandy soils,the engineering characteristics and responses of these soils subjected to monotonic and cyclic loadings have been a subject of intense interest among the geotechnical and earthquake engineering communities.This paper critically reviews the progress of experimental investigations on the undrained behavior of coral sandy soils under monotonic and cyclic loadings over the last three decades.The focus of coverage includes the contractive-dilative behavior,the pattern of excess pore-water pressure(EPWP)generation and the liquefaction mechanism and liquefaction resistance,the small-strain shear modulus and strain-dependent shear modulus and damping,the cyclic softening feature,and the anisotropic characteristics of undrained responses of saturated coral sandy soils.In particular,the advances made in the past decades are reviewed from the following aspects:(1)the characterization of factors that impact the mechanism and patterns of EPWP build-up;(2)the identification of liquefaction triggering in terms of the apparent viscosity and the average flow coefficient;(3)the establishment of the invariable form of strain-based,stress-based,or energy-based EPWP ratio formulas and the unique relationship between the new proxy of liquefaction resistance and the number of cycles required to reach liquefaction;(4)the establishment of the invariable form of the predictive formulas of small strain modulus and strain-dependent shear modulus;and(5)the investigation on the effects of stress-induced anisotropy on liquefaction susceptibility and dynamic deformation characteristics.Insights gained through the critical review of these advances in the past decades offer a perspective for future research to further resolve the fundamental issues concerning the liquefaction mechanism and responses of coral sandy sites subjected to cyclic loadings associated with seismic events in marine environments.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

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.

Dip2a regulates stress susceptibility in the basolateral amygdala

Dysregulation of neurotransmitter metabolism in the central nervous system contributes to mood disorders such as depression, anxiety, and post–traumatic stress disorder. Monoamines and amino acids are important types of neurotransmitters. Our previous results have shown that disco-interacting protein 2 homolog A(Dip2a) knockout mice exhibit brain development disorders and abnormal amino acid metabolism in serum. This suggests that DIP2A is involved in the metabolism of amino acid–associated neurotransmitters. Therefore, we performed targeted neurotransmitter metabolomics analysis and found that Dip2a deficiency caused abnormal metabolism of tryptophan and thyroxine in the basolateral amygdala and medial prefrontal cortex. In addition, acute restraint stress induced a decrease in 5-hydroxytryptamine in the basolateral amygdala. Additionally, Dip2a was abundantly expressed in excitatory neurons of the basolateral amygdala, and deletion of Dip2a in these neurons resulted in hopelessness-like behavior in the tail suspension test. Altogether, these findings demonstrate that DIP2A in the basolateral amygdala may be involved in the regulation of stress susceptibility. This provides critical evidence implicating a role of DIP2A in affective disorders.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

Distribution law and susceptibility of geohazards across a gradient belt of the Western Sichuan Plateau

Across a gradient belt of the Western Sichuan Plateau,geohazards have seriously limited economic and social development.According to incomplete statistics,15,673 geohazards have been recorded in the study area.In order to mitigate the threat of geohazards to human engineering activities in the region,an overall understanding of the distribution pattern of geohazards and susceptibility assessment are necessary.In this paper,a gradient belt of the Western Sichuan Plateau and its zoning criteria were defined.Subsequently,on the basis of relief amplitude,distance to faults,rainfall,and human activities,three indicators of endogenic process were introduced:Bouguer gravity anomaly gradient,vertical deformation gradient,and horizontal deformation gradient.Thereafter,the distribution patterns of geohazards were investigated through mathematical statistics and ArcGIS software.By randomly selecting 10,449 hazards,a geohazard susceptibility map was generated using the Information Value(IV)model.Finally,the IV model was validated against 5224 hazards using the Area Under Curve(AUC)method.The results show that 47.6%of the geohazards were distributed in the zone of steep slope.Geohazards showed strong responses to distance to faults,human activities,and annual rainfall.The distribution of geohazards in the gradient belt of the Western Sichuan Plateau is more sensitive to vertical internal dynamics factors(such as vertical deformation gradient and Bouguer gravity anomaly gradient)without any apparent sensitivity to horizontal internal dynamics factors.The areas of high and very-high risk account for up to 32.22%,mainly distributed in the Longmenshan and Anning River faults.According to the AUC plot,the success rate of the IV model for generating the susceptibility map is 76%.This susceptibility map and geohazard distribution pattern can provide a reference for geological disaster monitoring,preparation of post-disaster emergency measures,and town planning.

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation

Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.

Knock-out of BnHva22c reduces the susceptibility of Brassica napus to infection with the fungal pathogen Verticillium longisporum

Verticillium longisporum(Vl43)is a soilborne hemibiotrophic fungal pathogen causing stem striping on oilseed rape(OSR)and severe yield losses.Breeding for resistant varieties is the most promising approach to control this disease.Here,we report the identification of Hva22c as a novel susceptibility factor and its potential for improving OSR resistance.Hva22c is a member of the Hva22 gene family,originally described for barley(Hordeum vulgare).Several Hva22 members have been located at the endoplasmic reticulum.Hva22c is up-regulated in response to Vl43 in both Arabidopsis and OSR.We demonstrate that knock-out of Hva22c in OSR by CRISPR/Cas9 and its homolog in Arabidopsis by T-DNA insertion reduced plants’susceptibility to Vl43 infection and impaired the development of disease symptoms.To understand the underlying mechanism,we analysed transcriptomic data from infected and non-infected roots of hva22c knock-out and wild type plants.We identified a homozygous mutant with frame-shifts in all four BnHva22c loci displaying a vastly altered transcriptional landscape at 6 dpi.Significantly,a large set of genes was suppressed under mock conditions including genes related to the endomembrane systems.Among the up-regulated genes we found several defense-related and phytohormone-responsive genes when comparing mutant to the wild type.These results demonstrate that Hva22c is functionally required for a fully compatible plant-fungus interaction.Its loss of function reduces plant susceptibility,most likely due to endoplasmatic reticulum and Golgi dysfunction accompanied by additionally activated defense responses.These findings can help improve OSR resistance to V.longisporum infection.

Induced CTL-S15 gene expression by Bacillus thuringiensis declines susceptibility in Spodoptera exigua

It has been reported that C-type lectins(CTLs),which are pattern recognition receptors of the insect innate immunity response,may compete with Cry toxin for the receptor alkaline phosphatase to decrease its toxicity in insects.However,to date,which CTLs affect larval susceptibility to Bt in Spodoptera exigua is not clear.In this study,33 CTL genes were identified from S.exigua.Based on the number of carbohydrate-recognition domains(CRDs)and the domain architectures,they were classified into three groups:(1)nineteen CTL-S(single-CRD),(2)eight immulectin(dual-CRD)and(3)six CTL-X(CRD with other domains).RT-qPCR analysis revealed that expression levels of SeCTL-S15,IML-4 and CTL-X6 were upregulated after challenge with Bt and Cry1Ab.Tissue and developmental stage expression analysis showed that only SeCTL-S15 was mainly expressed in the midgut and larva,respectively.Knockdown of SeCTL-S15 significantly increased Bt susceptibility,as indicated by reduced survival and larval weight.These results suggest that CTL-S15 might play a vital role in the low susceptibility of larvae to Bt in S.exigua.Our results provide new insights into CTL function in insects.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.

Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin,China

Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".

Machine learning solution for regional landslide susceptibility based on fault zone division strategy

Landslide susceptibility assessment is an essential tool for disaster prevention and management. In areas with multiple fault zones, the impact of fault zone on slope stability cannot be disregarded. This study performed qualitative analysis of fault zones and proposed a zoning method to assess the landslide susceptibility in Chengkou County, Chongqing Municipality, China. The region within a distance of 1 km from the faults was designated as sub-zone A, while the remaining area was labeled as sub-zone B. To accomplish the assessment, a dataset comprising 388 historical landslides and 388 non-landslide points was used to train the random forest model. 10-fold cross-validation was utilized to select the training and testing datasets for the model. The results of the models were analyzed and discussed, with a focus on model performance and prediction uncertainty. By implementing the proposed division strategy based on fault zone, the accuracy, precision, recall, F-score, and AUC of both two sub-zones surpassed those of the whole region. In comparison to the results obtained for the whole region, sub-zone B exhibited an increase in AUC by 6.15%, while sub-zone A demonstrated a corresponding increase of 1.66%. Moreover, the results of 100 random realizations indicated that the division strategy has little effect on the prediction uncertainty. This study introduces a novel approach to enhance the prediction accuracy of the landslide susceptibility mapping model in areas with multiple fault zones.

Nucleotide excision repair gene polymorphisms and hepatoblastoma susceptibility in Eastern Chinese children:A five-center case-control study

Objective:Nucleotide excision repair(NER)plays a vital role in maintaining genome stability,and the effect of NER gene polymorphisms on hepatoblastoma susceptibility is still under investigation.This study aimed to evaluate the relationship between NER gene polymorphisms and the risk of hepatoblastoma in Eastern Chinese Han children.Methods:In this five-center case-control study,we enrolled 966 subjects from East China(193 hepatoblastoma patients and 773 healthy controls).The TaqMan method was used to genotype 19 single nucleotide polymorphisms(SNPs)in NER pathway genes,including ERCC1,XPA,XPC,XPD,XPF,and XPG.Then,multivariate logistic regression analysis was performed,and odds ratios(ORs)and 95%confidence intervals(95%CIs)were utilized to assess the strength of associations.Results:Three SNPs were related to hepatoblastoma risk.XPC rs2229090 and XPD rs3810366 significantly contributed to hepatoblastoma risk according to the dominant model(adjusted OR=1.49,95%CI=1.07−2.08,P=0.019;adjusted OR=1.66,95%CI=1.12−2.45,P=0.012,respectively).However,XPD rs238406 conferred a significantly decreased risk of hepatoblastoma under the dominant model(adjusted OR=0.68,95%CI=0.49−0.95;P=0.024).Stratified analysis demonstrated that these significant associations were more prominent in certain subgroups.Moreover,there was evidence of functional implications of these significant SNPs suggested by online expression quantitative trait loci(eQTLs)and splicing quantitative trait loci(sQTLs)analysis.Conclusions:In summary,NER pathway gene polymorphisms(XPC rs2229090,XPD rs3810366,and XPD rs238406)are significantly associated with hepatoblastoma risk,and further research is required to verify these findings.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

Fast measurement and prediction method for electromagnetic susceptibility of receiver

Aiming at evaluating and predicting rapidly and accurately a high sensitivity receiver’s adaptability in complex electromagnetic environments,a novel testing and prediction method based on dual-channel multi-frequency is proposed to improve the traditional two-tone test.Firstly,two signal generators are used to generate signals at the radio frequency(RF)by frequency scanning,and then a rapid measurement at the intermediate frequency(IF)output port is carried out to obtain a huge amount of sample data for the subsequent analysis.Secondly,the IF output response data are modeled and analyzed to construct the linear and nonlinear response constraint equations in the frequency domain and prediction models in the power domain,which provide the theoretical criteria for interpreting and predicting electromagnetic susceptibility(EMS)of the receiver.An experiment performed on a radar receiver confirms the reliability of the method proposed in this paper.It shows that the interference of each harmonic frequency and each order to the receiver can be identified and predicted with the sensitivity model.Based on this,fast and comprehensive evaluation and prediction of the receiver’s EMS in complex environment can be efficiently realized.

Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.