Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy)

This paper discusses some methodological aspects for the production of susceptibility maps of slope instability developed within the CARG Project (Geological Cartography of Italy at 1:50,000 scale). It describes an example of a susceptibility map in the presence of low susceptibility, using database having zero or negligible cost, with the aim to test some methodologies that can be easily reproducible to get a first estimate of the landslide susceptibility on a wide area. Two statistical approaches have been applied: the non-parametric conditional analysis and the logistic analysis for rare events. The predictive ability obtained from the two methodologies, was evaluated by the success-prediction curves for the conditional analysis, and by the Receiver Operating Characteristic curve (ROC), for the logistic model. The landslide susceptibility maps have been classified into four classes using both the Natural Breaks algorithm and the method proposed by Chung and Fabbri (2003). The paper considers the influence of these two classification methods on the quality of final results.

Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

2004 Top 10 Chinese Rare Earth Events

1. Management to the Investment in Rare Earth IndustryConfirmedIn July 2004, "Decision on the Reform in Investment System" was formally publicized by the State Council of the People's Republic of China. The fifth item in the Decision stipulates that ore exploitation, smelting & separation and rare earth deep-processed projects with total investment over RMB￥100 million should be approved by the investment governing department of the State Council, and that

Quality Decisions Based on Time between Events Data Analysis

decisions(Quality Decisions)depend on scientific analysis of data.Data are collected,generally,in two ways:1)one sample of suitable size,2)subsequent samples,at regular intervals of time.Often the data are considered normally distributed.This is wrong because the data must be analysed according to their distribution:Decisions are different.In several cases the data are exponentially distributed:we see how to scientifically deal with Control Charts(CC)to decide;this is opposite to what gives the T Charts that are claimed to be a good method for dealing with“rare events”:The Minitab Software(19&20&21)for“T Charts”is considered.The author will compare some methods,found in the literature with the author’s Theory RIT(Reliability Integral Theory):We will see various cases found in the literature.Classical Shewhart Control Charts and the TBE(Time Between Events)Control Charts have been considered:it appears that with RIT the future decisions will be both sounder and cheaper,for data is exponentially distributed.The novelty of the paper is in the scientific way of dealing with the Control Charts and their Control Limits,both with normally distributed data and with exponentially distributed data.In this way,a lot of wrong published papers on“Time Between Events”are to be discarded,even if their authors claim“We used Standard Statistical methods,typical in the vast literature of similar papers”.The author had to self-cite because it seems the only one that has been fighting for years for“Papers Quality”;he humbly asked the readers to inform him if some people did the same.

Computing large deviation prefactors of stochastic dynamical systems based on machine learning

We present a large deviation theory that characterizes the exponential estimate for rare events in stochastic dynamical systems in the limit of weak noise.We aim to consider a next-to-leading-order approximation for more accurate calculation of the mean exit time by computing large deviation prefactors with the aid of machine learning.More specifically,we design a neural network framework to compute quasipotential,most probable paths and prefactors based on the orthogonal decomposition of a vector field.We corroborate the higher effectiveness and accuracy of our algorithm with two toy models.Numerical experiments demonstrate its powerful functionality in exploring the internal mechanism of rare events triggered by weak random fluctuations.

Statistical learning prediction of fatigue crack growth via path slicing and re-weighting

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design.However,fatigue often involves entangled complexities of material microstructures and service conditions,making diagnosis and prognosis of fatigue damage challenging.We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties.Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations.Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth.Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events.The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns.The end-to-end approach is validated by representative examples with fatigue cracks in plates,which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.

Record Flooding Risk and Power Outage Restoration

We need to predict the probability of unprecedented flooding of lands and coastlines due to unexpected storms,overflowing rivers,hurricanes,tidal surges and dam failures.This paper addresses new record floods that exceed all prior“historic”levels and are invariably due to extreme or severe weather and/or unexpected precipitation,defeating barriers and causing extensive power system outages.Given their inherently low occurrence,the probabilities of new(rare)record floods are treated as random outcomes and independent events using classical statistical mechanics and related hypergeometric sampling.This analysis straightforwardly replaces tuning or fitting to“normal”precipitation,regular tides and prior flood data and the traditional use of multi-parameter extreme value distributions(EVDs)used for weather-induced flood forecasting and estimating“return periods”.The approach is not reliant on geographic computer models,meteorological forecasting,published“flood zone”charts,or hydrological techniques and images.We illustrate the universal applicability of this Bayesian-style approach of solely addressing new records for a wide range of specific flooding case studies for rivers,major hurricanes,quasi-periodic coastal tides,and dam failures.The quantitative link is shown between extreme event extent and power outage duration,and the results impact disaster resilience,infrastructure vulnerability and emergency preparedness measures.

Recovery of photosynthesis,sucrose metabolism,and proteolytic enzymes in Kandelia obovata from rare cold events in the northernmost mangrove,China

Introduction:Understanding how mangroves respond to rare cold events has implications for both restoration and conservation under climate change scenarios.This study investigated the responses of photosynthesis and activities of key enzymes involving carbon and nitrogen metabolism at different ages of Kandelia obovata to a rare cold event in the winter of 2010.Methods:This study took place on Ximen Island,Zhejiang Province,China.We measured the physiological recovery of 2-3-,5-6-,9-10-and 54-55-year-old K.obovata trees after freezing injury in February and March in 2011 and 2012,respectively.Results:Chilling injury index and electrolyte leakage of K.obovata increased with increasing tree age in the winter of 2010,and electrolyte leakage in K.obovata at different ages in the winter of 2010 was far higher than that in the winter of 2011.The rare cold events significantly changed the recoveries of the leaf net photosynthetic rate(Pn)and stomatal conductance(Gs);ratios of chlorophyll a/chlorophyll b(Chl a/Chl b);contents of total soluble sugar(TSS),sucrose,free amino acid(FAA),and soluble protein;and activities of sucrose phosphate synthase(SPS),endopeptidase,and carboxypeptidase in K.obovata at different ages.These effects were mainly due to changes in the physiological mechanism in the 2-year-old trees.A clear decrease in Pn of the 2-year-old trees was observed in February 2011,as exemplified by reductions in ratios of Chl a/Chl b and chlorophyll/carotenoid(Chl/Car),as well as inhibition of the levels of TSS and FAA(osmotic substances).During recovery in 2011 and 2012,the activities of SPS and sucrose synthase(SS)were responsible for sucrose synthesis after the rare cold events in 2011,but only SPS activity was one of the main factors contributing to the metabolism of stachyose to sucrose without cold damage in 2012.Carboxypeptidase played a more important role than endopeptidase during protein hydrolysis after the rare cold events.Conclusions:The results suggest that the recovery of photosynthetic capacity in K.obovata was changed after a rare cold event,which is associated with pigment components and activities of SS,SPS,and carboxypeptidase,especially the seedlings.

Optimization-Based String Method for Finding Minimum Energy Path

We present an efficient algorithm for calculating the minimum energy path(MEP)and energy barriers between local minima on a multidimensional potential energy surface(PES).Such paths play a central role in the understanding of transition pathways between metastable states.Our method relies on the original formulation of the string method[Phys.Rev.B,66,052301(2002)],i.e.to evolve a smooth curve along a direction normal to the curve.The algorithm works by performing minimization steps on hyperplanes normal to the curve.Therefore the problem of finding MEP on the PES is remodeled as a set of constrained minimization problems.This provides the flexibility of using minimization algorithms faster than the steepest descent method used in the simplified string method[J.Chem.Phys.,126(16),164103(2007)].At the same time,it provides a more direct analog of the finite temperature string method.The applicability of the algorithm is demonstrated using various examples.

A Multilevel Splitting Algorithm for Quick Sampling

To reduce intermediate levels of splitting process and enhance sampling accuracy, a multilevel splitting algorithm for quick sampling is proposed in this paper. Firstly, the selected area of the elite set is expanded to maintain the diversity of the samples. Secondly, the combined use of an adaptive difference evolution algorithm and a local searching algorithm is proposed for the splitting procedure. Finally, a suite of benchmark functions are used for performance testing. The results indicate that the convergence rate and stability of this algorithm are superior to those of the classical importance splitting algorithm and an adaptive multilevel splitting algorithm.

A multi-level splitting algorithm based on differential evolution

Multi-level splitting algorithm is a famous rare event simulation(RES)method which reaches rare set through splitting samples during simulation.Since choosing sample paths is a key factor of the method,this paper embeds differential evolution in multi-level splitting mechanism to improve the sampling strategy and precision,so as to improve the algorithm efficiency.Examples of rare event probability estimation demonstrate that the new proposed algorithm performs well in convergence rate and precision for a set of benchmark cases.

PERFORMANCE EVALUATION OF DISCRETE EVENT SYSTEMS INVOLVING HENSTOCK-KURZWEIL INTEGRAL

This paper presents a study on the performance of flexible manufacturing systems （FMSs）, by using discrete event system （DES） models, considering resource losses modelled by a parameter entitled coverage factor. We conclude that the resources cell loss distribution between the tasks of a FSM is a real function that cannot be integrated, in order to calculate its primitive, in the classical sense of Riemann or Lebesgue, but only in the sense of Henstock-Kurzweil integral. Our result allows one to study more general processes where highly oscillatory functions occur. The method used to deduce the functi6n describing the resources cell loss distribution is compared with a classical method related in the literature, respectively rational interpolants. An example has been constructed to emphasize what we believe to be, new approaches.

An Improved Adaptive Minimum Action Method for the Calculation of Transition Path in Non-Gradient Systems

The minimum action method(MAM)is to calculate the most probable transition path in randomly perturbed stochastic dynamics,based on the idea of action minimization in the path space.The accuracy of the numerical path between different metastable states usually suffers from the“clustering problem”near fixed points.The adaptive minimum action method(aMAM)solves this problem by relocating image points equally along arc-length with the help of moving mesh strategy.However,when the time interval is large,the images on the path may still be locally trapped around the transition state in a tangle,due to the singularity of the relationship between arc-length and time at the transition state.Additionally,in most non-gradient dynamics,the tangent direction of the path is not continuous at the transition state so that a geometric corner forms,which brings extra challenges for the aMAM.In this note,we improve the aMAM by proposing a better monitor function that does not contain the numerical approximation of derivatives,and taking use of a generalized scheme of the Euler-Lagrange equation to solve the minimization problem,so that both the path-tangling problem and the non-smoothness in parametrizing the curve do not exist.To further improve the accuracy,we apply the Weighted Essentially nonoscillatory(WENO)method for the interpolation to achieve better performance.Numerical examples are presented to demonstrate the advantages of our new method.