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.

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.

An efficient self-optimized sampling method for rare events in nonequilibrium systems

Rare events such as nucleation processes are of ubiquitous importance in real systems.The most popular method for nonequilibrium systems,forward flux sampling(FFS),samples rare events by using interfaces to partition the whole transition process into sequence of steps along an order parameter connecting the initial and final states.FFS usually suffers from two main difficulties:low computational efficiency due to bad interface locations and even being not applicable when trapping into unknown intermediate metastable states.In the present work,we propose an approach to overcome these difficulties,by self-adaptively locating the interfaces on the fly in an optimized manner.Contrary to the conventional FFS which set the interfaces with equal distance of the order parameter,our approach determines the interfaces with equal transition probability which is shown to satisfy the optimization condition.This is done by firstly running long local trajectories starting from the current interface i to get the conditional probability distribution Pc(>i|i),and then determining i+1by equaling Pc(i+1|i)to a give value p0.With these optimized interfaces,FFS can be run in a much more efficient way.In addition,our approach can conveniently find the intermediate metastable states by monitoring some special long trajectories that neither end at the initial state nor reach the next interface,the number of which will increase sharply from zero if such metastable states are encountered.We apply our approach to a two-state model system and a two-dimensional lattice gas Ising model.Our approach is shown to be much more efficient than the conventional FFS method without losing accuracy,and it can also well reproduce the two-step nucleation scenario of the Ising model with easy identification of the intermediate metastable state.

The Last 15,000 Years: Climate-Controlled and “Rare-Event”-Triggered/Rise and Fall of Holocene Cultures in the Near/Middle East and in Central Europe—Evidence and Background

Pushed by the results of a preceding publication on the possibly Quaternary Jebel Waqf as Suwwan Meteorite Crater, Jordan [5], where an amazing coincidence of Rapid Climate Changes (RCCs) with Rise and Fall of Neolithic and Bronze Age Cultures became evident for the Near/Middle East, this paper deals with the same subject, however, relating to the complete Holocene period in the same area and, additionally, in Central Europe as well. By application of modern climatic data [6] comprising isotope analysis (δ18O, 14C, 10Be), acid and aerosol events, and greenhouse gases (CO2, CH4) Greenland ice cores as well as other astro-/geophysical and geological parameters, an overwhelming coincidence/relation/interdependence of both natural and cultural evidences becomes obvious throughout the last 15,000 years across the Northern Hemisphere. Apart from solar output and other astrophysical processes, most important climate- and Earth-related parameters are Mega-Volcanism (i.e.Santorini Greece: ~3640 yr cal. B. P.), Impact Events (i.e. during Mesolithic: ~9600 yr cal. B. P), rapid oceanic current change (DO-Events), and Plate Tectonics (possibly Atlantis-Event: ~11,500 yr cal. B.P. = Pleistocene/Holocene boundary). The most essential parameter is a significant temperature change related to more or less restricted latitude realms of the Northern Hemisphere. Thus, glacier advance/retreat controls the mobility of peoples (i.e. Nations' Migration, Teutonic Empires) and the access to ore deposits (Au, Ag, Cu, Sn, Zn, Pb, Fe) located in Alpine Mountain Ranges (i.e. End-Neolithic, Early Bronze Age). Myths like the Gilgamesh Epos and John Apocalypse convincingly reveal realistic contents relating to natural hazards like tsunamis, impact and flooding events. They unmisunderstandably make obvious that Myths may provide valuable contributions, especially to Geosciences. Some of the controlling parameters interrelate with others or present a kind of hierarchy: Mega-Volcanism/impact events à ejecta à wildfires, heat storms à cosmic winter, sint winter à stop of photosynthesis à mass extinction environmental pollution à greenhouse effects. Significant events (21 cases in total) occurred on i.e.

New Zircon U-Pb Age of the Super-Large Lijiagou Spodumene Deposit in Songpan Garze Fold Belt, Eastern Tibet： Implications for Early Jurassic Rare-Metal Polymetallic Event

Objective The Songpan-Garze Fold Belt(SGFB),located in the eastern part of the Tibet Plateau and west of the Sichuan Basin,is an important pegmatite province in China.Some famous pegmatite type deposits occur in the SGFB,including the Xuebaoding,Jiajika,Keeryin rare metal deposits and Danba muscovite deposit(Li Jiankang et al.,2015).The newly discovered super-large Lijiagou

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

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.

完备市场和不确定性风险下的养老金投资组合

为了更好地契合实际金融市场,同时为模糊厌恶的养老投资者提供最优的投资策略,将价格扩散不确定性、波动扩散不确定性及跳跃过程的不确定性整合到具有罕见事件的传统股票市场中,推导出半封闭形式的稳健动态衍生品投资策略。通过数值分析,得出以下结论:在波动率跳跃幅度较大的情况下,投资者倾向于在负价格跳跃时增加对价格和波动率的风险敞口,并减少对市场跳跃的敞口。股票和波动率风险的最优敞口主要受到投资者对股票价格和波动率风险因素的模糊厌恶的影响,而市场跳跃风险的最优敞口则受到投资者对波动率和罕见事件风险因素的模糊厌恶的显著影响。

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.

特殊临床表现多系统免疫相关不良事件1例

目的报道特殊临床表现多系统免疫相关不良事件个案,探讨本例免疫相关性不良反应发生的机制及危险因素,提高对该病的认识。方法分析本例患者的临床病例资料及诊治过程,并复习相关文献进行讨论。结果本例特殊临床表现多系统免疫相关不良事件患者,免疫相关性肺炎为G3,免疫相关性肝炎为G2,免疫相关性内分泌毒性G2。症状好转后仍死亡。结论特殊临床表现多系统免疫相关不良事件临床罕见,发病机制、危险因素、治疗方法仍存争议,虽发生率低,但预后差,值得进一步研究。

液氩探测器在稀有事例探测中的应用和发展

稀有事例探测是近几年热门的粒子物理前沿课题,如暗物质、无中微子双贝塔衰变、中微子-核子相干弹性散射等实验都在逐渐被规划和实施.进行稀有事例探测要求探测器有极佳的性能,同时对环境本底有很高的要求,因此探测器和相关材料的选择是稀有事例探测的一个重要课题.液氩因为成本低、闪烁性能好、体积限制较小等优势成为稀有事例探测器的一种重要介质.经过几十年的发展,单相液氩闪烁体探测器和两相氩时间投影室成为两种常见的液氩探测器类型,并开始被国内外各实验组应用于稀有事例探测实验中.本文首先对两种常见的液氩探测器的原理和特性进行介绍,然后详细介绍国内外相关稀有事例探测实验组对液氩探测器的研究和应用现状以及未来规划,最后讨论未来液氩探测器在稀有事例探测中的应用前景和优化方向.

自适应重要抽样技术在发输电系统可靠性评估中的应用

将自适应重要抽样技术应用在发输电系统可靠性评估中,并提出根据系统的概率特点构造抽样密度初值的方法,从而改善最优抽样技术在元件故障概率减小时出现的退化现象。应用该方法对IEEE-RTS系统及修改后的测试系统进行发输电系统可靠性评估,结果表明该方法在保持计算精度的情况下能加快可靠性评估的速度,特别是在元件故障概率较小时,能显著提高可靠性评估的计算效率。

提高小概率事件预报成功率的一条途径

在给出小概率事件定义与评估小概率事件预报成功率参数的基础上 ,根据参数变换后的表达式推断 :变小概率事件为条件概率下的大概率事件 ,是提高小概率事件预报成功率的一条途径。

稀有事件logistic回归在医学研究中的应用

目的探讨稀有事件logistic回归模型的参数估计及概率估计方法。方法在普通logistic回归结果的基础上进行先验校正、加权校正和MCN校正,并根据Vuong检验原理编程实现非嵌套模型间的对比分析。结果 logistic回归MCN加权校正对实例数据拟合较好。结论针对稀有事件数据的分析,在模型的参数估计和预测预报方面,稀有事件logistic回归结果优于普通logistic回归,是一种值得推广的应用统计模型。

面向安全攸关系统中小概率事件的统计模型检测

在开放运行环境中,安全攸关系统的不确定性行为有可能导致小概率事件的发生,而此类事件的可靠性指标往往很高,小概率事件一旦发生就会产生灾难性的后果,严重威胁到人们的生命、财产安全.因此,评估、预测小概率事件发生的概率,对于提高系统的可靠性具有重要意义.统计模型检测是一种基于模拟的模型验证技术,结合了系统的快速模拟及统计分析技术,能够有效提高模型检测的效率,适用于验证、评估安全攸关系统的可靠性,但其面临的挑战性问题之一是在可接受的样本数量下,使用统计模型检测技术难以预测、评估小概率事件发生的概率.因此,提出一种改进的统计模型检测框架,设计和开发基于机器学习的统计模型检测器,实现在相对较少的样本数量下预测和评估小概率事件发生的概率.结合轨道交通控制系统中避碰控制案例分析,进一步证明改进后的统计模型检测器能够有效预测和评估安全攸关系统中小概率事件发生的概率.

突发事件影响度评价研究

提出了基于突变理论和粗集约简理论的突发事件影响度评价方法。给出了突发事件影响度的评价指标,并采用粗集约简理论将影响因素约简为最小因素集。根据突变理论的归一公式给出了突发事件影响度的量化方法。该方法可将定量、定性指标纳入同一模型中,并可避免人为确定权重的主观性。实例表明,该方法是有效和可行的。

基于重要抽样技术的稀有事件仿真方法

传统的Monte Carlo方法仿真稀有事件需要较长的时间,而重要抽样技术可以有效的缩短仿真时间,提高仿真效率。因此,提出一种新的重要抽样实现方法,用来估计仿真模型中的稀有事件的概率。先选取经典的指数变换方法构造重要抽样分布类,再利用极小化重要抽样估计量方差的方法寻找最优重要抽样分布函数。仿真结果显示了该算法在估计稀有事件概率方面的有效性。

极端事件冲击下含糊厌恶投资者的最优投资组合选择问题

研究了在极端事件冲击下含糊厌恶投资者的最优投资组合问题,其中,投资者不仅对损失风险是厌恶的而且对模型不确定也是厌恶的.首先,利用随机微分方程理论,对含糊厌恶投资者的最优期望效用进行刻画.其次,利用动态规划原理,建立最优消费和投资策略所满足的HJB方程.再次,利用市场分解的方法解出HJB方程,获得投资者最优消费和投资策略的显示解.最后,通过数值模拟,分析了含糊厌恶、风险厌恶和跳对投资者最优投资组合选择问题的影响.