Maximum Research Articles

Generalized fiducial inference for the GEV change-point model

Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we propose a change-point detection procedure for GEV distribution based on generalized fiducial inference. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis–Hastings algorithm is utilised to estimate the location of the change point and its confidence interval. In addition, the generalized fiducial factor is used to test whether there is a change point. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, we apply the proposed method to the annual maximum rainfall data in Beijing.

Work-Zone Safety Analysis: Evaluating Rear-End Crash Risk with Extreme Value Theory

Work-Zone Safety Analysis: Evaluating Rear-End Crash Risk with Extreme Value Theory

Willingness to Pay for Blueberries: Sensory Attributes, Fruit Quality Traits, and Consumers’ Characteristics

Understanding consumers’ preferences for fruit quality attributes is key to informing breeding efforts, meeting consumer preferences, and promoting increased market demand. The objective of this study was to assess the effect of fruit quality traits and hedonic sensory evaluation on consumers’ willingness to pay (WTP) for a selection of fresh northern and southern highbush blueberry cultivars. The WTP was elicited by using a double-bounded contingent valuation conducted in conjunction with a consumer sensory test. Two types of models were estimated using either sensory evaluations (i.e., consumer preference and consumer intensity) or instrumental measurement data (i.e., measures of soluble solids, titratable acidity, sugars, acids, and firmness) as explanatory variables to model WTP. Results using sensory evaluations indicated that flavor liking, flavor intensity, and sweetness intensity are key factors that influence consumers’ acceptance and WTP for blueberries. A regression analysis using instrumental measurements indicated that measures related to sweetness and acidity traits are important factors that determine WTP. Higher WTP was associated with higher total sugar content across different levels of total organic acid. The WTP increases with organic acid content, because this is needed for enhanced flavor; however, WTP declines at high concentrations of organic acid. Except for extreme values of firmness, the WTP increased as measures of fruit firmness increased, indicating a consumer preference for firmer blueberries. Overall, the results provided new insights into the relationships between consumer preference and WTP and fruit quality benchmarks to select for improved quality.

Kansei engineering for the intelligent connected vehicle functions: An online and offline data mining approach

The big data era enables automakers to mine users’ affective (Kansei) requirements for the car design. However, existing literature mostly applies text mining with users’ online comments, possibly leading to biased results since users without online comments were not considered. To fill in this gap, this paper proposes to jointly analyse users’ online commenting and offline usage big data, and develops a novel framework to efficiently fuse these two datasets for the Kansei engineering of the intelligent connected vehicle (ICV) functions. A behaviour-enhanced large language model is proposed to process users’ online comments; then, users’ Kansei requirements are further jointly analysed with their offline in-cabin behaviour data, by the proposed NLP-MDCEV (natural language process — multiple discrete-continuous extreme value) model, to understand user’s complex discrete and continuous choice decisions in the smart cockpit. In addition, the proposed framework aims to solve the problem of design tasks prioritization, where not all the Kansei requirements can be met if design resources are limited. The proposed framework is applied in the studied new energy vehicle company, with more than nine-months’ online comments and six-months’ offline usage data, where results suggest its merits of economic, efficient, and effective.

New Professionals and Experiences of Extreme Values Violations: A Study of Novice Physicians in Kenya

New Professionals and Experiences of Extreme Values Violations: A Study of Novice Physicians in Kenya

New Professionals and Experiences of Extreme Values Violations: A Study of Novice Physicians in Kenya

New Professionals and Experiences of Extreme Values Violations: A Study of Novice Physicians in Kenya

Experimental analysis of extreme wave loads on a containership

Model-scale experiments were conducted to investigate the non-linearities associated with the Vertical Bending Moment (VBM) of a rigid container vessel in irregular waves. Long-duration runs ( ≃ 25 hours of data in full scale) were performed to allow the calculation of statistically meaningful extreme values of hogging and sagging on five different sea-states (from Hs = 6 m up to Hs = 17 m).The model tests are presented in detail, and sampling errors are estimated. It is found that the ratio between linear and non-linear VBM (the so-called non-linear factor) can be modeled as a function of the linear value only. For a given linear value, the non-linear correction is the same, whatever the sea state. This observation, new in the fully non-linear case, is very valuable as it opens the door to efficient design assessment methodologies such as increased design sea state or design waves.

Effects of perinatal exposure to daily maximum THI and THI fluctuations on serum total proteins and health of preweaned Holstein heifers raised in a dry climate.

The objective of this study was to assess the effects of the exposure to daily maximum and temperature-humidity index (THI) and to daily THI fluctuations (∆THI = maximum THI - minimum THI) at exposure periods comprising 2 d before birth to birth (-2 d), birth date (0 d), birth to 2 d of age (+2 d), and birth to 7 d of age (+7 d) on serum total proteins (STP), transfer of passive immunity (TPI), and the occurrence of scours and respiratory disease. A total of 841 Holstein heifer calves were retrospectively observed from -2 d until 65 days of age. Colostrum quality was assessed using a colostrometer to ensure a minimum globulin concentration of 52mg/mL in the colostrum fed to the study calves. Two temperature and relative humidity sensors were installed at the calf yard. Maximum, minimum, and ∆THI values were obtained for each exposure period, and thermal exposure categories were defined as heat stress (HS: maximum THI>70 units; non-HS: THI≤70 units) and ∆THI (low <20 units, medium ≥20 to ≤30 units, high >30). The TPI was classified as poor (STP <5.1g/dL), fair (5.1 and 5.7g/dL), good (>5.7 and 6.1g/dL), and excellent (≥ 6.1g/dL). Associations between the thermal exposure categories and the study outcomes were examined using ANOVA, logistic regression, and survival analyses. No differences in STP at -2 d were observed between HS and non-HS calves (6.83±0.05 vs. 6.91±0.05g/dL), whereas HS exposed calves at 0 d tended to have lower STP compared with non-HS calves (6.82±0.05 vs. 6.92±0.05g/dL). Calves exposed to small ∆THI at 0 d had greater STP compared with calves exposed to medium ∆THI (7.00±0.06 vs. 6.75±0.05g/dL). No association was found between HS, and ∆THI categories and the TPI category. The odds of scours were about two times greater in HS calves compared with non-HS calves at all exposure periods. In addition, HS calves were affected by scours between 9 and 15 days earlier than non-HS calves. Furthermore, high ∆THI favored the development of respiratory problems compared with medium and low ∆THI. Assessment of extreme THI values and THI fluctuations provides a research opportunity for assessing thermal stress in dairy heifer calves raised in dry climate.

Influence of Ground Motion Non-Gaussianity on Seismic Performance of Buildings

The non-Gaussian feature of seismic ground motion has been reported in some works. However, there remains a lack of research on the influence of the ground motion non-Gaussianity on the seismic performance of buildings, which motivates this study. By employing a non-Gaussian non-stationary random process simulation method previously proposed by the authors, 40,000 ground motion acceleration signals are efficiently generated, including 20,000 Gaussian and 20,000 non-Gaussian records. As computational examples, a four-story frame building and a three-tower super-tall building are selected. The generated acceleration signals serve as external excitations for the two buildings, allowing for a comparison of the differences in seismic structural responses caused by the Gaussian and non-Gaussian earthquake groups. Probability analysis is performed using top-layer displacement and maximum inter-story drift ratio as damage indicators. The results show that the structural responses induced by both Gaussian and non-Gaussian earthquake groups have identical first- and second-order moments but different higher-order moments. The responses from non-Gaussian earthquakes display distinct non-Gaussian traits, with their distribution of extreme values exhibiting a longer tail compared to the Gaussian counterparts. This leads to a notably larger value of non-Gaussian responses under high crossing probabilities, with an amplification that can surpass 18%.

Change point detection in length-biased lognormal distribution

In this paper, we develop two procedures for identifying the dynamic trends in the parameters of length-biased lognormal distribution based on likelihood ratio and modified information criterion. These methods mainly consider the test of the existence of the change point and provide the maximum likelihood estimation of the change point when the change point exists. In addition, the asymptotic distribution of test statistic based on likelihood ratio is derived as an extreme value distribution, while the asymptotic distribution of test statistic based on modified information criterion is derived as a chi-square distribution. And the consistency of parameter estimation is proved. Simulations are conducted to study the performance of the proposed method in terms of power, coverage probabilities and average sizes of confidence sets. The proposed methods are applied to a real data to illustrate the detecting procedures.

Pricing of insurance-linked securities: a multi-peril approach

In this paper we build a methodology for pricing of insurance-linked securities which are tied to multiple natural catastrophe perils. As a representative example, we construct a multi-peril catastrophe (CAT) bond which can be linked to the industry loss indices or actual losses incurred by an insurer. We provide pricing formulas for such CAT bonds. We illustrate the introduced methodology on the US natural catastrophe data obtained from Property Claim Services (PCS). Within this dataset, we specifically examine two types of risks: losses associated with wind and thunderstorm events, and those linked to winter storm events. Then, we fit and validate the underlying compound non-homogeneous Poisson processes taking into account the fact that the data are left-truncated. The best fitted loss distributions appear to be Burr and Generalised Extreme Value and for the first peril and log-normal for the second. Finally, we visualise the zero-coupon CAT bond prices for the selected best-fitted models.

The legacy of STAHY: Milestones, achievements, challenges, and open problems in statistical hydrology

Statistical tools are crucial for a variety of hydrological applications, whether to model processes and enhance understanding and knowledge or to design infrastructure systems. Given the rapid evolution of statistical methods and the need for a solid theoretical foundation for their correct application, a multidisciplinary community (STAHY-WG) aggregated under the IAHS umbrella to contribute to this research field. Now, after more than fifteen years since its inception, this paper summarizes the main achievements of this productive community collaboration in four (of many) branches of statistical hydrology: extreme value analysis, multivariate analysis, time series analysis, and regionalization. The aim is to provide an overview of recent developments, offer practical suggestions (e.g. software packages), and outline future challenges to support scientists and practitioners in their endeavors within the realm of statistical hydrology studies.

Comparison Between Three Statistical Methods for the Extreme Value Analysis of Waves and the Projection of Return Periods

Comparison Between Three Statistical Methods for the Extreme Value Analysis of Waves and the Projection of Return Periods

How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

The SPAR Model: A New Paradigm for Multivariate Extremes: Application to Joint Distributions of Metocean Variables

Abstract This paper presents the application of a new multivariate extreme value model for the estimation of metocean variables. The model requires fewer assumptions about the forms of the marginal distributions and dependence structure compared to existing approaches, and provides a flexible and rigorous framework for modeling multivariate extremes. The method involves a transformation of variables to polar coordinates. The tail of the radial variable is then modeled using the generalized Pareto distribution, with parameters conditional on angle, providing a natural extension of univariate theory to multivariate problems. The resulting model is referred to as the semi-parametric angular-radial (SPAR) model. We consider the estimation of the joint distributions of (1) wave height and wave period, and (2) wave height and wind speed. We show that the SPAR model provides a good fit to the observations in terms of both the marginal distributions and dependence structures. The use of the SPAR model for estimating long-term extreme responses of offshore structures is discussed, using some simple response functions for floating structures and an offshore wind turbine with monopile foundation. We show that the SPAR model is able to accurately reproduce response distributions, and provides a realistic quantification of uncertainty.

Optimizing the Size of a Moving Annular Hollow Laser Heat Source

The physical phenomenon of the annular hollow laser surface treatment process is complex, and the internal mechanism involves multiple disciplines and fields. In addition to the general parameters of laser beams, such as laser power and scanning speed, an annular hollow laser beam exhibits unique physical characteristics, including hollow ratio and hollow area. The selection of the inner and outer annular radii of the laser plays a critical role in the study of metal surface heat treatment. From the point of view of heat transfer, the entransy dissipation theory is introduced in the metal surface treatment process with an annular hollow heat source. Firstly, using the principle of the extreme value of the entransy dissipation rate, under a constant heat flux boundary condition, the entransy dissipation rate is obtained through the temperature field distribution in the calculation area by numerical simulation. Secondly, the selection of the inner and outer ring radii of the annular laser is explored, and the average temperature difference of the heating surface is minimized to reduce the thermal stresses of the material. This paper seeks new insights into the geometric parameters of the inner and outer radii of the annular heat source.

ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.

Reliability evaluation of reinforced concrete arch bridges during construction based on LGSSA‐SVR hybrid algorithm

AbstractDuring the cantilever casting process of reinforced concrete arch bridges with cantilever cast‐in‐situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200 m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502–3.608. The indexes of buckling cable stress reliability are 3.806–6.784. The indexes of arch ring stress reliability are 4.379–7.562, and the indexes of buckling tower deflection reliability are 3.608–8.123.

Application of Interactive Object Model in Sports Teaching

Traditional physical education is just repeated training without quantitative teaching and training data results. Multimedia information analysis and retrieval technology can make repeated research on the content of competition or training, and increase the quantitative analysis of training indicators. Therefore, this paper proposes an interactive compressed domain key video object model to realize the design and production of physical education video, so as to quantify the effect of physical education. This paper discusses the interactive design and application of PE teaching video from three aspects: static, dynamic, and semantic. The interactive object model proposed in this paper can formulate corresponding strategies for different key sports video objects. The distortion rate of the complete key video object is 2.3%, which is 0.09% lower than the distortion rate of the Kim2002 scheme. From a usage perspective, through the scheme proposed in this article, coaches can obtain teaching videos with simple initial extreme values on an interactive interface, and the distortion rate of the video object is lower than that of traditional schemes.

The application of the analysis framework for compound extreme event dependencies in China

AbstractThe escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process‐based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.