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Parallel-connected digital valve arrays are commonly utilized in the pilot stage of the proportional directional valve to enhance dynamic performance and reliability. However, when the digital valve array is driven by a digital signal, it is difficult to optimally assign the signal pulses to each valve. If the assignment is not well executed, it can significantly reduce the switching uniformity of the digital valves or lead to performance degradation of the system. In this paper, a model-based sliding mode control strategy based on the intelligent distribution of control law is proposed and successfully applied to a proportional valve driven by digital valve arrays. The intelligent distribution strategy encompasses a logic distribution algorithm and a circular sliding distribution algorithm that automatically assigns control laws to different valves based on the rolling of the PWM signal cycle. Experimental results confirm that the proposed strategy not only simultaneously reduces the total number of valve switches and enhances the switching uniformity among the valves, but also adapts to the variation in the number of valves. The proposed strategy is not limited to the application of digital valve arrays, it is also applicable in other fields of multi-actuators driven by digital signals, and can simultaneously improve the control accuracy, lifetime, and maintenance friendliness. • A digital proportional valve is proposed, where the pilot stage consists of fixed orifices and digital valve arrays. • The intelligent distribution strategy is designed based on a logic distribution and a circular sliding distribution. • The proposed MSMC with intelligent distribution can be adapted to DVAs with any number of valves. [ABSTRACT FROM AUTHOR]

In this paper the financial properties, economic viability and the risk of investing in an agroforestry system (AFS) for indigenous communities (IC) are evaluated to address a critical gap in the literature. An AFS proposal was designed by: (1) assessing raw data from a rural extension project, university and IC Guarani Kaiowá in the Indigenous Reserve of Dourados (IRD); (2) collecting notes and data from local field experts and IC; (3) selecting a scheme from the literature. The AFS was prepared to meet the needs of the IC, mainly food security, income generation, medicine, timber, cultural aspects, welfare. The AFS included a wide range of species, aiming at the diversified and continuous food production. The study was conducted in the IRD, Dourados, Mato Grosso do Sul, Brazil. As methods for investment analysis, capital budgeting techniques were applied. The minimum attractiveness rate (MAR) was calculated according to the hybrid CAPM model (HA-CAPM). For risk assessment, the Monte Carlo method was triggered (10,000 interactions with the variables price, productivity, and cost variables) using the triangular probability distribution. The AFS showed economic viability, with NPV and EUAV values greater than zero, IRR and MIRR greater than MAR (12.80%), PI greater than 1 and the payback period less than the project duration. The analysis confirmed the economic viability (probability of a negative NPV was null). The research findings provide a comprehensive insight into the importance of AFS in promoting economic well-being and support the implementation or strengthening of AFS in IC, bearing in mind that problems and solutions should not be narrowed to a capitalist approach. [ABSTRACT FROM AUTHOR]

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

As public transportation faces increasing ridership demand, metrics such as the number of passengers denied boarding become important for measuring the service quality of transit systems. Many studies in the past have used automated fare collection (AFC) (also known as smart card data) and automated vehicle location data to infer the probability distributions for commuters that experience unable-to-board (UTB) events in metro systems, but few have studied UTB events for buses. In this paper, we demonstrate that the probability distribution of UTB commuters inferred from AFC data can be modelled by a truncated binomial distribution under certain assumptions. This model is then validated against synthetic UTB events generated using simulations and against actual UTB events recorded from ground surveys. Finally, we apply our model on real AFC data of commuters in the Singapore bus network to serve as a case study. Our method enables transport planners and operators to identify bus stops and time intervals where overcrowding and UTB events is prevalent, so that appropriate measures can be taken to mitigate such occurrences. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel probability distribution model known as the unit compound Rayleigh distribution, which possesses the distinctive characteristic of defining the range within the bounded interval (0,1). Through an in-depth investigation of this distribution, we analyzed various statistical and structural characteristics including reliability function, risk function, quantile function, moment analysis, order statistics, and entropy measurement. To estimate the unknown parameters of our proposed distribution model, we employed maximum likelihood (ML) estimation and Bayesian estimation. Furthermore, we derived several entropy measures based on ML estimation under the unit compound Rayleigh distribution. To comprehensively evaluate the performance of these entropies, we employed the Monte Carlo simulation method to calculate the average entropy estimate, average entropy bias, corresponding mean square error, and mean relative estimate for assessing the performance of various entropies within the unit compound Rayleigh distribution model. Finally, in order to validate its potential for practical applications, two sets of real data were selected for empirical analysis where fitting and parameter estimation were conducted to demonstrate the advantages of utilizing the unit compound Rayleigh distribution in describing and predicting actual data. This study not only introduces a new probability theory and statistics framework by proposing a novel distribution model but also provides researchers and practitioners in related fields with a powerful analytical tool. [ABSTRACT FROM AUTHOR]

The spatial distribution pattern of rural settlements in the Yellow River Basin is scattered and numerous. It is of great significance to study the discrete distribution of rural settlements for achieving high-quality development and promoting rural revitalization strategy. In this paper, we propose an enhanced evaluation model for assessing the spatial distribution dispersion of rural settlements, incorporating the weight of road grade (the road grade refers to the ranking of traffic capacity and importance of a particular type of road, indicating varying levels of time accessibility). We investigate the dispersion characteristics of rural settlements in the Yellow River Basin in 2020, focusing on both county and city scales. Furthermore, we conduct a comprehensive analysis of the spatial differentiation and scale effects of dispersion evaluation outcomes and their driving forces. Our findings reveal the following insights: (1) The road grade significantly influences the dispersion evaluation. When considering road grade in the dispersion calculation, the results align more closely with the actual situation. (2) The dispersion of rural settlements in the Yellow River Basin exhibits a decreasing trend from west to east. Specifically, the dispersion is higher in the upper reaches compared to the middle and lower reaches. Both city and county scales show spatial autocorrelation in dispersion, with a positive spatial correlation observed. High dispersion values cluster in the west, while low values concentrate in the east. Notably, the agglomeration degree is more pronounced at the county scale than at the city scale, highlighting more localized patterns of agglomeration and dispersion. (3) The multiscale geographically weighted regression model emerges as the optimal model for analyzing the driving forces of dispersion. At the city scale, factors such as river density, road density, and rural economy negatively impact dispersion. However, at the county scale, average elevation and rural economy positively affect dispersion, whereas river density, road density, and rural population density have a negative influence. By incorporating the weight of road grade into our evaluation model, we provide a more nuanced understanding of the spatial distribution dispersion of rural settlements in the Yellow River Basin. Our findings offer valuable insights for policymakers and planners seeking to optimize rural settlement patterns and promote sustainable rural development. [ABSTRACT FROM AUTHOR]

Ball bearings operating at low speeds and under heavy loads are susceptible to wear failure, leading to significant economic losses. The existing reliability-based robust design optimization method of the fourth-moment method has high accuracy and does not need to determine the random distribution of the input variables, but it is not possible to apply it to ball bearing wear due to the complexity of the bearing wear state function that cannot be characterized as an explicit form. To address this issue, this paper proposes a novel design method for ball bearing wear. Firstly, a surrogate model is constructed using the Kriging model method to establish a relationship between the bearing design parameters and the mechanical response. Subsequently, a wear reliability model is developed on the basis of the fourth-moment method, and reliability sensitivity analysis is conducted. Finally, the ball bearing wear reliability-based robust design optimization is accomplished through the use of a genetic algorithm. The results of the case calculations demonstrate that the proposed method effectively calculates the ball bearing wear reliability and analyzes the impact of design parameter randomness on reliability. Furthermore, optimizing the design parameters reduces the sensitivity of wear reliability to parameter randomness. [ABSTRACT FROM AUTHOR]

Robust geometric fitting is one of the crucial and fundamental problems in computer vision and pattern recognition. While random sampling and consensus maximization have been popular strategies for robust fitting, finding a balance between optimization quality and computational efficiency remains a persistent obstacle. In this paper, we adopt an optimization perspective and introduce a novel maximum consensus robust fitting algorithm that incorporates the maximum entropy framework into the consensus maximization problem. Specifically, we incorporate the probability distribution of inliers calculated using maximum entropy with consensus constraints. Furthermore, we introduce an improved relaxed and accelerated alternating direction method of multipliers (R-A-ADMMs) strategy tailored to our framework, facilitating an efficient solution to the optimization problem. Our proposed algorithm demonstrates superior performance compared to state-of-the-art methods on both synthetic and contaminated real datasets, particularly when dealing with contaminated datasets containing a high proportion of outliers. [ABSTRACT FROM AUTHOR]

Anomaly detection for network traffic aims to analyze the characteristics of network traffic in order to discover unknown attacks. Currently, existing detection methods have achieved promising results against high-intensity attacks that aim to interrupt the operation of the target system. In reality, attack behaviors that are commonly exhibited are highly concealed and disruptive. In addition, the attack scales are flexible and variable. In this paper, we construct a multiscale network intrusion behavior dataset, which includes three attack scales and two multiscale attack patterns based on probability distribution. Specifically, we propose a stacked ensemble learning-based detection model for anomalous traffic (or SEDAT for short) to defend against highly concealed multiscale attacks. The model employs a random forest (RF)-based method to select features and introduces multiple base learning autoencoders (AEs) to enhance the representation of multiscale attack behaviors. In addressing the challenge of a single model's inability to capture the regularities of multiscale attack behaviors, SEDAT is capable of adapting to the complex multiscale characteristics in network traffic, enabling the prediction of network access behavior. Comparative experiments demonstrate that SEDAT exhibits superior detection capabilities in multiscale network attacks. In particular, SEDAT achieves an improvement of at least 5% accuracy over baseline methods for detecting multiscale attacks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recurrent Neural Networks (RNNs) are classical models for processing sequential data, demonstrating excellent performance in tasks such as natural language processing and time series prediction. However, during the training of RNNs, the issues of vanishing and exploding gradients often arise, significantly impacting the model's performance and efficiency. In this paper, we investigate why RNNs are more prone to gradient problems compared to other common sequential networks. To address this issue and enhance network performance, we propose a method for gradient normalization of network weights. This method suppresses the occurrence of gradient problems by altering the statistical properties of RNN weights, thereby improving training effectiveness. Additionally, we analyze the impact of weight gradient normalization on the probability-distribution characteristics of model weights and validate the sensitivity of this method to hyperparameters such as learning rate. The experimental results demonstrate that gradient normalization enhances the stability of model training and reduces the frequency of gradient issues. On the Penn Treebank dataset, this method achieves a perplexity level of 110.89, representing an 11.48% improvement over conventional gradient descent methods. For prediction lengths of 24 and 96 on the ETTm1 dataset, Mean Absolute Error (MAE) values of 0.778 and 0.592 are attained, respectively, resulting in 3.00% and 6.77% improvement over conventional gradient descent methods. Moreover, selected subsets of the UCR dataset show an increase in accuracy ranging from 0.4% to 6.0%. The gradient normalization method enhances the ability of RNNs to learn from sequential and causal data, thereby holding significant implications for optimizing the training effectiveness of RNN-based models. [ABSTRACT FROM AUTHOR]

In the current paper, size‐dependent mechanical analysis of functionally graded porous (FGP) nanobeams resting on Pasternak foundation in thermal environment is presented based on nonlocal strain gradient theory and Gurtin‐Murdoch surface elasticity theory. The nanobeam is modeled based on Euler‐Bernoulli beam theory, Timoshenko beam theory and Reddy's third‐order shear deformation theory and the set of the governing equations are solved for a clamped‐clamped FGP nanobeam using generalized differential quadrature method. Numerical results show that as porosity parameter increases, static deflection increases and critical buckling load decreases, but its effect on the fundamental frequency strongly depends on the porosity distribution pattern. It was revealed that among all studied porosity distribution patterns, the minimum value of the static deflection and maximum values of critical buckling load and fundamental frequency can be achieved using symmetric distribution pattern of pores. It is concluded that tensional residual stress reduces static deflection and increases both critical buckling load and fundamental frequency and temperature rise has reverse effects. [ABSTRACT FROM AUTHOR]

Twist angle errors along the blade radial direction are uncertain and affected by cutting force, tool wear, and other factors. In this paper, the measured twist angle errors of 13 sections of 72 rotor blades were innovatively analyzed to obtain the rational statistical distribution. It is surprisingly found that the under-deflection systematic deviation of twist angle errors shows a gradually increasing W-shaped distribution along the radial direction, while the scatter is nearly linear. Logically, the statistical model is established based on the linear correlation of the scatter by regression analysis to reduce variable dimension from 13 to 1. The influence of the radial non-uniform twist angle errors' uncertainty on the aerodynamic performance of the three-dimensional compressor rotor is efficiently quantified combining the non-intrusive polynomial chaos method. The results show that the mean values of mass flow rate, total pressure ratio, and isentropic efficiency at the typical operating conditions are lower than the nominal values due to the systematic deviation, indicating that the under-deflection twist angle errors lead to the decrease in compressor thrust. The compressor's stable operating range is more sensitive to the scatter of twist angle errors, which is up to an order of magnitude greater than that of the total pressure ratio and isentropic efficiency, indicating the compressor's safe and stable operation risk increases. Additionally, the flow field at the tip region is significantly affected by twist angle errors, especially at the shock wave position of the near-stall condition. [ABSTRACT FROM AUTHOR]

Every year the volume of information is growing at a high rate; therefore, more modern approaches are required to deal with such issues efficiently. Distributed systems, such as Apache Spark, offer such a modern approach, resulting in more and more machine learning models, being adapted into using distributed logic. In this paper, we propose a classification model, based on Bayesian Networks (BNs), that utilizes the distributed environment of Apache Spark using the Dataframes paradigm. This model can exploit any user-provided directed acyclic graph (DAG) that portrays the dependencies between the features of a dataset to estimate the parameters of the conditional probability distributions associated with each node in the graph to make accurate predictions. Moreover, in contrast with the majority of implementations that are only able to handle discrete features, it is also capable of efficiently handling continuous features by calculating the Gaussian probability density function. [ABSTRACT FROM AUTHOR]

When trading in the call and put contracts of option chains, the portfolios of strikes must be selected. The trader must also decide whether to take long or short positions at the selected strikes. Dynamic strategies for making these decisions are discussed in this paper. On any day, the strategies estimate the drift and volatility parameters of the future probability distribution of the price of the underlying asset. From this distribution, the trader can further estimate the future expected profit and expected loss that may be experienced for any portfolio of strikes of the call and put contracts. Expected profit and expected loss are the reward and risk metrics of such portfolios. An optimal portfolio can then be selected by making the reward as high as possible under the risk tolerance set by the trader. Extensive back-testing applications to historical data of SPY option chains illustrate the effectiveness of these strategies, particularly when dealing with short-term expiry options and when acting as a seller of put and call options. [ABSTRACT FROM AUTHOR]

The multivariate inverse hypergeometric (MIH) distribution is an extension of the negative multinomial (NM) model that accounts for sampling without replacement in a finite population. Even though most studies on longitudinal count data with a specific number of 'failures' occur in a finite setting, the NM model is typically chosen over the more accurate MIH model. This raises the question: How much information is lost when inferring with the approximate NM model instead of the true MIH model? The loss is quantified by a measure called deficiency in statistics. In this paper, asymptotic bounds for the deficiencies between MIH and NM experiments are derived, as well as between MIH and the corresponding multivariate normal experiments with the same mean-covariance structure. The findings are supported by a local approximation for the log-ratio of the MIH and NM probability mass functions, and by Hellinger distance bounds. [ABSTRACT FROM AUTHOR]

In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure/reinforcement theory reduces the positive impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more diffuse (fewer precise) signals in case of negative (positive) information. This results in postearnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents' perceptions converge to the fundamental—even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4941. [ABSTRACT FROM AUTHOR]

The authors of the paper (Vitali et al 2022 J. Phys. A: Math. Theor. 55 224012) analyzed a simple CTRW model with a waiting time distribution defined as the weighted sum of two exponential distributions. They showed that their model meets many paradigmatic features that belong to the anomalous diffusion as it is observed in living systems. This comment point out the previous paper that considers a similar model and improves on the authors' result regarding the time dependence of the mean-square displacement. [ABSTRACT FROM AUTHOR]

This paper generalizes, by the use of compound distributions, a result of HAIGHT (1966) on counts that are delayed but not lost. An application of Markov chains indicates a simple method of numerical evaluation of the distributions involved. Finally a continuous time analogue is studied. [ABSTRACT FROM AUTHOR]

Liseo and Loperfido [A note on reference priors for the scalar skew-normal distribution. J Statist Plann Inference. 2006;136(2):373–389] studied some peculiar features of default Bayes analysis of the scalar skew-normal model. In particular, they showed that, by considering the simplest model with a single unknown parameter λ of skewness, the reference – or Jeffreys’ – prior for this parameter is proper. They proved that tails of Jeffreys’ prior are of orderO(λ−3/2). But they made a mistake in their proof. In this note, we will modify their proof. [ABSTRACT FROM AUTHOR]

Piezoelectric semiconductor (PS) materials have attracted much attention in recent years due to their unique properties. This paper explores the electromechanical coupling behavior of bent piezoelectric semiconductor fibers with non-uniform cross-sectional areas. The study uses the generalized differential quadrature method to numerically solve the field equations with variable coefficients derived from piezoelectric theory. The research examines the mechanical and electrical field distribution of bent variable cross-sectional fibers, comparing the performances of non-uniform fibers with different profiles. The study reveals that the variable cross-sectional profile of the fiber changes the characteristic of the uniform fiber's electrical distribution along the axis, and it exhibits a more sensitive and stronger electrical response to the same external force. The research also shows that the concavity and convexity of the radius distribution function of the non-uniform fibers determine whether there are extreme points of surface potential. Finally, the study suggests that by designing extreme points of the PS fiber profile, surface potential extreme points can be artificially created at the same location. These results offer a theoretical direction for creating advanced piezoelectric semiconductor nanodevices and present novel insights into designing higher-efficiency nanogenerators and mechanical strain sensors in the future. [ABSTRACT FROM AUTHOR]

Background: Although previous studies on the droplet deposition behaviour of rice leaves have modelled the leaves as flat surface structures, their curved surface structures actually have a significant effect on droplet deposition. Results: In this paper, the statistical distribution of the coordinate parameters of rice leaves at the elongation stage was determined, computational fluid dynamics (CFD) simulation models of droplet impact on rice leaves with different curvature radii were built, and the effect of leaf curvature radius on the deposition behaviour and spreading diameter of droplets on rice leaves was studied using validated simulation models. The results showed that the average relative errors of the CFD simulation models were in the range of 2.23–9.63%. When the droplets struck the rice leaves at a speed of 4 m/s, the 50 μm droplets did not bounce within the curvature radii of 25–120 cm, the maximum spreading diameters of 200 and 500 μm droplets that just adhered to the leaves were 287 and 772 μm, respectively. The maximum spreading diameters of 50, 200, and 500 μm droplets that just split were 168, 636, and 1411 μm, respectively. As the curvature radii of the leaves increased, the maximum spreading diameter of the droplets gradually decreased, and droplet bouncing was more likely to occur. However, a special case in which no significant change in the maximum spreading diameter arose when 50 μm droplets hit a leaf with a curvature radius exceeding 50 cm. Conclusion: Splitting generally occurred for large droplets with a small curvature radius and small tilt angle; bouncing generally occurred for large droplets with a large curvature radius and large tilt angle. When the droplet was small, the deposition behaviour was mostly adhesion. The change in spreading diameter after stabilisation was similar to the change in maximum spreading diameter, where the spreading diameter after stabilisation greatly increased after droplet splitting. This paper serves as a reference for the study of pesticide droplet deposition and its application in rice-plant protection. [ABSTRACT FROM AUTHOR]

Scatterometers are dedicated to monitoring sea surface wind vectors, but they also provide valuable data for polar applications. As a new type of scatterometer, the rotating fan beam scatterometer delivers a higher diversity of incidence angles and more azimuth sampling. The paper takes the first rotating fan beam scatterometer, the China France Oceanography Satellite scatterometer (CSCAT), as an example to explore the effectiveness of this new type of scatterometer in polar sea ice detection. In this paper, a Bayesian method with consideration of geometric characteristics of CSCAT is developed for sea ice detection. The implementation of this method includes the definition of CSCAT backscatter space, an estimation of the sea ice Physical Model Function (GMF), a calculation of the sea ice backscatter distance to the sea ice GMF, a probability distribution function (PDF) estimation of the square distance to the GMF (sea ice GMF and wind GMF), and a calculation of the sea ice Bayesian posterior probability. This algorithm was used to generate a daily CSCAT polar sea ice mask during the CSCAT mission period (2019–2022) (by setting a 55% threshold on the Bayesian posterior probability). The sea ice masks were validated against passive microwaves by quantitatively comparing the sea ice edges and extents. The validation suggests that the CSCAT sea ice edge and extent show good agreement with the sea ice concentration distribution (i.e., sea ice concentration ≥ 15%) of the Advanced Microwave Scanning Radiometer 2 (AMSR2). The average Euclidean distance of the sea ice edges was basically less than 12.5 km, and the deviation of the sea ice extents was less than 0.3 × 106 km2. [ABSTRACT FROM AUTHOR]

This paper used dynamic image analysis (DIA) to analyze the grain size and shape data of beach surface sediments in Bao'ding Bay, eastern Hainan Island, China, and explored the effects of sediment transport modes and beach morphology on the grain size–shape trend curves. This paper adopted a method of combining grain size cumulative frequency curves and grain size–shape trend curves to identify three sediment transport modes: suspension, saltation, and traction, and analyzed the characteristics of the grain size–shape trend curves under different beach morphologies (reflective, intermediate, and dissipative). This paper found that the grain size–shape trend curves can effectively indicate the sediment transport modes and improve the accuracy of subpopulation division; the grain size–shape trend curves showed different distribution characteristics in the cross-shore and longshore directions, which are closely related to the beach's morphology and sediment transport direction. This paper provides a new method and idea for studying beach sediment transport and sedimentary environment using sediment grain size and shape data. [ABSTRACT FROM AUTHOR]

Precipitation modeling holds significant importance in various fields such as agriculture, animal husbandry, weather derivatives, hydrology, and risk and disaster preparedness. Stochastic precipitation generators (SPGs) represent a class of statistical models designed to generate synthetic data capable of simulating dry and wet precipitation stretches for a long duration. The construction of Hidden Markov Models (HMMs), which treat latent meteorological circumstances as hidden states, is an efficient technique for simulating precipitation. Considering that there are many choices of emission distributions used to generate positive precipitation, the characteristics of different distributions for simulating positive precipitation have not been fully explored. The paper includes a simulation study that demonstrates how the Pareto distribution, when used as the distribution for generating positive precipitation, addresses the limitations of the exponential and gamma distributions in predicting heavy precipitation events. Additionally, the Pareto distribution offers flexibility through adjustable parameters, making it a promising option for precipitation modeling. We can estimate parameters in HMMs using forward–backward algorithms, Variational Bayes Expectation-Maximization (VBEM), and Stochastic Variational Bayes (SVB). In the Xilingol League, located in the central part of the Inner Mongolia Autonomous Region, China, our study involved data analysis to identify crucial locations demonstrating a robust correlation and notable partial correlation between the Normalized Difference Vegetation Index (NDVI) and annual precipitation. We performed fitting of monthly dry days ratios and monthly precipitation using seasonal precipitation and year-round precipitation data at these crucial locations. Subsequently, we conducted precipitation predictions for the daily, monthly, and annual time frames using the new test dataset observations. The study concludes that the SPG fits the monthly dry-day ratio better for annual daily precipitation data than for seasonal daily precipitation data. The fitting error for the monthly dry day ratio corresponding to annual daily precipitation data is 0.053 (exponential distribution) and 0.066 (Pareto distribution), while for seasonal daily precipitation data, the fitting error is 0.14 (exponential distribution) and 0.15 (Pareto distribution). The exponential distribution exhibits the poorest performance as a model for predicting future precipitation, with average errors of 2.49 (daily precipitation), 40.62 (monthly precipitation), and 130.40 (annual precipitation). On the other hand, the Pareto distribution demonstrates the best overall predictive performance, with average errors of 0.69 (daily precipitation), 34.69 (monthly precipitation), and 66.42 (annual precipitation). The results of this paper can provide decision support for future grazing strategies in the Xilingol League. [ABSTRACT FROM AUTHOR]

Characterizing distributions is the process of finding the unique properties that distinguish a particular distribution from others. There are various methods for doing this, such as using moments, maximum entropy, hazard rate, and so on. Nanda (2010) used failure rate and mean residual life functions to characterizing some distributions. In this paper, we extend the results of Nanda (2010). We characterize the Pareto, exponential, and Rayleigh distributions by refining the main results of that paper. We take some inequalities from that paper and propose a new inequality. Then, we raise both sides of the inequalities to some power and show that equality holds if and only if certain distributions are characterized. [ABSTRACT FROM AUTHOR]

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm was utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization was investigated, and different charging profiles and probability distributions were defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources were introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios. [ABSTRACT FROM AUTHOR]

Soil–water characteristic curve (SWCC) contains the key information related to the hydromechanical properties of unsaturated soil. It is noted that the shapes of the SWCC for the fine-grained soils could be different if the specimens were prepared with different initial void ratios. The pore structure of soil is altered when the specimen experiences mechanical or suction loading. Determination of a group of SWCCs for different specimens through the experimental measurement is time-consuming and costly. In this paper, a new method is proposed to predict the SWCCs for the fine-grained soil with different initial void ratios. The measured SWCC in the form of gravimetric water content (SWCC-w) for the specimen with a high void ratio and the volumetric shrinkage curve is adopted as the input information. The SWCC-w for the specimen with another void ratio is subsequently estimated using the proposed method in this paper. The estimated results from the proposed method in this paper show a good agreement with the experimental data from the published literature. The proposed method provides an alternative method for the prediction of the SWCC for the fine-grained soil with different initial void ratios. [ABSTRACT FROM AUTHOR]

This paper examines the random variation of the residual radius along the length of both naturally corroded and artificially corroded rebars. The rebars are washed to remove rust, and their residual area is measured by the water volume method for every 20-mm long segment of each rebar. Tests are carried out to determine the stationarity or non-stationarity of the stochastic field used to model the residual radius of the specimens. Then, the autocorrelation function, power spectrum and marginal probability distribution function of each specimen are estimated. It is found that the residual radius field of both naturally and artificially corroded rebars with mean corrosion levels below 20% is stationary, while the situation can change as the mean corrosion level increases and concrete cover spalling occurs. The residual radius field along the rebar length of stationary specimens is found to be white noise for both naturally and artificially corroded rebars. The marginal probability distribution function of the residual radius is determined to be skewed and can be fitted with a Beta distribution for naturally corroded rebars and with a Weibull distribution for artificially corroded rebars. This is the first paper studying and comparing both naturally and artificially corroded rebars. [ABSTRACT FROM AUTHOR]

• An original centrality metric objective function for the LMRNP is introduced. • A new data-driven adaptive distributionally robust equilibrium programming method. • The DBSCAN algorithm is newly adopted to construct a data-driven ambiguity set. • The proposed model is theoretically derived into a computationally solvable form. • The performance of the deduced model is validated by two real data sets. This paper studies a variant of the two-stage last mile relief network problem by introducing a centrality metric objective including the distance centrality metric, the demand centrality value-at-risk and the residual supplies centrality metric. Under the scenario-based design, the demand in each scenario is described as a random fuzzy variable whose probability distribution is ambiguous. In order to characterize its ambiguousness, this paper designs a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on sample data to construct a data-driven ambiguity set containing the support and mean, which not only realizes the visualization of the ambiguous probability distribution, but also provides a finite set of scenarios for the uncertain demand. Given the constructed data-driven ambiguity set, this paper proposes a data-driven adaptive distributionally robust equilibrium programming (DDADREP) method for the last mile relief network problem (LMRNP). In view of its computational difficulty, the safe approximation, duality technique and credibility measure theory are used to approximate it as a mixed integer second-order cone programming with significant computational advantages. Finally, the proposed model is validated with two real-world cases, and its effectiveness as well as advantages is demonstrated by comparison with the classical robust optimization and the nominal equilibrium optimization, respectively. [ABSTRACT FROM AUTHOR]

In this work, we investigate spacings based on order statistics obtained from continuous distribution functions. At the beginning of the paper, we present distributional results for spacings and a method of classification of distributions according to their tails. Then we use this method to derive asymptotic results for spacings. By applying some special versions of Borel–Cantelli lemma, we obtain their strong limit results. At the end of the paper, we present some illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most current reinforcement learning method assumes a single-view observation space, and this imposes limitations on the observed data, such as lack of spatial information and occlusions. This makes obtaining ideal observational information from the environment difficult and is a bottleneck for real-world robotics applications. In this paper, we use contrastive learning to train a shared latent space between different viewpoints and show how the Products of Experts approach can be used to integrate and control the probability distributions of latent states for multiple viewpoints. We also propose Multi-View DreamingV2, a variant of Multi-View Dreaming that uses a categorical distribution to model the latent state instead of the Gaussian distribution. Experiments show that the proposed method outperforms simple extensions of existing methods in a realistic robot control task. [ABSTRACT FROM AUTHOR]

Data-driven turbulence modeling has been extensively studied in recent years. To date, only high-fidelity data from the mean flow field have been used for Reynolds-averaged Navier–Stokes (RANS) modeling, while the instantaneous turbulence fields from direct numerical simulation and large eddy simulation simulations have not been utilized. In this paper, a new framework is proposed to augment machine learning RANS modeling with features extracted from instantaneous turbulence flow data. A conditional generative model is trained to model the probability distribution of the local instantaneous turbulence field given local mean flow features. Then, the generative model is transferred to machine learning RANS modeling. The present work is mainly focused on generating a local instantaneous turbulence field using conditional generative adversarial networks (CGANs). Several GANs are trained first on the turbulence data from channel flow and periodic hill flow to generate complete one-dimensional and two-dimensional turbulence fields. Then, a CGAN is trained on the periodic hill flow data to generate local turbulence fields. Statistical analysis is performed on the generated samples from the GAN models. The first and second moments, the two-point correlation, and the energy spectra conform well to those of real turbulence. Finally, the information learned by the CGAN is used for machine learning RANS modeling by multitask learning, and the feasibility of the framework proposed in this paper is initially verified. [ABSTRACT FROM AUTHOR]

Nowadays time series clustering is of great importance in manufacturing industries. Meanwhile, it is considerably challenging to achieve explainable solution as well as significant performance due to computation complexity and variable diversity. To efficaciously handle the difficulty, this paper presents a novel metaheuristic-based time series clustering method which can improve the effectiveness and logicality of existing clustering approaches. The proposed method collects candidate cluster references from hierarchical and partitional clustering through shape-based distance measure as well as dynamic time warping (DTW) on manufacturing time series data. By applying metaheuristics highlighting estimation of distribution algorithms (EDA), such as extended compact genetic algorithm (ECGA), on the collected candidate clusters, advanced cluster centroid combinations with minimal distances can be achieved. ECGA employs the least complicated and the most closely related probabilistic model structure regarding population space during generation cycle. This feature strengthens the comprehension of clustering results in how such optimal solutions were achieved. The proposed method was tested on real-world time series data, open to the public, from manufacturing industry, and showed noticeable performances compared to well-established methods. Accordingly, this paper demonstrates that obtaining both comprehensible result as well as prominent performance is feasible by employing metaheuristic techniques to time series data clustering methods. [ABSTRACT FROM AUTHOR]

Recent studies have shown that modern deep neural network classifiers are easy to fool, assuming that an adversary is able to slightly modify their inputs. Many papers have proposed adversarial attacks, defenses and methods to measure robustness to such adversarial perturbations. However, most commonly considered adversarial examples are based on perturbations in the input space of the neural network that are unlikely to arise naturally. Recently, especially in computer vision, researchers discovered "natural" perturbations, such as rotations, changes of brightness, or more high-level changes, but these perturbations have not yet been systematically used to measure the performance of classifiers. In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them. These metrics, called latent space performance metrics, are based on the ability of generative models to capture probability distributions. On four image classification case studies, we evaluate the proposed metrics for several classifiers, including ones trained in conventional and robust ways. We find that the latent counterparts of adversarial robustness are associated with the accuracy of the classifier rather than its conventional adversarial robustness, but the latter is still reflected on the properties of found latent perturbations. In addition, our novel method of finding latent adversarial perturbations demonstrates that these perturbations are often perceptually small. [ABSTRACT FROM AUTHOR]

Purpose: Product counterfeiting has been ubiquitously observed in various segments of the supply chain. The intrinsic values of brands create more opportunities for counterfeiting. The damaging reputation of such brands leaves them to deal with the fallouts of counterfeits. Hence, such companies address them mainly through legal action, price and quality strategy. However, consumer characteristics and the random distribution of counterfeit products to the consumer types affect the effectiveness of a counter strategy. This paper aims to generate insights on how to leverage digital technology to curb counterfeit entities with consideration of consumer characteristics and the random distribution of counterfeits to them. Design/methodology/approach: The authors used game theory and vertical differentiation model to understand and encounter deceptive counterfeiting of brand products. The study understands the economic relationship between a brand product manufacturer and consumer types based on their awareness. Further, the authors have considered different cases in the model to gain useful insights. Findings: The results reveal that when the consumers are proactive, informed and value-conscious brand product manufacturers take digital technology counterstrategy to earn the maximum revenue. Hence, this analysis highlights that the effectiveness of a counterstrategy critically depends on the consumer characteristics, whether they are proactive, informed or unaware. Practical implications: The study outlines that brand product manufacturers must emphasize on the digital supply chain, product redesign and product tracking facility to empower informed and value-conscious and proactive consumers. Moreover, the government should take steps to create awareness among uninformed consumers via information campaigns. Originality/value: This paper incorporates the role of consumers and brand product manufacturers to understand and address the deceptive counterfeiting issue. [ABSTRACT FROM AUTHOR]

Laplace probability density function with additional shape parameter that regulates the degree of skewness is a skew Laplace distribution. The various forms of skew Laplace distribution are found in the literature, the distributions defined by Mc Gill (1962), Holla and Bhattacharya (1968), Lingappaiah (1988), Fernandez and Steel (1998). The skew log Laplace distribution is the probability distribution of a random variable whose logarithm follows a skew Laplace distribution. In this paper, the classical optimum tests for skewness parameter of skew log Laplace distribution (SLLD) derived from Lingappaiah (1988) distribution are discussed. Uniformly most powerful test, uniformly most powerful unbiased test and Wald's sequential probability ratio test for skewness parameter are compared. The exact likelihood ratio test and Neyman structure test for testing skewness parameter when scale parameter is known are derived. Finally, the underreported income of Road Transport Company is analysed on the basis of the tests derived in this paper. [ABSTRACT FROM AUTHOR]

A major puzzle concerning the wide stellar binaries (semimajor axes a ≳ 103 au) in the Solar neighbourhood is the origin of their observed superthermal eccentricity distribution function (DF), which is well approximated by P (e) ∝ e α with α ≈ 1.3. This DF evolves under the combined influence of (i) tidal torques from the Galactic disc and (ii) scattering by passing stars, molecular clouds, and substructure. Recently, it was demonstrated that Galactic tides alone cannot produce a superthermal eccentricity DF from an initially isotropic, non-superthermal one, under the restrictive assumptions that the eccentricity DF was initially of power-law form and then was rapidly phase-mixed toward a steady state by the tidal perturbation. In this paper, we first prove analytically that this conclusion is valid at all times, regardless of these assumptions. We then adopt a thin Galactic disc model and numerically integrate the equations of motion for several ensembles of tidally perturbed wide binaries to study the time evolution in detail. We find that even non-power-law DFs can be described by an effective power-law index α eff which accurately characterizes both their initial and final states, and that a DF with initial (effective or exact) power-law index α i is transformed by Galactic tides into another power law with index α f ≈ (1 + α i)/2 on a time-scale |$\sim 4\, \mathrm{Gyr}\, (a/10^4\mathrm{AU})^{-3/2}$|⁠. In a companion paper, we investigate separately the effect of stellar scattering. As the GAIA data continues to improve, these results will place strong constraints on wide binary formation channels. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) networks support ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). The coexistence of URLLC and eMBB is often organized by non-orthogonal multiple access (NOMA), giving priority to URLLC and resulting in eMBB quality of service (QoS) degradation. In this paper, we address the issue of joint URLLC and eMBB transmission, focusing on the problem from the perspective of delay-tolerant eMBB. Due to the priority given to URLLC, we assume that an eMBB session may be interrupted if there are no free resources available for URLLC or delayed when a new eMBB session arrives. To make the scheme more flexible, we propose that interrupted and delayed eMBB sessions periodically check for free resources, rather than continuously. To analyze this scenario, we propose a retrial queuing system with two retrial buffers (orbits) for interrupted and delayed eMBB sessions. The stationary probability distribution, provided in matrix form by recursive formulas, is presented. The paper concludes with a numerical example showing that the scheme with two buffers, compared to one buffer, practically doubles the average number of active eMBB sessions while keeping the interruption probability below 0.001. We provide an illustration of the configuration of eMBB retrial rates to meet its QoS requirements. [ABSTRACT FROM AUTHOR]

This paper explores the concept of autonomous decision rules of Industry 4.0. The goal is to model and simulate a production system in which products autonomously choose the resource in which they will be processed based on shortest completion time (shortest queue length). For validation and benchmark, a production system previously presented in the literature is modeled. The performance of the autonomous model is evaluated in comparison to a model with dedicated lines for each product. In the previous work considered, a specific interarrival function and only deterministic parameters for the processing times are used, with no consideration of setup times. After validation, the proposed models are simulated with other parameters (different probability distributions for interarrival and processing times) and are extended to include machine setups, according to different rules. Reductions in total throughput times and their variability were observed for the systems with autonomous products, when compared to those with dedicated lines, which indicates greater responsiveness and stability of the shop floor. An optimization model for scheduling was also proposed and applied to the same system and input data of the simulation of autonomous products, for comparison. The results showed that the proposed decision rule based on the queues' length, used in the simulation, can yield solutions of good quality, close to the optimal solution. This paper contributes to extend and generalize the results of the literature and the discussion about variability seeks to motivate organizations to implement systems with autonomous products. [ABSTRACT FROM AUTHOR]

This study is anchored in the H2020 SEAMLESS project (www.seamlessproject.org), which aims to develop ensemble assimilation methods to be implemented in Copernicus Marine Service monitoring and forecasting systems, in order to operationally estimate a set of targeted ecosystem indicators in various regions, including uncertainty estimates. In this paper, a simplified approach is introduced to perform a 4D (space-time) ensemble analysis describing the evolution of the ocean ecosystem. An example application is provided, which covers a limited time period in a limited subregion of the North Atlantic (between 31°W and 21°W, between 44°N and 50.5°N, between March 15 and June 15, 2019, at a 1/4° and a 1 day resolution). The ensemble analysis is based on prior ensemble statistics from a stochastic NEMO/PISCES simulator. Ocean colour observations are used as constraints to condition the 4D prior probability distribution. As compared to classic data assimilation, the simplification comes from the decoupling between the forward simulation using the complex modelling system and the update of the 4D ensemble to account for the observation constraint. The shortcomings and possible advantages of this approach for biogeochemical applications are discussed in the paper. The results show that it is possible to produce a multivariate ensemble analysis continuous in time and consistent with the observations. Furthermore, we study how the method can be used to extrapolate analyses calculated from past observations into the future. The resulting 4D ensemble statistical forecast is shown to contain valuable information about the evolution of the ecosystem for a few days after the last observation. However, as a result of the short decorrelation time scale in the prior ensemble, the spread of the ensemble forecast is increasing quickly with time. Throughout the paper, a special emphasis is given to discussing the statistical reliability of the solution. Two different methods have been applied to perform this 4D statistical analysis and forecast: the analysis step of the Ensemble Transform Kalman Filter (with domain localization) and a Monte Carlo Markov Chain (MCMC) sampler (with covariance localization), both enhanced by the application of anamorphosis to the original variables. Despite being very different, the two algorithms produce very similar results, thus somehow validating each other. As shown in the paper, the decoupling of the statistical analysis from the dynamical model allows us to restrict the analysis to a few selected variables and, at the same time, to produce estimates of additional ecological indicators (in our example: phenology, trophic efficiency, downward flux of particulate organic matter). This approach can easily be appended to existing operational systems to focus on dedicated users' requirements, at small additional cost, as long as a reliable prior ensemble simulation is available. It can also serve as a baseline to compare with the dynamical ensemble forecast, and as a possible substitute whenever useful. [ABSTRACT FROM AUTHOR]

This paper examines a twofold tension in Gerhard Schurz's (2019) recent proposal to solve Hume's problem of induction. Schurz concedes to the skeptic that there is no non-circular epistemic justification of the reliability of induction, but then argues for the optimality of meta-induction so that if any prediction method is reliable, then meta-induction is. There is a tension in this proposal between meta-induction and our inductive practice: Are we supposed to abandon our inductive practice in favor of meta-induction? Schurz claims that given the actual way the world has been, the meta-inductive method supports the standard inductive method. There then arises a second tension: The challenges Schurz cites against the reliability of induction—such as the anti-inductive hypothesis and the state-uniform probability distribution—cast doubt on Schurz's claim that meta-induction supports induction. The reasoning in this paper moves in the opposite direction: There are ways of answering these challenges in defense of Schurz's claim that meta-induction supports induction, but these ways also point to ways of defending induction directly without the mediation of meta-induction. [ABSTRACT FROM AUTHOR]

The adequate choice of a distribution that can fit a dataset, especially to its right tail (large extreme events), is a major problem in flood frequency analysis. Decision support systems (DSS) have been used in the past to define the appropriate class of distribution based on the tail behaviour of the data before its model selection. This paper investigates the tail behaviour of probability distribution of the daily streamflow data in south Indian rivers and also assesses the information related to tail risk, as it has many practical and societal consequences. In this paper, we apply and compare two DSS, (i) given by Martel et al. (J Hydrol Eng 18(1):1–9, 2013) and (ii) concentration profile–concentration adjusted expected shortfall (CP–CAES) based DSS, along with some newly developed graphical diagnostic tools, such as CP, CAES, discriminant moment ratio plot, maximum-to-sum plot, and Zenga plot to characterize the tails of probability distributions into an appropriate class. Further, the tail risk is analyzed using a novel risk management approach known as a concentration map (CM), which makes use of the concentration profiles of daily streamflow datasets. Results indicate that the proposed DSS is a potential tool for tail characterization. The study suggests the use of heavy-tailed distributions to model daily streamflow data over south Indian catchments. Neglecting heavy-tailed distributions, when found appropriate, can lead to an underestimation of the likelihood of floods and has catastrophic consequences for risk. CM is found suitable for assessing the tail risk associated with the daily streamflow dataset, which inherently represents the frequency and magnitude of extreme floods. [ABSTRACT FROM AUTHOR]

Background: To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D‐printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. Purpose: To present the design process of an anthropomorphic 3D‐printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X‐ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. Methods: Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3. This DICOM dataset was printed on paper using iodinated ink. The phantom was CT‐scanned and compared to the original image dataset used for printing the phantom. The water‐equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual‐energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. Results: Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X‐ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid‐frequencies (e.g., for ω = 0.3942 mm−1, |f(ω)|original = 0.09 * |fmax|original and |f(ω)|phantom = 0.12 * |fmax|phantom). Conclusions: 3D‐iodine‐ink‐printing technology can be used to print anthropomorphic phantoms with a water‐equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual‐energy CT analyses. [ABSTRACT FROM AUTHOR]