Maximum Research Articles

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods

Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

Comfort weight analysis for optimising ergonomic cabin design of a drilling rig machine using three-dimensional human simulation

Ergonomics in product design enhance user comfort, correlating with increased efficiency and usability between the user and the product. Anthropometry is an important part of creating ergonomic design that correlates with human body size. By utilising anthropometry data, this research aims to analyse the cabin's comfort in drilling rig machine by creating 3D simulations for human-machine interaction. The comfort values will be assessed using Jack software. An experimental orthogonal design is applied to generate 81 optimal combination samples. The sample with the optimal comfort value can also be obtained. Following that, the extreme learning machine and mean impact value (ELM-MIV) algorithm is employed to evaluate the comfort weight analysis of each influencing factor. The accuracy value of the prediction model assessed using mean squared error (MSE) is 0.004, and the squared correlation coefficient (R2 ) value is 0.996. It can be inferred that the prediction error is apparently minimal, indicating the reliability of ELM implementation in data training. It will optimise the redesign of a drilling rig machine cabin to solve work-related health issues and improve operator comfort and efficiency.

Statistical Analysis of Frechet Distribution in Step-Up and Step-Down Testse

This study explores the application of the Frechet distribution in step-up and step-down tests, investigating its effectiveness and applicability in extreme value analysis. Through systematic collection and statistical analysis of experimental data, this paper first clarifies the basic concept of the Frechet distribution and the physical significance of its parameters. It then analyzes the design principles of step-up and step-down tests and their data processing methods, focusing on evaluating the performance of the Frechet distribution in describing maximum and minimum value characteristics. The results show that the Frechet distribution can effectively fit experimental data, providing a significant theoretical basis for the evaluation of material and structural performance in engineering. However, the paper also points out the limitations of applying the Frechet distribution and suggests future research directions.

Eguchi–Hanson metrics arising from Kähler–Einstein edge metrics

Calabi–Hirzebruch manifolds are higher-dimensional generalizations of both the football and Hirzebruch surfaces. We construct a family of Kähler-Einstein edge metrics singular along two disjoint divisors on the Calabi–Hirzebruch manifolds and study their Gromov–Hausdorff limits when either cone angle tends to its extreme value. As a very special case, we show that the celebrated Eguchi–Hanson metric arises in this way naturally as a Gromov–Hausdorff limit. We also completely describe all other (possibly rescaled) Gromov–Hausdorff limits which exhibit a wide range of behaviors, resolving in this setting a conjecture of Cheltsov–Rubinstein. This gives a new interpretation of both the Eguchi–Hanson space and Calabi’s Ricci flat spaces as limits of compact singular Einstein spaces.

Evaluation of flood metrics across the Mississippi-Atchafalaya River Basin and their relation to flood damages.

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks.

Assessing air-gap responses of semi-submersible platforms in extreme sea states based on in-situ measurement

The air-gap response of semi-submersible platforms is critical for ensuring operational sustainability. This study analysed platform-based in-situ measurements from the HYSY 981 semi-submersible platform during extreme sea conditions caused by super typhoon Doksuri. Our primary aim was to investigate how motions induced by the dynamic positioning system, particularly rotations, affected the platform's air-gap response. Results demonstrated that under extreme sea states, thruster-induced rotation significantly influenced air-gap responses. To estimate extreme air-gap response values, we employed two statistical models: the Weibull tail fitting (WTF) method and the Naess–Gaidai tail fitting (NGTF) method. Our analysis revealed that the NGTF method provided more robust and stable estimates of the most probable maximum (MPM) values compared to the three-parameter WTF method, especially when applied to in-situ measurement data. This research contributes to a better understanding of semi-submersible platform behavior during extreme weather events, which is essential for enhancing operational sustainability and structural integrity in offshore operations.

Estimates of (convective core) masses, radii, and relative ages for ~14,000 Gaia-discovered gravity-mode pulsators monitored by TESS

Gravito-inertial asteroseismology saw its birth from the 4-year-long light curves of rotating main-sequence stars assembled by the Kepler\/ space telescope. High-precision measurements of internal rotation and mixing are available for about 600 stars of intermediate mass so far that are used to challenge the state-of-the-art stellar structure and evolution models. Our aim is to prepare for future large ensemble modelling of gravity-mode pulsators by relying on a new sample of such stars recently discovered from the third Data Release of the Gaia\/ space mission and confirmed by space photometry from the TESS mission. This sample of potential asteroseismic targets is about 23 times larger than the Kepler\/ sample. We use the effective temperature and luminosity inferred from Gaia\/ to deduce evolutionary masses, convective core masses, radii, and ages for sim 14,000 gravity-mode pulsators classified as such from their nominal TESS light curves. We do so by constructing two dedicated grids of evolutionary models for rotating stars with input physics from the asteroseismic calibrations of Kepler\/ pulsators. These two grids consider the distribution of initial rotation velocities at the zero-age main sequence deduced from gravito-inertial asteroseismology, for two extreme values found for the metallicity of stars deduced from spectroscopy M/H = 0.0$ and $-0.5$). We find the new gravity-mode pulsators to cover an extended observational instability region covering masses from about 1.3 to about 9 We provide their mass-luminosity and mass-radius relations, as well as convective core masses. Our results suggest that oscillations excited by the opacity mechanism occur uninterruptedly for the mass range above about 2 where stars have a radiative envelope aside from thin convection zones in their excitation layers. Our evolutionary parameters for the sample of Gaia\/ -discovered gravity-mode pulsators with confirmed modes by TESS offer a fruitful starting point for future TESS ensemble asteroseismology once a sufficient number of modes is identified in terms of the geometrical wave numbers and overtone for each of the pulsators.

Flood risk and overtopping assessment for an exposed coastal road

The coastal village of Vík is located on the South Coast of Iceland. The area is subjected to the full force of extratropical cyclones of the North Atlantic. The village beach is not a stable phenomenon, after a nearby volcanic eruption in 1918 it accreted by 500 m over 50 years but has eroded at similar rate since 1970, which poses increased flood risk in the area. There are plans to move the current ring road, that lies through the village, with one possibility of it running along the coast. A study was conducted using multivariate extreme value modelling to determine the desired coastal defences needed for the new road. The method in question uses a multivariate joint probability model to simulate variables that cause coastal flooding events. It generates a large sample of extreme ocean events based on a 52-year time series of hindcast data. The large sample of extreme events were then used to determine return periods for overtopping events and to determine a desired crest height for coastal defence structures. The study puts forth suggestions of improvements on current coastal defence structures and the design specification for defence structures along a planned road along the coast.

Research on extreme value estimations and dynamic coefficients of wheel load for high-speed railway based on generalized Pareto distribution theory

The design value of wheel load is one of the key design parameters for high-speed railway ballastless track structure, which essentially belongs to the extreme estimation of wheel load. Based on the generalized Pareto distribution theory, this article carries on the research on the extreme value estimations of wheel load excited by whole wavelength range irregularity, which provides a basis for the design of ballastless track structure. When using automatic threshold selection method, sample declustering can improve the independence between data. The shape parameter screening method is proposed for the extreme value estimations of wheel load, and the corresponding sample size and shape parameter range for each cluster are determined. A reasonable sample size is determined, and the mean of the extreme estimations of wheel load is used as the final extreme estimation of wheel load. The dynamic coefficients that match the design life of the ballastless track structure are obtained.

Extending the blended generalized extreme value distribution

AbstractThe generalized extreme value (GEV) distribution is commonly employed to help estimate the likelihood of extreme events in many geophysical and other application areas. The recently proposed blended generalized extreme value (bGEV) distribution modifies the GEV with positive shape parameter to avoid a hard lower bound that complicates fitting and inference. Here, the bGEV is extended to the GEV with negative shape parameter, avoiding a hard upper bound that is unrealistic in many applications. This extended bGEV is shown to improve on the GEV for forecasting heat and sea level extremes based on past data. Software implementing this bGEV and applying it to the example temperature and sea level data is provided.

Dynamic reliability assessment for high CFRDs incorporating ground motion and material parameter variability via GPDEM

The randomness of earthquake and material parameters significantly impacts the seismic response and dynamic reliability of geotechnical engineering structures. In this study, a dynamic reliability analysis method for high concrete face rockfill dams (CFRDs) under the influence of random factors is proposed by combining random sample generation methods with the generalized probability density evolution method (GPDEM). Firstly, the spectral representation-random function method is employed to achieve dimensionality reduction expression for intensity-frequency non-stationary ground motion. Sensitivity analysis is then used to identify the random variables of the rockfill constitutive model, and the improved generalized F-discrepancy (GF-discrepancy) method is utilized to generate random material parameters. Then, the random samples of earthquake and material parameter are introduced into the finite element calculation of CFRD, conducting a series of dynamic finite element calculations. Finally, GPDEM is used to derive probability information and dynamic reliability from the deterministic time history responses. The results indicate that random factors contribute to the variability of seismic response and dynamic reliability. Stochastic ground motion significantly affects extreme values, while variability in material parameters has a more pronounced influence on the overall process of seismic response. These findings provide valuable references for the seismic design of CFRDs.

A Methodological Approach to Improving Extreme Precipitation Reanalysis Data Using the Clausius-Clapeyron Relationship: A Case Study in a Mediterranean City

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

Unified framework for stochastic dynamic responses and system reliability analysis of long-span cable-stayed bridges under near-fault ground motions

Long-span cable-stayed bridges are complex systems with numerous components, and prone to failures of multiple components under near-fault ground motions with long-period velocity pulses. The evaluation of dynamic system reliability of cable-stayed bridges is an important challenging issue. In this work, a unified framework for simultaneously determining both the stochastic dynamic responses and system reliabilities of long-span cable-stayed bridges under near-fault stochastic ground motions is proposed based on direct probability integral method (DPIM) with adaptive strategy. Firstly, the 3-dimensional finite element model of typical cable-stayed bridge and the stochastic synthesis model of near-fault ground motions are established. Based on probability density integral equation, new formulas in input probability space of time-variant moments of response are derived. Then, DPIM with adaptive strategy is suggested to calculate the mean value and standard deviation of response via new formulas. Dynamic system reliability of cable-stayed bridge under near-fault stochastic ground motions is evaluated by establishing failure criteria of extreme value mappings of multiple performance functions. Finally, numerical results indicate that the long-period velocity pulse of near-fault motions imposes an important effect, resulting in large mean responses and failure of cables. Failure probability of cables is significantly increased with the occurrence of velocity pulses, which causes the decrease of system reliability of cable-stayed bridge. Thus, the randomness and velocity pulse effect of near-fault ground motions should be seriously considered for reliability-based design and safety assessment of cable-stayed bridges.

Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory

Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory

Estimating rate of lung function change using clinical spirometry data

RationaleIn chronic obstructive pulmonary disease (COPD), accurately estimating lung function from electronic health record (EHR) data would be beneficial but requires addressing complexities in clinically obtained testing. This study compared...

Significance of different probability distributions in flood frequency analysis of Brahmani-Baitarani River Basin, India

Appropriate flood management in a basin requires analysis of peak discharge prediction and flood frequency. The main aim is to find the most suitable distribution model at various gauges for the entire Brahmani-Baitarani River basin for proper flood management. Daily flow data lengths ranging from 23 to 46 years for multiple gauges are obtained from the Central Water Commission. The present analysis employs the Generalized Extreme Value (GEV), Gumbel Max, Log-Pearson III (LP3), and Lognormal models to estimate the flood quantiles of various return periods. The L-moment, Method of Moment, and Maximum Likelihood Method are applied to estimate the model parameters. Goodness-of-fit tests, accuracy measures, and relative scoring indicated LP3 as the best-fit model, followed by GEV. The results indicated that floods with return periods of longer than 100 years can be considered for designing hydraulic structures in the upper reaches of the basin, whereas floods with return periods of longer than 50 years can be considered for the lower reaches. Significant constraints in the present study involve a limited number of gauges and their varied record lengths in this extensive basin. The findings of this study can be used in water resource planning, flood hazard mapping, and the design of hydraulic structures within similar catchments.

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

Quantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domains

Atrial fibrillation (AF) affects millions of people in the world, causing increased morbidity and mortality. Treatment involves antiarrhythmic drugs and catheter ablation, showing high success for paroxysmal AF but challenges for persistent AF. Experimental evidence suggests reentrant waves and rotors contribute to AF substrates. Ablation procedures rely on electroanatomical maps and electrogram (EGM) signals; however, current methods used in clinical practice lack consideration for time–frequency varying EGM components. The fractional Fourier transform (FrFT) can be adopted to capture time-varying frequency components, thereby enhancing the comprehension of arrhythmogenic substrates during AF for improved ablation strategies. To this end, a FrFT-based algorithm is developed to characterize non-stationary components in EGM signals from simulated AF episodes. The proposed algorithm comprises a pre-processing step to enhance the coarser features of the EGM waveform, a windowing process for dynamic assessment of the EGM, and a FrFT order optimization stage that seeks compact signal representations in fractional Fourier domains. The resulting order is related to the rate of frequency change in the signal, making it a useful indicator for frequency-modulated components. The FrFT-based algorithm is implemented on EGM signals from AF simulations in 2D domains representing a region of the atrial tissue. Consequently, the computed optimum FrFT orders are used to build maps that are spatially correlated to the underlying propagation dynamics of the simulated AF episode. The results evince that the extreme values in the optimum orders map pinpoint the localization of fibrillatory mechanisms, generating EGM activation waveforms with varying frequency content over time.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.