Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors:A modeling study in Cambodia

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

Unreliability Tracing of Power Systems for Identifying the Most Critical Risk Factors Considering Mixed Uncertainties in Wind Power Output

For conventional power systems,the forced outage of components is the major cause of load shedding.Unreliability tracing is utilized to allocate the total system load-shedding risk among individual components in accordance with their different contributions.Therefore,critical components are identified and pertinent measures can be taken to improve system reliability.The integration of wind power introduces additional risk factors into power systems,causing previous unreliability tracing methods to become inapplicable.In this paper,a novel unreliability tracing method is proposed that considers both aleatory and epistemic uncertainties in wind power output and their impacts on power system load-shedding risk.First,modelling methods for wind power output considering aleatory and epistemic uncertainties and component outages are proposed.Then,a variance-based index is proposed to measure the contributions of individual risk factors to the system load-shedding risk.Finally,a novel unreliability tracing framework is developed to identify the critical factors that affect power system reliability.Case studies verify the ability of the proposed method to accurately allocate load-shedding risk to individual risk factors,thus providing decision support for reliability enhancement.

Interaction between γ'precipitate distribution and microstructure homogeneity during hot deformation in a Ni-based superalloy

The inhomogeneous microstructure of the Ni-based superalloys used for turbine disks was an intolerable defect for the mechanical performance.The effects of the distribution ofγ'precipitate,forging temperature,and strain level on the microstructure evolution of GH4730 alloy were investigated by EBSD during hot deformation.The results showed that the heterogeneous factor peaked at the transition temperature from the single-phase to the double-phase region.The coupling effect of heat and stress led to the heterogeneous precipitation and distribution ofγ'phase during the transition region,which was the main reason for the formation of inhomogeneous microstructures.The coherentγ'phases of approximately 0.3μm were diffusely distributed inside the large grains,increasing the grain strength,making recrystallization refinement difficult,and thus forming large unrecrystallized grains.The incoherentγ'phases with a size of approximately 1.1μm located at the grain boundaries and pinned the grain boundaries,and thus the accumulated strain at the grain boundaries caused the occurrence of discontinuous dynamic recrystallization and promoted continuous refinement of the grains.The microstructure evolution of new Ni-based superalloys during hot forging was focused,and the formation mechanism of inhomogeneous microstructure and control measures was explained.A theoretical basis for improving the microstructure homogeneity of the new cast and wrought superalloys was provided.

Potential Characteristics of Supervisory Board,Company Asset Scale and Irregularity of Listed Companies:Empirical Analysis Based on Heckman Two-Stage Model

Based on heterogeneity extraction,this paper analyzes four potential characteristics of the supervisory board,they are Individual Heterogeneity of the Supervisory Member(Internal Heterogeneity),Organization Size of the Supervisory Board(Organization Size),Structural Characteristics of the Supervisory Board(Structural Characteristics)and Identity Background of the Supervisory Board(Identity Background);and verifies the impact and action path of the potential characteristics on irregularities.Then,systematically evaluates the micro enterprise organization construction and corporate governance behavior by using the methods of factor analysis and Heckman two-stage model.Empirical research shows that the scale of corporate assets does have an important impact on corporate irregularities and the governance of the board of supervisors.Under the regulation of the company scale,the three potential characteristics:Organization Size,Identity Background and Structural Characteristics have played a significant inhibitory role on irregularities,and the Internal Heterogeneity has no significant effect.When using violation behavior as an alternative variable of supervision performance,the sample selection deviation will be caused by the lack of information disclosure.This paper suggests that we should pay attention to the team of the board of supervisors scientifically and reasonably,weaken the appropriate personalized differences within the board of supervisors,and comprehensively consider the interaction between the company scale,asset quality and the performance of the board of supervisors when formulating the corporate internal management system.

A Closed Form Characterization of the Stationary Outcome in Multilateral Bargaining

In this paper we consider infinite horizon multilateral bargaining with al- ternate offers. We prove that there exists only one stationary subgame perfect equilib- rium outcome and it corresponds to the unique invariant measure of a column stochas- tic matrix. We characterize this stationary subgame perfect equilibrium outcome in a closed form, and also extend the approach to the multilateral bargaining with random moves.