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This document provides a commentary on a paper titled "Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks." The paper summarizes the current state of the field and proposes extensions to marked linear network point processes. It focuses on reviewing summary statistics for point processes and discusses other important topics in the field, such as marked temporal point processes and the Growth-Interaction process. The document explains different mark spaces, summary statistics, and product density/intensity functions for marked point processes. It also discusses concepts like mark filtering, marked intensity reweighted stationarity, kth-order intensity reweighted stationarity, and mark-weighted kth-order summary statistics. The text explores the use of test functions and weighted local summary statistics, as well as linear network summary statistics and marked inhomogeneous summary statistics. The authors propose proper estimators for these summary statistics and discuss their applications. [Extracted from the article]

Paper-type gas diffusion layer (GDL) plays the role of structural support, mass transport, electron and heat transfer in the fuel cell. The deformation and fracture behavior of paper-type GDL directly determines its physical characteristics. In this paper, we obtained the distribution function of fiber length among intersections using statistical analysis. Based on the bending theory and fracture analysis, the deformation model and the maximum allowable pressure criterion of paper-type GDL are established. The deformation modulus is related to the modulus and the initial volume fraction of carbon fiber, with an ideal value of 1.4 MPa. The tensile fracture is the main form of fiber fracture. The maximum allowable pressure is related to the expected fracture probability, the strength and the initial volume fraction of carbon fiber. The residual strain increases and the deformation modulus decreases with load range, which means that the strength of the paper-type GDL is weakened when residual strain occurs. [Display omitted] • The fiber length between intersections obeys exponential distribution function. • The compression deformation model of paper-type GDL is established. • The maximum allowable pressure criterion for the paper-type GDL is obtained. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor addressing a typographical error in the formulas for the double Pareto-lognormal distribution and the normal-Laplace distribution. The authors clarify that the formulas themselves are correct, but there was an error in the expression of the Mills ratio in the correction by Amini and Rabbani. The authors provide the correct formulas for both distributions and offer further explanation upon request. The authors are affiliated with Juraj Dobrila University of Pula in Croatia. [Extracted from the article]

Thermal energy storage and utilization has been widely concerned due to the intermittency, renewability, and economy of renewable energy. In this paper, the potential energy function of binary Na2CO3–K2CO3 salt was first constructed using the Deep Potential GENerator (DPGEN) enhanced sampling method. Deep potential molecular dynamics simulations were performed to calculate the thermal properties and structural evolution of binary carbonates. The results show that as the temperature increases from 1073 to 1273 K, the viscosity and thermal conductivity decrease from 5.011 mPa s and 0.502 W/(m K) to 2.526 mPa s and 0.481 W/(m K), respectively. The decrease in viscosity is related to the distance and interaction between the molten salt ions. In addition, the diffusion coefficients, energy barriers, ionic radius, angular distribution function, and coordination number of molten salt were calculated and analyzed. The CO32− exhibits a stable planar triangular structure. The ionic radius of Na+ is smaller than that of K+, which makes Na+ suffer less spatial hindrance during motion and has a higher diffusion coefficient. The energy barriers that Na+ needs to overcome to escape the Coulomb force is greater than that of K+ ions, so molten salt containing Na+ may possess greater heat storage potential. We believe that the potential function constructed with DPGEN enhanced sampling strategy can provide more convincing results for predicting the thermal properties of molten salts. This paper aims to provide a technical route to develop the novel complex molten salt phase change material for thermal energy storage. [ABSTRACT FROM AUTHOR]

Bubbles, as typical defects of oil–paper insulation systems, pose a great threat to the insulation state. In this article, the characteristics and mechanisms of the partial discharge (PD) inception, evolution, and induced breakdown of bubbles embedded between oil-impregnated paper (OIP) layers are investigated. The results show that the PD inception has a significant statistical time delay due to the lack of free electrons. The PD inception time delay (PDTD) follows an exponential distribution and leads to the large dispersion of the PD inception voltage (PDIV), which follows a Weibull distribution. The changes in PD behavior [charge, repetition rate, pulse current waveform, and phase-resolved PD (PRPD)] over time all show two separable stages (stages 1 and 2). The continuous recording of bubble area and gas composition under PD reveals that the evolution of bubble PD is attributed to the change in gas composition rather than the change in surface conditions as believed by researchers. The OIP breakdown induced by bubble PD indicates two breakdown modes, fast and slow, which correspond to direct-induced breakdown in stage 1 and cumulative-damage breakdown in stage 2. The former occurs because of the severe distortion of the electric field by the space charge at the head of the discharge channel inside the bubble, whereas the latter occurs because of the gradual deterioration of the OIP due to the continuous PD action. This study increases our understanding of the mechanisms of bubble discharge and of oil–paper insulation deterioration, and it provides guidance for fault diagnoses. [ABSTRACT FROM AUTHOR]

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. [ABSTRACT FROM AUTHOR]

Understanding and simulating the evolution of fracture in cellulose-based paper materials is vital for many eco-sustainable applications of packaging solutions. In this work, we conduct an integrated experimental-statistical mechanics analysis to elucidate how the microstructural mechanisms govern the fracture behavior of paper. We employ in-situ tensile tests combined with confocal microscopy to observe and analyze key microstructural phenomena, such as fibers' activation and recruitment, and progressive tensile failure of unnotched paper samples under uniaxal loading. The developed stochastic mechanics critical cross-section model to treat the cellulose fiber network is able to decouple the stochastic morphological parameters from the constitutive model of the fibers. Notably, it robustly predicts the anisotropic properties of paper materials in the machine direction (MD) and cross-machine direction (CD) as a direct consequence of the different statistical distribution of fibers' orientation, for the same fiber constitutive model. The proposed approach is finally employed to get an insight into the role of the main morphological parameters, local/non-local fibers' strain redistribution, and finite elasticity of fibers on the whole mechanical behavior of paper. [Display omitted] • Stochastic network model of a cellulose fiber network. • Mechanical simulation of paper material till fibers' failure. • In-situ micro-mechanical testing of paper. • The model decouples the effect of fibers' morphology from their constitutive law. [ABSTRACT FROM AUTHOR]

This paper presents the design of a single-phase solid phononic crystal (PnC) structure featuring a regular hexagonal perforation pattern. The structure manifests three negative refraction bands, encompassing one for transverse waves and two for longitudinal waves, thereby enabling simultaneous control of shear and longitudinal waves. Due to the high symmetry of the triangular lattice, the equal frequency curves corresponding to the negative refraction band approach circular shapes, suggesting a nearly isotropic negative refraction effect. This negative refraction effect is achieved through specific mass resonance modes closely related to the porous structure designed in this paper. Initially, we analyze the band structure of the PnC, followed by designing the PnC plate structure to achieve negative refraction control for transverse waves at a frequency of 32.4 kHz, with a negative refraction index of −1. Additionally, negative refraction control for longitudinal waves is attained at frequencies of 44 and 64.54 kHz. Subsequently, we scrutinize the influence of various conditions on negative refraction, including different structural parameters, incident angles, and operating frequencies, while verifying the robustness of the designed phonon crystal structure. Leveraging the negative refraction characteristics of the structure, we construct an elastic wave lens to achieve perfect imaging of shear and longitudinal waves. Finally, employing finite element simulation and analyzing focusing imaging characteristics with different source positions, we validate that the results closely align with theoretical expectations. The solid PnC structure designed in this study holds significant potential for applications in the fields of elastic wave imaging. [ABSTRACT FROM AUTHOR]

The superadiabatic dynamical density functional theory (superadiabatic-DDFT) is a promising new method for the study of colloidal systems out-of-equilibrium. Within this approach, the viscous forces arising from interparticle interactions are accounted for in a natural way by explicitly treating the dynamics of the two-body correlations. For bulk systems subject to spatially homogeneous shear, we use the superadiabatic-DDFT framework to calculate the steady-state pair distribution function and the corresponding viscosity for low values of the shear-rate. We then consider a variant of the central approximation underlying this superadiabatic theory and obtain an inhomogeneous generalization of a rheological bulk theory due to Russel and Gast. This paper thus establishes for the first time a connection between DDFT approaches, formulated to treat inhomogeneous systems, and existing work addressing nonequilibrium microstructure and rheology in bulk colloidal suspensions. [ABSTRACT FROM AUTHOR]

Voyage charterparty arrangement (VCA) for river shipping is a common agreement between a shipowner and a charterer regarding the transportation of specific cargoes from one port to another via a tramp vessel. Optimizing VCA with the essential terms such as laytime and sailing speed of the vessel, is a crucial task for both parties to reduce potential disputes and achieve win-win outcomes. This paper models the problem from two perspectives: the VCA negotiation stage and execution stage. Firstly, according to the given probability distribution of port time, the theoretically optimal laytime is obtained by using textbook formulation of newsvendor problems (NVP). On this basis, considering the concerns about the inaccuracy of the given probability distribution and the adjustment measures taken by charterers and shipowners in reality, an integrated adjustment model is constructed to determine the optimal laytime in the VCA negotiation stage. Secondly, taking into account the vessel's uncertain lock crossing time, an operations coordination model with information sharing of the berth plan is established to obtain the optimal sailing speed under a certain confidence level in the VCA execution stage. Finally, a case study of chartering tramp vessels on the Yangtze River validates the applicability and effectiveness of the models proposed in this paper, and some important management insights are derived. The results show that establishing lower optimal speed limits and relatively shorter laytime when signing a contract with the shipowner of green river vessels is reasonable for the charterer. By notifying the shipowner of the berth plan after the green river vessel crosses through the lock, the charterer can avoid higher demurrage costs due to speeding-up. Operations coordination would be mutually beneficial without abusing the contract terms. Compared to traditional river vessels, it's more reasonable to reduce the benefits allocated to charterers from the deceleration of green river vessels by shipowners. • Optimizing voyage charterparty arrangement (VCA) for river tramp shipping. • Combining with judgmental adjustments in the optimal laytime model. • Establishing an operations coordination model by considering streamflow and uncertain lock crossing time. • Providing differentiated VCA strategies for green and traditional river vessels. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Accurate segmentation of gliomas is crucial for diagnosis, treatment planning, and evaluating therapeutic efficacy. Physicians typically analyze and delineate tumor regions in brain magnetic resonance imaging (MRI) images based on personal experience, which is often time‐consuming and subject to individual interpretation. Despite advancements in deep learning technology for image segmentation, current techniques still face challenges in clearly defining tumor boundary contours and enhancing segmentation accuracy.To address these issues, this paper proposes a conditional diffusion network (SF‐Diff) with a spatial channel attention infusion (SCAI) module and a frequency spatial attention (FSA) mechanism to achieve accurate segmentation of the whole tumor (WT) region in brain tumors.SF‐Diff initially extracts multiscale information from multimodal MRI images and subsequently employs a diffusion model to restore boundaries and details, thereby enabling accurate brain tumor segmentation (BraTS). Specifically, a SCAI module is developed to capture multiscale information within and between encoder layers. A dual‐channel upsampling block (DUB) is designed to assist in detail recovery during upsampling. A FSA mechanism is introduced to better match the conditional features with the diffusion probability distribution information. Furthermore, a cross‐model loss function has been implemented to supervise the feature extraction of the conditional model and the noise distribution of the diffusion model.The dataset used in this paper is publicly available and includes 369 patient cases from the Multimodal BraTS Challenge 2020 (BraTS2020). The conducted experiments on BraTS2020 demonstrate that SF‐Diff performs better than other state‐of‐the‐art models. The method achieved a Dice score of 91.87%, a Hausdorff 95 of 5.47 mm, an IoU of 84.96%, a sensitivity of 92.29%, and a specificity of 99.95% on BraTS2020.The proposed SF‐Diff performs well in identifying the WT region of the brain tumors compared to other state‐of‐the‐art models, especially in terms of boundary contours and non‐contiguous lesion regions, which is clinically significant. In the future, we will further develop this method for brain tumor three‐class segmentation task. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]

The construction industry is characterized by its fragmented nature, which introduces numerous uncertainties and challenges for project management. Analyzing project impact factors is crucial for federal agencies to mitigate risks, make informed decisions, and ensure successful project outcomes. Although many previous studies explored critical project impact factors for various project types, this paper's primary objective is to identify and categorize the impact factors critical to projects' success in federal facilities. Additionally, the study ranked impact factors based on their significance and relevance to project management teams in federal facilities. The study surveyed federal agencies to identify the top-ranking factors within and across categories based on their impact. The research methodology encompassed a thorough literature review, data mapping, survey development, and analysis to identify, classify, and rank impact factors for federal projects. Spearman's rank-order correlations were computed for correlations between the impact factor categories. The impact factors were ranked based on their relevance and importance for the federal facilities based on frequency distributions of survey results—aligning with the data mapping and correlations exercises. The study's findings indicate that the top five factors with a maximum negative impact on the federal capital facility project belong to the early project life cycle phases, classified within the Governance and Stakeholders and Planning and Execution categories. The paper's findings aim to emphasize the optimization of stakeholder engagement and strategic planning to address critical risks for federal capital facilities projects early in the project life cycle—particularly the detailed engineering and procurement stages. The study's findings contribute to a better understanding of project management in the federal sector and offer beneficial guidance for future project planning and execution. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is acknowledged as a beneficial instrument for identifying and mitigating system failures. However, the traditional FMEA method has its limitations. For instance, crisp numbers fail to adequately represent the intricate information and cognitive nuances of experts. Additionally, the conventional approach overlooks the significance of weights assigned to FMEA experts and risk factors (RFs). Furthermore, the simplistic ranking of failure modes in traditional FMEA does not accurately reflect priorities. In light of these drawbacks, this paper introduces an innovative, fully data‐driven FMEA method, leveraging a probabilistic uncertain linguistic term sets (PULTSs) environment and the Weighted Aggregates Sum Product Assessment (WASPAS) method. In the assessment process, PULTSs serve as linguistic tools that express probability distribution, allowing for a more reasonable and precise description of information. To address the issue of weights for RFs, the regret theory and Modified CRITIC method are employed. Subsequently, the WASPAS method is applied to determine the risk rankings of failure modes. To illustrate the feasibility and rationality of this novel FMEA model, the paper includes an example involving the production of Lithium‐ion batteries. To emphasize the excellence of the proposed FMEA model, sensitivity and comparative analyses are carried out. [ABSTRACT FROM AUTHOR]

This paper introduces a new statistical distribution called length biased quasi suja distribution (LBQS). It explores its properties, including moments, moment generating function(MGF), characteristic function(CF), harmonic mean, reliability, hazard rate and reverse hazard rate. Order statistics of the above distribution is obtained. Furthermore, the paper also examines various entropy which measures the randomness of system, like Renyi entropy and Tsalli's entropy. It also evaluates Bonferroni and Lorenz curves which are useful in measuring the inequality. It also discusses parameter estimation techniques specifically maximum likelihood estimation and likelihood ratio testing. Moreover, a simulation study has been conducted to demonstrate how well the distribution would perform in real-life situation. The validity of the distribution is also demonstrated with real-world data example of failure data, highlighting its potential for practical applications in data analysis. [ABSTRACT FROM AUTHOR]

The trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship. Several ship maneuverability mathematical models and each one with a particular hydrodynamic coefficient approximation using towing tank tests are existed. However, it is presented difficult to directly inverse estimate the hydrodynamic maneuvering coefficients of a ship maneuverability mathematical model from operational data consisting of ship trajectory and maneuvering operation records. This paper proposed a method for estimating the hydrodynamic maneuvering coefficients of the MMG 3DOF model using three types of time-series ship motions (surge, sway, and yaw velocity) as observed data. In the assumption of this paper, there is uncertainty in observations and the hydrodynamic maneuvering coefficients of the MMG 3DOF model. The proposed method outputs samples of the simultaneous posterior probability distribution of the hydrodynamic maneuvering coefficients by the MCMC method using the observed data and stochastic model. A robust trajectory with a wide range can be presented by conducting ship maneuvering simulations using these samples. To verify the feasibility of the proposed method, this paper conducted observation system simulation experiments (OSSE) using the KVLCC2 L7 model and applied the proposed method to several free-running model ship tests. Results showed that on the assumption that MMG 3DOF model can explain the ship's state and trajectory in real world, the proposed method can estimate the ship hydrodynamic maneuvering coefficients of the MMG 3DOF model corresponding to the observed ship trajectory and control data including the error of observed data. [ABSTRACT FROM AUTHOR]

This paper investigates the optimization of the inner thermal layout in nanofluid-filled horizontal annular pipes under natural convection conditions. Two-dimensional models of annular pipes are established using a numerical simulation approach. Then, using this as the simulation tool, the layouts of single-, triple-, and quadruple-heat-source annular pipes are optimized. The coordinates of the inner cylinders are defined as the decision variables, and the average Nusselt number (Nu) on the cold wall surface as the objective function. For the single-cylinder model, both the Bayesian optimization algorithm (BOA) and the genetic algorithm (GA) derive the same results: an axisymmetric layout where the single heat source is positioned slightly above the axis of the annulus. However, the BOA takes much less computational time than the GA and, consequently, is chosen for cases with more complex geometry. The optimization layout of the three-cylinder model also shows an axisymmetric distribution, while the result for the four-cylinder model presents a centrally symmetric distribution. Compared to the original average Nu, the optimized ones are enhanced by 17.83%, 8.36%, and 6.18% for single-, triple-, and quadruple-heat-source annular pipes, respectively. The results of this study can be used for guiding the layout design and optimization of the nanofluid-filled exchangers with multi-inner heat sources. [ABSTRACT FROM AUTHOR]

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system. [ABSTRACT FROM AUTHOR]

This paper deals with the probabilistic analysis of a class of compartmental models formulated via a system of linear differential equations with time‐dependent non‐homogeneous terms. For the sake of generality, we assume that initial conditions and rates between compartments are random variables with arbitrary distributions while the source terms defining the flows entering the compartments are stochastic processes. We then take extensive advantage of the so‐called random variable transformation (RVT) technique to determine the first probability distribution of the solution of such a randomized model under very general hypotheses. In the simplest but relevant case of time‐independent source terms, we also obtain the probability distribution of the equilibrium, which is a random vector. Furthermore, we first particularize the aforementioned results for different types of integrable source terms that permit obtaining an explicit solution of the compartmental model: random constants, a train of Dirac delta impulses with random intensities, and a Wiener process. Secondly, we show an alternative approach based on the Liouville–Gibbs equation, which is useful when dealing with source terms that do not admit a closed‐form primitive. All the previous theoretical results are first illustrated through several numerical examples and simulations where a wide range of different probability distributions are assumed for the model parameters. The paper concludes by applying a compartmental model that describes the dynamics of oral drug administration through multiple chronologically spaced doses using synthetic data generated according to pharmacological references. [ABSTRACT FROM AUTHOR]

Highlights This paper presents a comprehensive study on anisotropic heat transmission behaviors in quartz fiber fabrics. Considering the random distribution characteristic of fibers within the yarn, an innovative two‐scale finite element method (tFEM) is introduced, incorporating multiple micro‐scale fiber models (MMFM) based on scanning electron microscope (SEM) observations. MMFM are divided into 8 distinct models to capture intricate fiber arrangements. The macro‐scale fabric model is constructed based on the geometric structure analysis by Micro‐CT technology. Both micro‐ and macro‐scale models are assembled with the air matrix to form the two‐phase composite model. The Hot‐Disk thermal analysis instrument is applied to measure the anisotropic thermal conductivity (ATC). Numerical results from MMFM shows more excellent agreements with the experimental ones at 3D orthogonal directions of fabrics, i.e., the error rates of the thermal conductivity in the warp, weft and thickness directions between numerical and experimental methods are all less than 3%, which indicates the tFEM including MMFM in this paper is more accurate than the tFEM including OSMFM. In addition, the temperature distribution and heat transmission differences due to fiber arrangements are simulated and illustrated through the MMFM. Afterwards, temperature drop and isothermal characteristics are demonstrated in this study. Effects of fiber arrangements on steady heat transfer behaviors at micro‐scale are illustrated. Isothermal and temperature‐drop characteristics at micro‐ and macro‐scale are simulated and studied. The transverse isotropy of thermal conductivity at micro‐scale and the anisotropy of that at macro‐scale are revealed. [ABSTRACT FROM AUTHOR]

Accurate aircraft turnaround time prediction is an important way to coordinate the operation time of airport ground service and improve the efficiency of airport operation. In this paper, by analyzing the aircraft turnaround operation process, a description model based on Time Transition Petri Net is proposed. The model describes the flight turnaround operation process and the logical relationship of the operation. According to the model, a dynamic prediction method of turnaround time based on Bayesian theorem is designed. According to the actual landing time of the flight, the aircraft turnaround time is predicted. The specific method is to obtain the prior probability distribution and joint distribution law of each operation link according to the flight history data, and use Shapiro-Wilke to test the prior probability distribution of each link. Based on the analysis and comparison between the actual turnaround data of a large airport in China and the forecast data proposed in this paper, the root-mean-square error 3.75 minutes and the mean absolute error 3.40 minutes can be calculated. This paper contributes to the improvement of flight punctuality rate and airport clearance level. [ABSTRACT FROM AUTHOR]

As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method. [ABSTRACT FROM AUTHOR]

In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and L r and L 0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D , r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution. [ABSTRACT FROM AUTHOR]

This paper is concerned with the modeling of complex living systems composed of active particles able to interact with each other and with the surrounding environment. Specifically the discrete thermostatted kinetic theory for active particles is generalized by extending the role of nonconservative interactions and by introducing the role of mutations. In particular the classical proliferation/destruction operator is generalized in order to take into consideration the role of nonlinear interactions, namely the dependence on the distribution functions and their moments. The natural birth and death events are recovered included the logistic growth rate. Finally the role of the outer environment is taken into consideration by introducing microscopic external agents. New thermostat operators are presented whose structures augment the degree of nonlinearity of the evolution equations. The different Cauchy problems are established and future research perspectives are discussed in the whole paper. [ABSTRACT FROM AUTHOR]

The total gamma values and the three radioactive mineral values recorded by sophisticated tool where the correlation between them was measured. Grain size analyses were performed using the Sieve Shaker to calculate the mass retained, the mean grain size and the sediment type for each of the rock extracted. The use of RS-332 SGR equipment revealed the cross plotting radioactive minerals. This paper highlights the use of Spectral Gamma Ray (SGR) RS-332 tools to analyze rock lithology, grain sizes and the geological analysis of the outcrop. In addition, the cumulative and probability distribution of Brunei region were computed to describe the grain size distribution and sediment type. Moreover, fine sand sediment type concluded that more of the rock samples collected were montmorillonite. The main objective of this paper is to study the spatial distribution pattern of the grain size and their correlation to naturally occurring radioactive minerals which include potassium, uranium, and thorium., Furthermore, python software is used to create spatial variability by interpolation, choosing kriging method for reliable variability results. [ABSTRACT FROM AUTHOR]

The L-fractional derivative is defined as a certain normalization of the well-known Caputo derivative, so alternative properties hold: smoothness and finite slope at the origin for the solution, velocity units for the vector field, and a differential form associated to the system. We develop a theory of this fractional derivative as follows. We prove a fundamental theorem of calculus. We deal with linear systems of autonomous homogeneous parts, which correspond to Caputo linear equations of non-autonomous homogeneous parts. The associated L-fractional integral operator, which is closely related to the beta function and the beta probability distribution, and the estimates for its norm in the Banach space of continuous functions play a key role in the development. The explicit solution is built by means of Picard's iterations from a Mittag–Leffler-type function that mimics the standard exponential function. In the second part of the paper, we address autonomous linear equations of sequential type. We start with sequential order two and then move to arbitrary order by dealing with a power series. The classical theory of linear ordinary differential equations with constant coefficients is generalized, and we establish an analog of the method of undetermined coefficients. The last part of the paper is concerned with sequential linear equations of analytic coefficients and order two. [ABSTRACT FROM AUTHOR]

The estimation of the slope (b -value) of the frequency–magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature, this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b -value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude data set. In this work, we provide the complete theoretical formulation including (i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; (ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions and (iii) the corresponding formulas for the parameter b. We deduce (iv) the standard 1 σ intervals for the estimated b. Moreover, we are able (v) to quantify the error associated with the Utsu minimum-magnitude correction. Furthermore, we have discussed the formulas to produce statistically independent magnitude differences. We tested extensively the b -value estimators on simulated synthetic data sets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next. We have also analysed the real aftershock sequence of the 30/10/2016 Norcia (central Italy) to integrate the finding of the simulations. To judge the performance of the various estimators we have introduced an index p that can be seen as a non-parametric extension of the Student's t index. The main outcomes of this paper are that (1) the b -value estimators devised for continuous magnitude data are not adequate for binned magnitudes, (2) for complete data sets, estimators based on magnitudes and on magnitude differences provide substantially equivalent results, (3) for incomplete magnitude data sets, estimators based on magnitude differences provide better results and (4) for incomplete aftershock sequences there is no evidence that methods based on positive magnitude differences are superior than other methods using differences. This conclusion is further confirmed by our analysis of the above-mentioned Norcia seismic sequence. This last finding contrasts with the van der Elst's claim that the so called |${{b}_ + }$| method is the most adequate to treat real aftershock sequences. [ABSTRACT FROM AUTHOR]

This study proposes a new five-parameter distribution called the gamma-exponentiated Weibull–Poisson (GEWP) distribution. As an extension of the exponentiated Weibull–Poisson family, the GEWP distribution offers a more flexible tool for analyzing a wider variety of data due to its theoretically and practically advantageous properties. It encompasses established distributions like the exponential, Weibull, and exponentiated Weibull. The development of the GEWP distribution proposed in this paper is obtained by combining the gamma–exponentiated Weibull (GEW) and the exponentiated Weibull–Poisson (EWP) distributions. Therefore, it serves as an extension of both the GEW and EWP distributions. This makes the GEWP a viable alternative for describing the variability of occurrences, enabling analysis in situations where GEW and EWP may be limited. This paper analyzes the probability distribution functions and provides the survival and hazard rate functions, the sub-models, the moments, the quantiles, and the maximum likelihood estimation of the GEWP distribution. Then, the numerical experiments for the parameter estimation of GEWP distribution for some finite sample sizes are presented. Finally, the comparative study of GEWP distribution and its sub-models is investigated via the goodness of fit test with real datasets to illustrate its potentiality. [ABSTRACT FROM AUTHOR]

This paper establishes a general framework for measuring statistical divergence. Namely, with regard to a pair of random variables that share a common range of values: quantifying the distance of the statistical distribution of one random variable from that of the other. The general framework is then applied to the topics of socioeconomic inequality and renewal processes. The general framework and its applications are shown to yield and to relate to the following: f-divergence, Hellinger divergence, Renyi divergence, and Kullback–Leibler divergence (also known as relative entropy); the Lorenz curve and socioeconomic inequality indices; the Gini index and its generalizations; the divergence of renewal processes from the Poisson process; and the divergence of anomalous relaxation from regular relaxation. Presenting a 'fresh' perspective on statistical divergence, this paper offers its readers a simple and transparent construction of statistical-divergence gauges, as well as novel paths that lead from statistical divergence to the aforementioned topics. [ABSTRACT FROM AUTHOR]

Degradation is an inevitable companion in the operation of solid oxide fuel cell (SOFC) systems since it directly deteriorates the reliability of the system's operation and the system's durability. Both are seen as barriers that limit the extensive commercial use of SOFC systems. Therefore, diagnosis and prognosis are valuable tools that can contribute to raising the reliability of the system operation, efficient health management, increased durability and implementation of predictive maintenance techniques. Remaining useful life (RUL) prediction has been extensively studied in many areas like batteries and proton-exchange membrane fuel cell (PEM) systems, and a range of different approaches has been proposed. On the other hand, results available in the domain of SOFC systems are still relatively limited. Moreover, methods relying on detailed process models and models of degradation turned out to have limited applicability for in-field applications. Therefore, in this paper, we propose an effective, data-driven approach to predicting RUL where the trend of the health index is modeled by an adaptive linear model, which is updated at all times during the system operation. This allows for a closed-form solution of the probability distribution of the RUL, which is the main novelty of this paper. Such a solution requires no computational load and is as such very convenient for the application in ordinary low-cost control systems. The performance of the approach is demonstrated first on the simulated case studies and then on the data obtained from a long-term experiment on a laboratory SOFC system. From the tests conducted so far, it turns out that the quality of the RUL prediction is usually rather low at the beginning of the system operation, but then gradually improves while the system is approaching the end-of-life (EOL), making it a viable tool for prognosis. [ABSTRACT FROM AUTHOR]

This paper introduces a new 2D representation of the orientation distribution function for an arbitrary material texture. The approach is based on the isometric square torus mapping of the Clifford torus, which allows for points on the unit quaternion hypersphere (each corresponding to a 3D orientation) to be represented in a periodic 2D square map. The combination of three such orthogonal mappings into a single RGB (red–green–blue) image provides a compact periodic representation of any set of orientations. Square torus representations of five different orientation sampling methods are compared and analyzed in terms of the Riesz s energies that quantify the uniformity of the samplings. The effect of crystallographic symmetry on the square torus map is analyzed in terms of the Rodrigues fundamental zones for the rotational symmetry groups. The paper concludes with example representations of important texture components in cubic and hexagonal materials. The new RGB representation provides a convenient and compact way of generating training data for the automated analysis of material textures by means of neural networks. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods. [ABSTRACT FROM AUTHOR]

This paper investigates the thermodynamic properties of strongly interacting gravitational systems. By deriving the partition function for such systems, the authors obtain expressions for various thermodynamic quantities, including internal energy, specific heat, Helmholtz free energy, entropy, chemical potential, and pressure. The paper introduces an interaction parameter that quantifies the degree of non-ideality in the system, and explores its effects on the thermodynamic properties. The authors also calculate the moments generating function, which provides information about the distribution of particle positions. Additionally, the distribution function for the system is derived. The paper highlights the existence of an upper bound temperature beyond which the partition function becomes negative, indicating a limit on the validity of the model. [ABSTRACT FROM AUTHOR]

As one of the dimensionality reduction techniques of hyperspectral image (HSI), band selection (BS) does not change the spectral characteristics and physical meaning of HSIs, which is beneficial to the identification and analysis of surface objects. Recently, many BS methods for target detection have achieved promising results by making full use of the priori spectral features of the target to be detected. Conversely, anomaly detection separates the anomaly based solely on the statistical distribution difference between anomaly and background without any prior information. Therefore, the development of BS for anomaly detection has lagged far behind that of BS for target detection. To this end, this paper proposes a novel BS algorithm dedicated to anomaly detection tasks, named anomaly‐background separation and particle swarm optimization (PSO)‐based BS. Specifically, an anomaly‐background separation framework (ABSF) is established to predetermine a priori knowledge of anomaly distribution. Then, three band prioritization criteria are constructed with the anomaly‐background constraints generated by ABSF. Finally, PSO is used to find the optimal subset of bands in the solution space. The experiments on two real datasets demonstrate that the proposed method yields better detection results and greater stability compared to other BS methods discussed in this paper. [ABSTRACT FROM AUTHOR]

Economic globalization and the rapid development of the Internet make logistics systems more and more diversified, people and enterprises have greatly increased their requirements for logistics systems, and fourth party logistics has received more and more attention from people and related enterprises. In order to further study the routing problem under uncertain stochastic environments, this paper considers the fourth party logistics routing problem from a single manufacturer to multiple distributors with uncertain times and random supplies under the complete information symmetry scenario and symmetric transportation volume decision space. Then, an uncertain stochastic programming model is established with the minimum total cost as its core objective, and the total transportation time, manufacturer's supply, and distributor's demand as constraints. In order to solve the optimal path of the above problems, this paper transforms the uncertain stochastic programming model into a classical mathematical programming model based on the distribution functions of uncertain time and random supply. Finally, two numerical examples are given to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents a novel class of interior solutions for anisotropic stars under the imposition of a self-similar symmetry. This means proposing exact solutions to the Einstein field equations to describe charged matter distribution with radiation flow. The Einstein–Maxwell system by employing specific choices of mass function is formulated to describe the gravitational collapse of charged, anisotropic, spherically symmetric distributions using the Schwarzschild metric. Two ordinary differential equations governing the dynamics are derived by matching a straightforward solution of the symmetry equations to the charged exterior (Reissner–Nordström–Vaidya). Models with satisfactory physical behavior are constructed by extensively exploring self-similar solutions for a set of parameters and initial conditions. Finally, the paper presents the evolution of physical variables and the collapsing radius, demonstrating the inevitable collapse of the matter distribution. [ABSTRACT FROM AUTHOR]

We consider the optimal scheduling problem in a multiserver queue with impatient customers belonging to multiple classes. We assume that each customer has a random abandonment time, after which the customer leaves the system if its service has not been completed before that. In addition, we assume that the scheduler is not able to anticipate the expiration of the abandonment times but only knows their distributions and how long each customer has been in the system. Many papers consider this scheduling problem under Poisson arrivals and linear holding costs assuming further that both the service times and the abandonment times have exponential distributions. Even with these additional assumptions, the exact solution is known only in very few special cases. To tackle this tricky problem, we apply the Whittle index approach. Unlike the earlier papers, which were restricted to exponential service times, we allow the service time distributions for which the hazard rate is decreasing. The Whittle index approach is applied to the discrete-time multiserver queueing problem with discounted costs. As our main theoretical result, we prove that the related relaxed optimization problem is indexable and derive the corresponding Whittle index explicitly. Based on this discrete-time result, we develop a reasonable heuristic for the original continuous-time multiserver scheduling problem. The performance of the resulting policy is evaluated in the M/G/M setup by numerical simulations, which demonstrate that it, indeed, gives better performance than the other policies included in the comparison. [ABSTRACT FROM AUTHOR]

This paper examines the paradoxical transparency involved in training machine-learning models. Existing literature typically critiques the opacity of machine-learning models such as neural networks or collaborative filtering, a type of critique that parallels the black-box critique in technology studies. Accordingly, people in power may leverage the models' opacity to justify a biased result without subjecting the technical operations to public scrutiny, in what Dan McQuillan metaphorically depicts as an "algorithmic state of exception". This paper attempts to differentiate the black-box abstraction that wraps around complex computational systems from the opacity of machine-learning models. It contends that the degree of asymmetry in knowledge is greater in the former than the latter. In the case of software systems, software codes are difficult to understand as only software experts with sufficient domain knowledge are equipped to formulate a sound critique. In contrast, the meanings of trained parameters in a machine-learning model are obscure even to the data scientists who configure and train the model. Hence, the asymmetry of knowledge lies only in how data examples are collected, the choice and configuration of machine-learning models, and the specification of features in model design. Under the trend of algorithmic decision-making proliferating with machine-learning heuristics, the paper contends that the more symmetric distribution of knowledge in machine learning could lead to a more transparent production process if proper policies are in place. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices should be designed taking security requirements into consideration, so that they can be used securely in open environments. Designing secure IoT devices requires the ability to design fast and secure cryptography modules. A component in these modules is the pseudorandom number generator (PRNG), which can be built using different strategies. Some of these strategies use chaotic maps, and in such cases, the chaotic map that is selected must be simple and feasible to implement in a digital device by using the IEEE-754 floating-point standard. The chaotic map must also generate number sequences whose statistical distribution looks uniform. In this way, this paper shows the digital implementation of a PRNG by using a non-scaled non-discretized skew tent map (STM). The proposed PRNG can produce uniformly distributed number sequences when the annulling chaos conditions are identified and avoided on the chaotic map. Furthermore, the pseudorandom sequences are generated in few milliseconds. Compared to similar PRNGs recently reported, the proposed PRNG has been successful, based on tests, such as the correlation coefficient, key sensitivity, statistical analysis, entropy analysis, key space and randomness. [ABSTRACT FROM AUTHOR]

Cellulose fibers come in a wide range of shapes and sizes. The heterogeneity of the fiber length, width, wall thickness, curl and external fibrillation is detrimental to the mechanical performance of products such as paper and paperboard. Although micro-mechanical models of these materials sometimes incorporate features of this heterogeneity, so far there is no standardized method of fully incorporating this. We examine a large number of industrial mechanical fiber pulps to determine what information such a standardized method would have to have. We find that the method must allow for both non-Gaussian distributions and dependence between the variables. We present a method of characterizing mechanical pulp under these conditions that views the individual fiber as outcome of a sampling process from a multivariate distribution function. The method is generally applicable to any dataset, even a non-Gaussian one with dependencies. Using a micro-mechanical model of a paper sheet the proposed method is compared with previously presented methods to study whether incorporating both a varying fiber size and dependencies is necessary to match the response of a sheet modeled with measured characterization data. The results demonstrate that micro-mechanical models of paper and paperboard should not neglect the influence of the dependence between the characteristic shape features of the fibers if the model is meant to match physical experiments. [ABSTRACT FROM AUTHOR]

Note, for instance, that the probabilities given by Castellares et al. ([1]) in page 4 are the same than those shown in expression (9.115) in the book by Johnson et al. ([3]), with the change of parameters Graph HT ht and Graph HT ht Neyman type A (NTA) distribution is frequently used in Biology, Biodosimetry, Environmental Sciences, Epidemiology, etc. A count random variable I X i is said to follow a stopped-sum Poisson, compound Poisson, multiple Poisson or clustered-Poisson distribution, if it can be represented as Graph HT ht where I N i is a Poisson random variable with parameter Graph HT ht and Graph HT ht are independent, identically distributed random variables that are also independent of I N i . [Extracted from the article]

[Display omitted] • The PVDF fluorescent test paper loaded with molecularly imprinted ratiometric fluorescence sensors (CdTe@SiO 2 @Si QDs-MIPs) was successfully prepared to selectively and visually detect catechol (CT). • The test paper made the CT detection out of equipment and realized the on-site analysis of CT. • PVDF membrane effectively improved the uneven distribution of the polymer on ordinary paper, enhanced the visual effect of the test paper. • Visualization of the PVDF fluorescent test paper can pre-judge CT concentration roughly in river water. Various methods have been proposed for the determination of catechol (CT), however, the actual applications of these methods were restricted by instrument dependence, complex operations and difficulty of the on-site monitoring. In this work, a molecularly imprinted ratiometric fluorescent test paper based on hydrophobic polyvinylidene difluoride (PVDF) membrane was successfully prepared for facile and on-site monitoring of CT. Firstly, a uniform core–shell molecularly imprinted ratiometric fluorescence sensor (CdTe@SiO 2 @Si QDs-MIPs) was successfully synthesized. Then the test paper was fabricated by facilely coating CdTe@SiO 2 @Si QDs-MIPs on a PVDF membrane by the vacuum filtration method. Ultimately, under optimal conditions, CdTe@SiO 2 @Si QDs-MIPs exhibited excellent stability and a relatively lower limit of detection (LOD) of 0.11 μM, satisfied recovery rates of 97.73%-102.30% and relative standard deviations of 2.01%-2.34%. Moreover, the selective study and ions inference experiment synergistically proved that the polymer has good anti-interference ability. Afterwards, the PVDF membrane was eventually selected to be the optimal substrate due to the uniform distribution of fluorescent substances on its surface, thus, greatly improved the accuracy of detection. The resultant test paper hugely improved the practicability and provided a reference for water quality testing. The facile and efficient strategy reported here opens a window for the rapid, visual and on-site detection of CT in river water samples. [ABSTRACT FROM AUTHOR]

Stiffness degradation of track–bridge systems under earthquake excitations can lead to track dynamic irregularity, impacting the safety of train operations post-earthquake. In this paper, the nonlinear time-history analysis of CRTS II track–bridge system model established by ANSYS finite element analysis software is carried out under the action of transverse random earthquake. The train–track–bridge system model considering the stiffness degradation caused by earthquake is established by MATLAB, and the earthquake-induced track dynamic irregularity sample library is constructed. The probability distribution mode of the power spectral density sample of the earthquake-induced track dynamic irregularity is explored and the calculation method based on the probability guarantee rate is proposed. The influence of structural damping ratio, train running speed and train type on the power spectral density curve of the earthquake-induced track dynamic irregularity is analyzed. The results show that the power spectrum samples of track dynamic irregularity conform to the hypothesis test of a normal distribution. The power spectral density of earthquake-induced dynamic irregularity primarily consists of medium and low-frequency components. When the structural damping ratio increases from 0.03 to 0.04, 0.04 to 0.05, 0.05 to 0.06, and 0.06 to 0.07, the variation gradients are 0.1701, 0.1240, 0.1034 and 0.0999, respectively, which indicates that the structural damping ratio has a significant effect on the power spectral density of earthquake-induced irregularity, and the impacts of train speed and train type on the power spectral density of near-fault earthquake-induced irregularity are minimal. [ABSTRACT FROM AUTHOR]

In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender's "future dangerousness." Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we use counterfactual reasoning to consider the prospects for improved fairness when members of a disadvantaged class are treated by a risk algorithm as if they are members of an advantaged class. We combine a machine learning classifier trained in a novel manner with an optimal transport adjustment for the relevant joint probability distributions, which together provide a constructive response to claims of bias-in-bias-out. A key distinction is made between fairness claims that are empirically testable and fairness claims that are not. We then use confusion tables and conformal prediction sets to evaluate achieved fairness for estimated risk. Our data are a random sample of 300,000 offenders at their arraignments for a large metropolitan area in the United States during which decisions to release or detain are made. We show that substantial improvement in fairness can be achieved consistently with a Pareto improvement for legally protected classes. [ABSTRACT FROM AUTHOR]

In current researches on belief rule base (BRB), input parameters are tended to be expressed in the form of quantitative values through expert knowledge combined with optimization methods. A singular quantitative value fails to capture the statistical properties, leading to irrational outcomes. Therefore, an attempt on attribute weights is made in this paper, and a new model with probability distribution attribute weights (pdw) called BRB-pdw is proposed. The combination of two attributes is in detail discussed, where attribute weights are described as random variables with specific probability distribution. To characterize the output of probability distribution attribute weight, a new concept of expectation of activation weight is proposed. In addition, the BRB-pdw is extended to multiple attributes to demonstrate its universality. Furthermore, fundamental properties and characteristics of the BRB-pdw are further validated by rigorous mathematical derivation. Finally, practicability of the BRB-pdw is validated with NASA lithium battery open dataset, and experiments show that the BRB-pdw model is more robust while maintaining precision. [ABSTRACT FROM AUTHOR]

Highlights Fatigue damages of Carbon Fiber Reinforced Polymer (CFRP) laminates are not obvious. Obtaining the evolution of the internal fatigue damage in CFRP laminates before visible cracks occur is of vital importance for ensuring structural reliability. Micro X‐ray computed tomography (Micro‐CT) can provide micron scale nondestructive imaging of internal structure of materials. In this paper, a preliminary study was conducted on the early fatigue damage evolution of 0° UD laminates. Axial tension‐tension fatigue tests of [0]16 CFRP laminates were carried out, and the evolutionary behavior of damages was confirmed by fatigue dissipated energy. Then, the internal damages of fatigued specimens were observed and reconstructed using Micro‐CT, and statistical parameters such as damage sphericity, length and angle were calculated. The spatial distribution characteristics of cracks were further acquired by surface porosity and frequency distribution of damage positions in ROI. Early damages from tensile‐tensile fatigue in 0° UD laminates extend along the fiber direction with original defects in the interlayer resin enrichment areas as the nucleus, and tend to concentrate near the width edge of the specimens. Finally, a “sandwich” structural model was developed to analyze the internal stresses of laminates under the influence of interlayer resin enrichment. The FEA results revealed that the internal stresses σy and σz concentrate near the width edges of the specimen, resulting in cracks tend to evolve more in the regions close to edges. Imaging matrix cracks of specimens by Micro‐CT at 12.91 μm voxel size. Demonstrating evolution patterns of cracks with its morphology statistics. Revealing crack distributions in ROIs with variations of surface porosity. Analyzing internal stress distribution by “sandwich” model created from reality. [ABSTRACT FROM AUTHOR]

AbstractThis paper considers the order-restricted statistical inference for two populations based on the joint type-II progressive censoring scheme. The lifetime distributions of the two populations are supposed to follow the Gompertz distribution with the same shape parameter but different scale parameters. The maximum likelihood estimates of the unknown parameters are derived by employing the Newton-Raphson algorithm and the expectation-maximization algorithm, respectively. The Fisher information matrix is then employed to construct asymptotic confidence intervals. For Bayes estimation, we assume an ordered Beta-Gamma prior for the scale parameters and a Gamma prior for the common shape parameter. Bayes estimations and the highest posterior density credible intervals for unknown parameters under two different loss functions are obtained with the importance sampling technique. To evaluate the performance of order-restricted inference, extensive Monte Carlo simulations are performed and two air-conditioning systems datasets are used to illustrate the proposed inference methods. In addition, the results are compared with the case when there is no order restriction on the parameters. Finally, the optimal censoring scheme is obtained by four optimality criteria. [ABSTRACT FROM AUTHOR]

The bound state solutions of the Schrödinger equation (SE) under a deformed hyperbolic potential are used in this paper to investigate the correlation between the information content, such as Shannon entropies and Fisher information, and the variance of a quantum system in both momentum and position spaces. The variance was obtained from the expectation moments in conjugated spaces and used to get the uncertainty products, Fisher information products and Shannon entropic sums for different potential parameters. These information measures were observed to vary with the potential parameters and obey their lowest bound inequalities. An increase in the Fisher information leads to a lower uncertainty and less information while decreases in the Fisher information result in higher uncertainties. We proposed a relationship between the Shannon entropies, Fisher information and the variance where an increase in the Fisher information leads to a lower Shannon entropy or a lower spread of the probability distribution and vice versa. The numerical results obtained with the proposed relations agree with the most utilized method in the literature. This current work simplifies the calculation of the Shannon entropies notably for complex potential functions. [ABSTRACT FROM AUTHOR]