One of the most intriguing aspects of quantum mechanics is the impossibility of measuring at the same time observables corresponding to noncommuting operators, because of quantum uncertainty. This ...impossibility can be partially relaxed when considering joint or sequential weak value evaluation. Indeed, weak value measurements have been a real breakthrough in the quantum measurement framework that is of the utmost interest from both a fundamental and an applicative point of view. In this Letter, we show how we realized for the first time a sequential weak value evaluation of two incompatible observables using a genuine single-photon experiment. These (sometimes anomalous) sequential weak values revealed the single-operator weak values, as well as the local correlation between them.
Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies ...on predictions of out‐of‐sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split‐sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log‐Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.
Key Points
Stationary predictions of flood peak distributions are preferred, overall
Extrapolation of the nonstationary model parameter trend rarely improves the stationary prediction, even if an observed trend continues
Using the most recent nonstationary parameters to predict with an updated stationary model is preferred for physically changing watersheds
Size‐exclusion chromatography coupled with SAXS (small‐angle X‐ray scattering), often performed using a flow‐through capillary, should allow direct collection of monodisperse sample data. However, ...capillary fouling issues and non‐baseline‐resolved peaks can hamper its efficacy. The UltraScan solution modeler (US‐SOMO) HPLC‐SAXS (high‐performance liquid chromatography coupled with SAXS) module provides a comprehensive framework to analyze such data, starting with a simple linear baseline correction and symmetrical Gaussian decomposition tools Brookes, Pérez, Cardinali, Profumo, Vachette & Rocco (2013). J. Appl. Cryst.46, 1823–1833. In addition to several new features, substantial improvements to both routines have now been implemented, comprising the evaluation of outcomes by advanced statistical tools. The novel integral baseline‐correction procedure is based on the more sound assumption that the effect of capillary fouling on scattering increases monotonically with the intensity scattered by the material within the X‐ray beam. Overlapping peaks, often skewed because of sample interaction with the column matrix, can now be accurately decomposed using non‐symmetrical modified Gaussian functions. As an example, the case of a polydisperse solution of aldolase is analyzed: from heavily convoluted peaks, individual SAXS profiles of tetramers, octamers and dodecamers are extracted and reliably modeled.
The US‐SOMO HPLC‐SAXS (high‐performance liquid chromatography coupled with small‐angle X‐ray scattering) module is an advanced tool for the comprehensive analysis of SEC‐SAXS (size‐exclusion chromatography coupled with SAXS) data. It includes baseline and band‐broadening correction routines, and Gaussian decomposition of overlapping skewed peaks into pure components.
•We introduce a process-informed extreme value analysis framework, namely ProNEVA.•ProNEVA allows incorporating physical drivers into statistical analysis of extremes.•ProNEVA allows both stationary ...and nonstationary modeling of climatic extremes.•Process-informed nonstationary models better capture the changing extreme events.•ProNEVA was applied to urbanization-flooding, CO2-temperature, sea level rise data.
Evolving climate conditions and anthropogenic factors, such as CO2 emissions, urbanization and population growth, can cause changes in weather and climate extremes. Most current risk assessment models rely on the assumption of stationarity (i.e., no temporal change in statistics of extremes). Most nonstationary modeling studies focus primarily on changes in extremes over time. Here, we present Process-informed Nonstationary Extreme Value Analysis (ProNEVA) as a generalized tool for incorporating different types of physical drivers (i.e., underlying processes), stationary and nonstationary concepts, and extreme value analysis methods (i.e., annual maxima, peak-over-threshold). ProNEVA builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This offers more robust uncertainty estimates of return periods of climatic extremes under both stationary and nonstationary assumptions. ProNEVA is designed as a generalized tool allowing using different types of data and nonstationarity concepts physically-based or purely statistical) into account. In this paper, we show a wide range of applications describing changes in: annual maxima river discharge in response to urbanization, annual maxima sea levels over time, annual maxima temperatures in response to CO2 emissions in the atmosphere, and precipitation with a peak-over-threshold approach. ProNEVA is freely available to the public and includes a user-friendly Graphical User Interface (GUI) to enhance its implementation.
Coastal regions are dynamic areas that often lie at the junction of different natural hazards. Extreme events such as storm surges and high precipitation are significant sources of concern for flood ...management. As climatic changes and sea-level rise put further pressure on these vulnerable systems, there is a need for a better understanding of the implications of compounding hazards. Recent computational advances in hydraulic modelling offer new opportunities to support decision-making and adaptation. Our research makes use of recently released features in the HEC-RAS version 5.0 software to develop an integrated 1D–2D hydrodynamic model. Using extreme value analysis with the Peaks-Over-Threshold method to define extreme scenarios, the model was applied to the eastern coast of the UK. The sensitivity of the protected wetland known as the Broads to a combination of fluvial, tidal and coastal sources of flooding was assessed, accounting for different rates of twenty-first century sea-level rise up to the year 2100. The 1D–2D approach led to a more detailed representation of inundation in coastal urban areas, while allowing for interactions with more fluvially dominated inland areas to be captured. While flooding was primarily driven by increased sea levels, combined events exacerbated flooded area by 5–40% and average depth by 10–32%, affecting different locations depending on the scenario. The results emphasise the importance of catchment-scale strategies that account for potentially interacting sources of flooding.
Albeit SH2 domains are abundant protein–protein interaction modules with fundamental roles in the regulation of several physiological and molecular pathways in the cell, the available information ...about the determinants of their thermodynamic stability and folding properties are still very limited. In this work, we provide a quantitative characterization of the folding pathway of the C‐terminal SH2 domain of SHP2, conducted through a combination of site‐directed mutagenesis and kinetic (un)folding experiments (Φ‐value analysis). The energetic profile of the folding reaction of the C‐SH2 domain is described by a three‐state mechanism characterized by the presence of two transition states and a high‐energy intermediate. The production of 29 site‐directed variants allowed us to calculate the degree of native‐like interactions occurring in the early and late events of the folding reaction. Data analysis highlights the presence of a hydrophobic folding nucleus surrounded by a lower degree of structure in the early events of folding, further consolidated as the reaction proceeds towards the native state. Interestingly, residues physically located in the functional region of the domain reported unusual Φ‐values, a hallmark of the presence of transient misfolding. We compared our results with previous ones obtained for the N‐terminal SH2 domain of SHP2. Notably, a conserved complex folding mechanism implying the presence of a folding intermediate arise from comparison, and the relative stability of such intermediate appears to be highly sequence dependent. Data are discussed under the light of previous works on SH2 domains.
The concept of a value chain has assumed a dominant position in the strategic analysis of industries. However, the value chain is underpinned by a particular value creating logic and its application ...results in particular strategic postures. Adopting a network perspective provides an alternative perspective that is more suited to New Economy organisations, particularly for those where both the product and supply and demand chain is digitized. This paper introduces the value network concept and illuminates on its value creating logic. It introduces Network Value Analysis (NVA) as a way to analyse competitive ecosystems. To illustrate its application, the provision of mobile services and content is explored to identify potential strategic implications for mobile operators. PUBLICATION ABSTRACT
Record rainfall amounts were recorded during Hurricane Harvey in the Houston, Texas, area, leading to widespread flooding. We analyze observed precipitation from the Global Historical Climatology ...Network with a covariate‐based extreme value statistical analysis, accounting for both the external influence of global warming and the internal influence of El Niño–Southern Oscillation. We find that human‐induced climate change likely increased the chances of the observed precipitation accumulations during Hurricane Harvey in the most affected areas of Houston by a factor of at least 3.5. Further, precipitation accumulations in these areas were likely increased by at least 18.8% (best estimate of 37.7%), which is larger than the 6–7% associated with an attributable warming of 1°C in the Gulf of Mexico and Clausius‐Clapeyron scaling. In a Granger causality sense, these statements provide lower bounds on the impact of climate change and motivate further attribution studies using dynamical climate models.
Key Points
Human‐induced climate change likely increased Hurricane Harvey's total rainfall by at least 19%
Climate change likely increased the chances of the observed rainfall by a factor of at least 3.5
Observations suggest that changes exceeded Clausius‐Clapeyron scaling, motivating dynamical studies
•Orographic impact in extreme precipitation from 5 min to 24 h is examined.•Differential impacts emerge for short (5–20′), medium (30–120′) and long (2–24 h) durations.•These three precipitation ...regimes drive two modes of orographic relationships.•Orographic enhancement emerges at long durations, reverse orographic effect at short.
Extreme precipitation of multiple durations is responsible for major natural hazards in mountainous regions, such as flash floods and debris flows. Understanding the orographic impact on the statistics of precipitation extremes is therefore crucial for improving hydrological design and risk management strategies. Here, we use a novel statistical approach for the analysis of extremes based on ordinary events to improve our understanding of the orographic impact on extreme precipitation of durations ranging between 5 min and 24 h. We focus on Trentino, a rough orographic region in the eastern Italia Alps, and use data from 78 quality-controlled rain gauges with 5-minute resolution. We show that our framework well reproduces the statistical properties of the observed annual maxima (Nash-Sutcliffe 0.82–0.95, Bias from -4% to 7%) as well as their relation with orography. We then exploit the reduced uncertainty of this approach to quantify the orographic impact on precipitation right-tail statistics and on extreme return levels using a regression analysis. We identify two main modes of orographic relationship: a reverse orographic effect for hourly and sub-hourly durations (10–20% decrease per 1000 m elevation) and an orographic enhancement for durations of ∼8 h or longer (7.5–10% increase per 1000 m elevation). We observe that these two modes result from three main precipitation regimes, which show different proportion between extreme and very-extreme events and which emerge at very short durations (∼20 min or shorter), mid durations (∼30 min-1 hour) and long durations (∼2 h or longer). These findings are of interest for risk management applications and climate change impact studies.
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