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Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research utilizes a fitted distribution for calculating fire insurance premiums in Indonesia. This research aims to study, analyze, and estimate the premium of some coverage on fire insurance data in Indonesia. Here, we study data on fire insurance in Indonesia from the period of 2006 to 2016. Property that can be insured based on the type of building used is called occupation. This is regulated in the 2017 Otoritas Jasa Keuangan (OJK) Circular Letter. Furthermore, one type of occupation is selected namely occupation number 2937 and as a result, we analyze 530 claims in total with three types of coverage; (i) building, (ii) stock and content, and (iii) building, stock, and content including severity of the claim. For the number of claims, we assume the data following the Poisson distribution. In contrast, for the severity of claim, two of the three candidate distributions are heavy-tailed distributions i.e. Weibull, Lognormal, and the latter is light-tailed distribution Gumbel. At first, it is assumed that the severity is Weibull distribution, considering that fire is a rare occurrence and can be considered as a Non-Homogeneous Poisson process. To obtain the best-fit distribution from these distributions, the traditionalmethod Kolmogorov-Smirnov is applied for each distribution. We also estimate each parameter for a best-fit distribution. Those parameters are used to estimate the premium for each coverage above. According to the probability density function's graph, the most fitted distribution for (i) and (ii) is Gumbel while the most fitted distribution for (iii) is Lognormal. The Indonesia's fire insurance data fits the heavy-tail distributions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is the canonical combinatorial optimization problem famous throughout literature. There exists an objective function associated with every feasible solution. However, the increase in the number of possible solutions makes this an NPHard problem. We show that the central limit theorem (CLT) applies to the problem. We then conduct extensive computational testing to show that the cycle lengths tend to a normal distribution as the problem grows large. When the size of the TSP problem exceeds computational power, better understanding solution distributions allows us to save resources. This is a non-trivial result as understanding solution distributions in huge TSP problems helps us to minimize computational effort that may not lead to significantly better results. [ABSTRACT FROM AUTHOR]

ObjectiveThis paper aims to study the coupling response law of an electromagnetic pulse acting on a pyramid horn antenna under the condition of the random distribution of incident parameters. Methods First, a statistical analysis model of the electromagnetic pulse response of the horn antenna is established on the basis of a computer simulation technology (CST) and Matlab co-simulation. A simulation model of the antenna electromagnetic impulse response is then established by CST, and the rationality of the model is verified by the voltage standing wave ratio (VSWR) and gain curve. The impedance characteristics of the radio frequency port of the communication system under different states such as transmitting and receiving are considered. The coupling response indexes of antenna ports such as open circuit voltage (OCV), short circuit current (SCC) and load current are obtained under high-altitude electromagnetic pulse (HEMP) irradiation with a peak field strength of 50 kV/m. Finally, the azimuth, elevation and polarization angles are set in the spherical coordinate system to obey the uniform distribution, and Matlab is used to fit the main signal indicators such as the peak value and waveform energy of the antenna load response waveform. ResultsThe results show that the probability distribution of the main signal indicators is mostly convex. Taking the waveform peak value as an example, there is a 90% probability that the peak value of the response waveform is less than 0.13A, which is only 16.2% of the maximum peak value of 0.8A; that is, except in the specific electromagnetic pulse incident range, the signal indicators of the pyramid horn antenna response waveform are kept at a low level in most cases.ConclusionThe results of this study lay the foundation for the electromagnetic pulse vulnerability analysis of communication systems.

The traveling salesman problem (TSP) is the canonical combinatorial optimization problem famous throughout literature. There exists an objective function associated with every feasible solution. However, the increase in the number of possible solutions makes this an NP-Hard problem. We show that the central limit theorem (CLT) applies to the problem. We then conduct extensive computational testing to show that the cycle lengths tend to a normal distribution as the problem grows large. When the size of the TSP problem exceeds computational power, better understanding solution distributions allows us to save resources. This is a non-trivial result as understanding solution distributions in huge TSP problems helps us to minimize computational effort that may not lead to significantly better results.

Fitting stochastic input-process models to data and then sampling from them are key steps in a simulation study but highly challenging to non-experts. We present Neural Input Modeling (NIM), a Generative Neural Network (GNN) framework that exploits modern data-rich environments to automatically capture simulation input processes and then generate samples from them. The basic GNN that we develop, called NIM-VL, comprises (i) a variational autoencoder architecture that learns the probability distribution of the input data while avoiding overfitting and (ii) long short-term memory components that concisely capture statistical dependencies across time. We show how the basic GNN architecture can be modified to exploit known distributional properties—such as independent and identically distributed structure, nonnegativity, and multimodality—to increase accuracy and speed, as well as to handle multivariate processes, categorical-valued processes, and extrapolation beyond the training data for certain nonstationary processes. We also introduce an extension to NIM called Conditional Neural Input Modeling (CNIM), which can learn from training data obtained under various realizations of a (possibly time series valued) stochastic "condition," such as temperature or inflation rate, and then generate sample paths given a value of the condition not seen in the training data. This enables users to simulate a system under a specific working condition by customizing a pre-trained model; CNIM also facilitates what-if analysis. Extensive experiments show the efficacy of our approach. NIM can thus help overcome one of the key barriers to simulation for non-experts. [ABSTRACT FROM AUTHOR]

The Same--Different task presents two stimuli in close succession and participants must indicate whether they are completely identical or if there are any attributes that differ. While the task is simple, its results have proven difficult to explain. Notably, response times are characterized by a fast-same effect whereby Same responses are faster than Different responses even though identical stimuli should be exhaustively processed to be accurate. Herein, we examine a little more than a quarter million response times (N = 255,744) obtained from 327 participants who participated in one of 14 variants of the task involving minor changes in the stimuli or their durations. We performed distribution fitting and analyzed estimated parameters stemming from the ex-Gaussian, lognormal, and Weibull distributions to infer the cognitive processing characteristics underlying this task. The results exclude serial processing of the stimuli and do not support dual-route processing. The fast-same effect appears only through a shift of the entire response time distributions, a feature impossible to detect solely with mean response time analyses. An attention-modulated process driven by entropy may be the most adequate model of the fast-same effect. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of the dynamic increase in data in real life and that the naive Bayes (NB) classifier only accepts or rejects the sample processing results, resulting in a high error rate when dealing with uncertain data, this paper combines three-way decision and incremental learning, and a new three-way incremental naive Bayes classifier (3WD-INB) is proposed. First, the NB classifier is established, and the distribution fitting is carried out according to the minimum residual sum of squares (RSS) for continuous data, so that 3WD-INB can process both discrete data and continuous data, then carry out an incremental learning operation, select the samples with higher data quality according to the confidence of the samples in the incremental training set for incremental learning, solve the problem of data dynamics and filter the poor samples. Then we construct the 3WD-INB classifier and determine the classification rules of the positive, negative and boundary domains of the 3WD-INB classifier, so that the three-way classification of samples can be realized and better decisions can be made when dealing with uncertain data. Finally, five discrete data and five continuous data are selected for comparative experimental analysis with traditional classification methods. The results show that 3WD-INB has high accuracy and recall rate on different types of datasets, and the classification performance is also relatively stable. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To analyze the electromagnetic scattering characteristics of stealth targets, radar cross section (RCS) values of the typical stealth aircraft model are evaluated by electromagnetic simulation. Based on the RCS electromagnetic simulation results, RCS statistical histograms of the stealth aircraft model are created. For describing the RCS statistical characteristics of stealth aircrafts, a Weibull‐generalized gamma mixture distribution model is proposed in this study. Some special cases of the Weibull‐generalized gamma mixture distribution model are summarized. Utilizing the nonlinear least squares technique, fitting results of the proposed and conventional RCS distribution models for the RCS statistical characteristics of typical stealth aircarfts are compared. Mean square error test and R‐square (R2 ${R}^{2}$) test results show that the proposed distribution model obtains better fitting results than the conventional RCS distribution models, which indicates that the proposed distribution model is a more efficient RCS statistical model. [ABSTRACT FROM AUTHOR]

Taxonomic spectra, i.e. relations between supraspecific taxa by the number of included species, remain poorly understood in aspect of the mathematical properties. We studied taxonomic spectra of plants (Magnoliophyta, Bryophyta), animals (Coleoptera, Aves), fungi (Agaricomycetes) and terrestrial protists (Myxomycetes), found in the Homilsha Forests National Nature Park (North-East of Ukraine), and concluded that they correspond to the hollow-curve distribution at the level of genera, families and orders. The spectra of most taxa, as shown by the Akaike information criterion, are closely approximated by the log-series distribution model at all taxonomic levels. This type of distribution is typical for the species abundance curves, based on collections made from small areas. At the same time, in the genera–families–orders row the similarity to the lognormal distribution increases. The central values and variability vary considerably between different taxonomic groups and ranks, however, without affecting the type of distribution. The number of orders in all taxa except Bryophyta has reached the saturation and coincides with the curve of the estimated number of orders according to the Chao1 coefficient. For families and especially genera the correspondence with estimated number of species is much lower. Our results do not confirm the assumption that hollow-curve distributions of taxonomic spectra result from the artificial fragmentation of taxa. These distributions neither depend on the insufficient knowledge about the species composition at the locality, nor reflect the size of the studied area. The presence of such distributions in both local and global biota of different groups may be explained by the common features of their evolution, especially by the existence of relict orphan groups. The fact that in Homilsha Forests the kurtosis and skewness of distributions decreases in the genera–families–orders row can therefore be explained by the relatively low percentage of the high-rank orphan taxa in the local biota. This may be a common feature of communities studied at small geographical scale, since orphan taxa often demonstrate a high level of endemism. Comparative studies of local communities from different climate zones may help to understand how universal are the patterns, described herein.

Sediment rating curves (SRCs) constitute a satisfactory deterministic approach to represent the sediment regime in catchments with limited or poor-quality records. Conversely, stochastic approaches like magnitude–frequency analysis have only lately been used directly on sediment records. This study aims to provide insight on the impact of SRC methodologies on the assessment of sediment discharge (Qs) variability. Three theoretical probability distributions were fitted to simulated mean monthly coarse (>100 μm) suspended Qs time series. The latter were developed by applying four SRC methodologies to the discharge records of 11 cross-sections of four major rivers in northwestern Greece. The analysis indicates that the SRC method used can influence the shape of the marginal distribution of the resulting time series. Its improper selection may lead to a dramatic underestimation of the sediment load maxima. The effect of different return periods per SRC method and theoretical distribution on Qs estimation are also assessed. [ABSTRACT FROM AUTHOR]

It is of great merit to construct apple drought index and analyze its hazard risk so as to support apple drought monitoring, prevention and mitigation, as well as agricultural disaster insurance management. Based on meteorological data, associated with historical disaster and phenophase data, apple drought index (DI) in northern China is firstly constructed, which fully considered previous and current water demand and precipitation supply. Afterwards, historical disaster remodeling, disaster sample reconstruction and process-based historical disaster analysis are comprehensively used as key technologies in evaluating the level of apple drought, integrating the independent sample T-test, Kolmogorov-Smirnov(K-S) test, cumulative probability inverse method, etc. Meanwhile, the apple drought risk is consequently estimated to seek the characteristics of apple drought hazard in detail. The results show that there are significant differences by independent T-test of drought index (DI) between historical recorded disaster samples and the non-disaster samples in three apple tree phenophases, i.e., the tree germinating to flower budding period, flower budding to full bloom and full bloom to mature periods (passing the test of 0.05 level). Therefore, the apple drought index constructed can effectively represent the drought disaster in different stages of apple development. The threshold of DI in the same level is higher in tree germinating to flower budding period, followed by flower budding to full bloom and full bloom to mature periods. The drought risk during flower budding to full bloom is high with regional average apple drought hazard index (M) of 0.44, followed by tree germinating to flower budding period and full bloom to mature period, with reginal average M of 0.40 and 0.25, respectively. Furthermore, the Bohai Bay region and northern Loess Plateau are detected as high-risk areas of apple drought. The evaluation method of apple drought based on historical disaster processing and re-analysis can provide new ideas for economic forest and fruit meteorological disaster research. The results of apple drought hazard risk assessment could provide evidence for the prevention and mitigation of apple drought in northern China.

Recent developments in SDN technologies have resulted in flow-based forwarding becoming are a feasible alternative for traditional mechanisms. However, as the efficiency of flow-based solutions strongly depends on traffic characteristics, they need to be evaluated using realistic and accurate flow models. In this paper, we present a software framework, called flow-models, for creating precise and reproducible statistical flow models from NetFlow/IPFIX flow records. The flow-models package can be used to merge split records, calculate histograms of flow features and create General Mixture Models fitting them. The models created by flow-models can be used both as an input in analytical calculations and to generate realistic traffic in simulations.

Probabilistic models of corrosion rate estimation in the case of two NiTi alloys obtained by different technological processes are analysed in this paper. The depth of corrosion was measured by focused ion beam analysis on a metal surface that was not protected by an anti-corrosion coating. The samples were exposed to the influence of three different seawater environments, and empirical data were obtained in a systematic measurement procedure after 6, 12 and 18 months. Assuming that corrosion processes begin immediately after exposure of the samples to the influence of the seawater environment, and observing the corrosion rate as a random variable affected by various stochastic processes, the formed standard linear corrosion model was analysed by a statistical approach. The three best-fitted three-parameter distributions which can describe the changes in the corrosion rate for NiTi alloys exposed to the influence of the seawater environment adequately were obtained by fitting the continuous theoretical distributions. Adequate statistical tests showed similarities and differences in the behaviour of the two observed NiTi alloys from the point of view of corrosion processes caused by air, tide and sea. [ABSTRACT FROM AUTHOR]

In view of the excess strength and the excessive quality of a comprehensive transmission box which belongs to special vehicle, it is necessary to optimize box further. In the optimization process, based on the empirical formula of the rotational torque a larger dynamic load factor is calculated by using traditional method,which reduces the subsequent optimization space. Therefore, the dynamic load factors in the process of stationary transmission are studied based on the load spectrum. Firstly, the box body was tested by bench test, then the load spectrum of the dangerous part was measured. Secondly, the dynamic load factors in a stationary time history were obtained by dealing with these load spectrum. Thirdly, the maximum likelihood method and the goodness of fit AD value were used to identify and validate these dynamic load factors. Finally the dynamic load factor was obtained at 99 th percentile. The 99 th percentile dynamic load factor was compared with the dynamic load factor obtained from the empirical formula. The results show that under the condition of smooth transmission, the empirical formula is larger, the structural design is conservative, and the 99 th percentile dynamic load factor is reasonable, which is helpful to the realization of the lightweight design.

Several guideline documents on pedestrian crossing warrants are followed around the world. Peak hour pedestrian volume “P” and vehicular volume “V” are two most commonly used factors in these guidelines. PV2 is a criteria, which is used in guideline documents of India, Iran and UK to identify the need of warranting a particular mid-block location. In India, these guidelines were adapted from UK in 1988 and have not been revised in the last three decades. These guidelines report a benchmark value, in the excess of which a location should be warranted. The benchmark values are based on peak flows of 1980s, which have increased drastically over the years. In addition, the guidelines do not identify the type of crossing facility, which should be provided at a particular location. Therefore, these guidelines need to be revised. In this paper, PV2 matrices have been developed for a variety of road configurations using the maximum field hourly flows as the upper bounds. Further, probability distributions have been fitted to the PV2 values. Threshold values have been proposed based on the curvilinear characteristics of the probability distributions. The revised PV2 values vary from 0.6×108 to 2.1×1011 for different roadway configurations. The ranges formed using these values have been used to classify the type of crossing facility to be installed using a systematic hierarchical approach. The warrant charts and PV2 value ranges can be used to identify the need, as well as the most appropriate crossing facility for the site based on the peak hour pedestrian volume and vehicular volume. The proposed PV2 values and warrants are based on Indian traffic flow conditions. These threshold values may require modifications for application in any other country.

Abstract Background Statistical approaches to genetic sequences have revealed helpful to gain deeper insight into biological and structural functionalities, using ideas coming from information theory and stochastic modelling of symbolic sequences. In particular, previous analyses on CG dinucleotide position along the genome allowed to highlight its epigenetic role in DNA methylation, showing a different distribution tail as compared to other dinucleotides. In this paper we extend the analysis to the whole CG distance distribution over a selected set of higher-order organisms. Then we apply the best fitting probability density function to a large range of organisms (>4400) of different complexity (from bacteria to mammals) and we characterize some emerging global features. Results We find that the Gamma distribution is optimal for the selected subset as compared to a group of several distributions, chosen for their physical meaning or because recently used in literature for similar studies. The parameters of this distribution, when applied to our larger set of organisms, allows to highlight some biologically relavant features for the considered organism classes, that can be useful also for classification purposes. Conclusions The quantification of statistical properties of CG dinucleotide positioning along the genome is confirmed as a useful tool to characterize broad classes of organisms, spanning the whole range of biological complexity.

Several guideline documents on pedestrian crossing warrants are followed around the world. Peak hour pedestrian volume "P" and vehicular volume "V" are two most commonly used factors in these guidelines. PV 2 is a criteria, which is used in guideline documents of India, Iran and UK to identify the need of warranting a particular mid-block location. In India, these guidelines were adapted from UK in 1988 and have not been revised in the last three decades. These guidelines report a benchmark value, in the excess of which a location should be warranted. The benchmark values are based on peak flows of 1980s, which have increased drastically over the years. In addition, the guidelines do not identify the type of crossing facility, which should be provided at a particular location. Therefore, these guidelines need to be revised. In this paper, PV 2 matrices have been developed for a variety of road configurations using the maximum field hourly flows as the upper bounds. Furt her, probability distributions have been fitted to the PV 2 values. Threshold values have been proposed based on the curvilinear characteristics of the probability distributions. The revised PV 2 values vary from 0.6 ·108 to 2.1·1011 for different roadway configurations. The ranges formed using these values have been used to classify the type of crossing facility to be installed using a systematic hierarchical approach. The warrant charts and PV 2 value ranges can be used to identify the need, as well as the most appropriate crossing facility for the site based on the peak hour pedestrian volume and vehicular volume. The proposed PV 2 values and warrants are based on Indian traffic flow conditions. These threshold values may require modifications for application in any other country. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (R 0 ) > 1, and infection waves are anticipated to end if the R 0 < 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents. [ABSTRACT FROM AUTHOR]

Probabilistic models of corrosion rate estimation in the case of two NiTi alloys obtained by different technological processes are analysed in this paper. The depth of corrosion was measured by focused ion beam analysis on a metal surface that was not protected by an anti-corrosion coating. The samples were exposed to the influence of three different seawater environments, and empirical data were obtained in a systematic measurement procedure after 6, 12 and 18 months. Assuming that corrosion processes begin immediately after exposure of the samples to the influence of the seawater environment, and observing the corrosion rate as a random variable affected by various stochastic processes, the formed standard linear corrosion model was analysed by a statistical approach. The three best-fitted three-parameter distributions which can describe the changes in the corrosion rate for NiTi alloys exposed to the influence of the seawater environment adequately were obtained by fitting the continuous theoretical distributions. Adequate statistical tests showed similarities and differences in the behaviour of the two observed NiTi alloys from the point of view of corrosion processes caused by air, tide and sea.

As startups and micro teams adopt real-time collaborative instant messaging solutions, a wealth of data is generated from day to day usage. Making sense of this data can be a challenge to teams, given the lack of inbuilt analytical tooling. In this study we model the distributions of duration, inter-arrival time, word count and user count of real-time electronic chat conversations in a framework, where these distributions can be used as an analogue to service time estimation of problem determination. Using both an enterprise and an open-source dataset, we answer the question of what distribution family and fitting techniques can be used to adequately model real-time chat conversations. Our framework can help startups and micro teams alike to effectively model their real-time chat conversations to allow high value decisions to be made based on their collaboration outputs.

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement.

Modeling the distribution of daily and hourly human mobility metrics is beneficial for studying underlying human travel patterns. In previous studies, some probability distribution functions were employed in order to establish a base for human mobility research. However, the selection of the most suitable distribution is still a challenging task. In this paper, we focus on modeling the distributions of travel distance, travel time, and travel speed. The daily and hourly trip data are fitted with several candidate distributions, and the best one is selected based on the Bayesian information criterion. A case study with online car-hailing data in Xi’an, China, is presented to demonstrate and evaluate the model fit. The results indicate that travel distance and travel time of daily and hourly human mobility tend to follow Gamma distribution, and travel speed can be approximated by Burr distribution. These results can contribute to a better understanding of online car-hailing travel patterns and establish a base for human mobility research.

• The mean effects of crash contributing factors across the world are different than them in Australia. • Globally obtained informative priors improve the precision of parameter estimates in SPFs. • The distribution type of informative priors influences the precision of parameters in SPFs. The precision and bias of Safety Performance Functions (SPFs) heavily rely on the data upon which they are estimated. When local (spatially and temporally representative) data are not sufficiently available, the estimated parameters in SPFs are likely to be biased and inefficient. Estimating SPFs using Bayesian inference may moderate the effects of local data insufficiency in that local data can be combined with prior information obtained from other parts of the world to incorporate additional evidence into the SPFs. In past applications of Bayesian models, non-informative priors have routinely been used because incorporating prior information in SPFs is not straightforward. The previous few attempts to employ informative priors in estimating SPFs are mostly based on local prior knowledge and assuming normally distributed priors. Moreover, the unobserved heterogeneity in local data has not been taken into account. As such, the effects of globally derived informative priors on the precision and bias of locally developed SPFs are essentially unknown. This study aims to examine the effects of globally informative priors and their distribution types on the precision and bias of SPFs developed for Australian crash data. To formulate and develop global informative priors, the means and variances of parameter estimates from previous research were critically reviewed. Informative priors were generated using three methods: 1) distribution fitting, 2) endogenous specification of dispersion parameters, and 3) hypothetically increasing the strength of priors obtained from distribution fitting. In so doing, the mean effects of crash contributing factors across the world are significantly different than those same effects in Australia. A total of 25 Bayesian Random Parameters Negative Binomial SPFs were estimated for different types of informative priors across five sample sizes. The means and standard deviations of posterior parameter estimates as well as SPFs goodness of fit were compared between the models across different sample sizes. Globally informative prior for the dispersion parameter substantially increases the precision of a local estimate, even when the variance of local data likelihood is small. In comparison with the conventional use of Normal distribution, Logistic, Weibull and Lognormal distributions yield more accurate parameter estimates for average annual daily traffic, segment length and number of lanes, particularly when sample size is relatively small. [ABSTRACT FROM AUTHOR]

Analogous to the way wind blows single grains of sand and the subsequent settling back atop sand dunes, we find statistical evidence to claim that the prices of cryptocurrencies exhibit similar unpredicted patterns, characterized by positive or negative jumps. Motivated by extant evidence of asset returns' non-normality, we capture distributional properties of the log-returns of the Bitcoin and the following three cryptocurrencies in terms of market capitalization (Ethereum, Ripple and Bitcoin cash). The total error induced by the fitted distribution is remarkably decreased when the generalized hyperbolic distribution is used, a finding further validated by a series of goodness-of-fit type statistical tests. A complementary analysis for the foreign exchange market is conducted, with inherent similarities to that of cryptocurrencies. We reveal that the generalized hyperbolic distribution can also be used to model very widely traded currency pairs significantly more accurately than the log-normal. • Statistical evidence to claim that the prices of cryptocurrencies exhibit positive or negative jumps. • The mass-size distribution of aeolian dust particles provide an excellent model. • The total error induced by the generalized hyperbolic distribution is remarkably decreased. • A complementary analysis for the foreign exchange market is conducted. • The generalized hyperbolic distribution is used to model very widely traded currency pairs. [ABSTRACT FROM AUTHOR]

A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient. [ABSTRACT FROM AUTHOR]

Due to the time-varying characteristics of the durability of a concrete structure, a long duration of exposure and monitoring in natural conditions is required. The present research provided the indoor accelerated life testing of concrete, which simulated the special natural environments in the Xining area, including solar radiation, dry-wet circulation, freeze-thaw cycle, and salt corrosion. The measured dynamic modulus was taken as an evaluation index for the durability of concrete, and the change of the relative dynamic modulus of the specimen was studied. Meanwhile, a two-parameter Weibull distribution and three-parameter Weibull distribution were proposed based on the stochastic analysis method in order to assess the durability of concrete structure. It has been observed that the indoor accelerated life testing of concrete can make up for the shortcomings of long-term exposure under natural conditions. The variation of the relative dynamic modulus can effectively reflect the durability of the concrete degradation process. By contrasting two methods of stochastic analysis, the three-parameter Weibull distribution method is better to fit the accelerated life data of concrete specimens and to evaluate the durability of concrete structures during service. [ABSTRACT FROM AUTHOR]

Irrigation-induced landslides with long runout distances endanger local communities. Estimating runout distance of landslides may contribute to the mitigation of potential hazards. Conventional mechanism-related methods require a series of experiments and/or numerical simulations that are commonly time-consuming and expensive, yet data-driven models reduce the experimental workload and require less prior knowledge in the geological history as well as mechanical behavior of the material. A data-driven model is proposed to forecast landslide runout distance using geometrical characteristics of the landslide. The geometrical dataset of the shallow loess landslides and loess-bedrock landslides occurred in Heifangtai terrace, China, was employed to develop the model. All geometrical datasets were obtained from field investigation and monitoring. Seven data-mining techniques were used and compared for runout estimation, among which the most optimal technique was integrated in the estimation model for loess slope failures. The multi-layer perceptron method outperforms other algorithms, and thus it was selected for the runout distance estimation model. Parametric models are constructed to fit runout distance based on the estimation. Hazard analysis measurements, including value-at-risk (VaR) and tail-value-at-risk (TVaR), are computed for the parametric distributions, which shows the potential area of impact and number of residential clusters at risk. [ABSTRACT FROM AUTHOR]

Background and Purpose: The aim of this article is to create a virtual forest stand based on the field measurement of spatially separated sample plots and to examine its credibility based on the deviation of the basic characteristics of the virtual stand as compared to the field measurements. Material and Methods: Field measurements were made on 20 circular sample plots with a 20 m radius, set on a 100x100 m grid. By using the univariate Ripley’s K function the regularity of the spatial pattern of trees was analysed. The diameter distribution and the frequencies of height within individual diameter class were mathematically fitted and used for generating the virtual stand. The whole process of generating the virtual stand was done in the R software. Area of study are even-aged silver fir stands in the Croatian Dinarides. Results: The main unit of the virtual stand is a tree, with the purpose that the virtual stand can then be used as a basis for forest stand growth simulators. The result of the research was a virtual stand of 3 ha whose characteristics only slightly differed from the field measured plots. Within the virtual stand, special emphasis has been put on tree heights, which were generated according to the variability of tree height for trees of the same diameter at breast height. Conclusions: Considering the distribution of diameters at breast height, tree heights, the number of trees, basal area and volume, the virtual stand has minimal deviations from the situation in the field and it adequately shows variability measured in the field.

This study attempts to find if better performance in power play leads a team to victory in a Twenty20 match. Based on the methodology devised to do so, the study tries to measure the performance of both the teams during power play overs in terms of batting and bowling. The developed measure is called ‘Prod’ which is a product of the difference of batting and bowling performance of the teams during power play overs. The team with better performance in both the skills during power play is expected to win the match. But it would be difficult to predict the outcome of a match if the performance of a team is better in bowling and worse in batting and vice-versa. A total of 261 matches from different seasons of Indian Premier League (IPL) are considered for the study. The outcomes of 220 matches are predicted based on the performance of two teams in power play out of which 153 of them were correctly predicted. Remaining 41 matches could not be predicted as it is not clear which team performed better during power play. Thus, out of the matches where the dominance of a team was clear in the power play, 70 percent cases that team ultimately won the match in Twenty20 cricket.

The crisis caused by the outbreak of COVID-19 revealed the global unpreparedness for handling the impact of a pandemic. In this paper, we present a first quarter chronicle of COVID-19 in Hubei China, Italy and Spain, particularly focusing on infection speed, death and fatality rates. By analysing the parameters of the best fitting distributions of the available data for the three rates in each of the three regions, we illustrate the pandemic’s evolution in relation to government measures. We compared the effectiveness of lockdown measures by observing the true situation in each dataset, without proposing a mathematical model. The feasibility of obtaining a firm conclusion in regard to the best solution for containing COVID-19 is limited, with a universal solution failing to exist due to globally varying culture, mentality and behaviours. Our method provides valid insights into the individual and national actions implemented and adhered to in order to slow the effect of the pandemic during the first-wave of COVID-19.

The relationship between the norm square of the standardized cumulative distribution and the chi-square statistic is examined using the form of the covariance matrix as well as the projection perspective. This investigation enables us to give uncorrelated components of the chi-square statistic and to provide interpretation of these components as innovations standardizing the cumulative distribution values. The norm square of the standardized difference between empirical and theoretical cumulative distributions is also examined as an objective function for parameter estimation. Its relationship to the chi-square distance enables us to discuss the large sample properties of these estimators and a difference in their properties in the cases that the distribution is evaluated at fixed and random points. [ABSTRACT FROM AUTHOR]

Background: Statistical approaches to genetic sequences have revealed helpful to gain deeper insight into biological and structural functionalities, using ideas coming from information theory and stochastic modelling of symbolic sequences. In particular, previous analyses on CG dinucleotide position along the genome allowed to highlight its epigenetic role in DNA methylation, showing a different distribution tail as compared to other dinucleotides. In this paper we extend the analysis to the whole CG distance distribution over a selected set of higher-order organisms. Then we apply the best fitting probability density function to a large range of organisms (>4400) of different complexity (from bacteria to mammals) and we characterize some emerging global features. Results: We find that the Gamma distribution is optimal for the selected subset as compared to a group of several distributions, chosen for their physical meaning or because recently used in literature for similar studies. The parameters of this distribution, when applied to our larger set of organisms, allows to highlight some biologically relavant features for the considered organism classes, that can be useful also for classification purposes. Conclusions: The quantification of statistical properties of CG dinucleotide positioning along the genome is confirmed as a useful tool to characterize broad classes of organisms, spanning the whole range of biological complexity. [ABSTRACT FROM AUTHOR]

We identify key characteristics of a correlated point process which include moments of the time between arrivals as well as measures of variability and correlation obtained from the counting process over different time intervals. A computational framework to calculate these characteristics is presented for the general MAP(n). We present simple closed-form-expressions for the key characteristics and an efficient algorithm to fit a MAP(2) to a point process. The contributions of this article include 1) developing a computational framework, in the form of partial-moment differential equations (PMDEs) and linear equations to derive a compact matrix exponential expression for the count process moments of a MAP(n), 2) developing an efficient and accurate algorithm to fit a MAP(2) based on count and inter-arrival moments. [ABSTRACT FROM AUTHOR]

Empirical studies have revealed that travel time variability (TTV) can significantly affect travelers’ behaviors and planners’ cost-benefit assessment of transportation projects. It is therefore important to systematically quantify the value of TTV (VTTV) and its impact. Recently, Fosgerau’s valuation method makes this quantification possible by converting the value of travel time (VTT) and the VTTV into monetary unit. Travel time reliability ratio (TTRR), defined as a ratio of the VTTV to the VTT, is a key parameter in Fosgerau’s valuation method. Calculating TTRR involves an integral of the inverse cumulative distribution function (CDF) of the standardized travel time distribution (STTD), i.e., the mean lateness factor. Using a well-fitted STTD is a straightforward way to calculate TTRR. However, it will encounter the following challenges: (1) determination of a well-fitted STTD; (2) non-existence of an algebraic expression for the CDF and its inverse CDF; and (3) lack of a closed-form expression to efficiently calculate TTRR. To circumvent the above issues, this paper proposes a distribution-fitting-free analytical approach based on the Cornish-Fisher expansion as an alternative way to calculate TTRR without the need to fit the whole CDF. The validity domain is rigorously derived for guaranteeing the accuracy of the proposed method. Realistic travel time datasets that cover 17 links are used to systematically explore the feature and accuracy of the proposed method in estimating TTRR. The comparative results demonstrate that the proposed method can efficiently and effectively estimate TTRR. When travel time datasets satisfy the validity domain, the proposed method outperforms the distribution fitting method in estimating TTRR. [ABSTRACT FROM AUTHOR]