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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]

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]

Overuse of empirical intravenous antibiotics in neonates in high-income countries (HICs) is well documented. The Kaiser Permanente neonatal early-onset sepsis (EOS) calculator is an evidence-based sepsis risk assessment tool that has demonstrated potential to reduce antibiotic usage in this population. The incidence of early-onset sepsis in most HICs is 0.4–0.8 per 1000 live births. The objective was to evaluate the calculator's impact on antibiotic rates and length of stay in a regional level II Special Care Nursery. A single-centre retrospective cohort study compared antibiotic administration rates in the first 72 h in neonates ≥35 weeks gestation born during two 6-month periods in 2019 (pre-EOS calculator) and 2021 (post-EOS calculator). Electronic and paper case records were accessed to capture data. Continuous data were summarised using mean and standard deviation, and categorical data were summarized using frequency distributions. There were 951 (2019) and 1129 (2021) infants born during the study periods. Following implementation of the calculator, antibiotic exposure decreased from 13.7% to 4.7% of all neonates without reported negative outcomes. Mean length of stay for neonates born across the two periods decreased from 2.38 to 2.13 days. Indications for antibiotic use shifted more towards clinical condition and away from obstetric risk factors. There were no culture-proven cases of sepsis or readmissions with EOS in either period. Implementation of the EOS calculator significantly reduced exposure to antibiotics, without adverse outcomes. [ABSTRACT FROM AUTHOR]

Abstract: The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not. [Copyright &y& Elsevier]

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]

[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]

Abstract: Aiming at the problem that paper smoothness cannot be measured on line, a new method for soft measurement of paper smoothness is put forward on the basis of studying paper quantization distribution. A soft measurement system is established, by which, paper quantization noise is processed by time–frequency analysis and non-linear transformation, so that online paper smoothness can be obtained through a Radial Basis Function Neural Networks (RBFNNs). The results show that this soft computing method is a feasible way with high measuring accuracy. [Copyright &y& Elsevier]

We analyze the multiauthorship matrix M, defined as the matrix where a cell M (j, κ) denotes the number of times authors with j publications are ranked as the κth author of an article. We prove that if the distribution of the number of authors per paper follows a power law, then the author rank distribution is approximately equal to this power law (more precisely, equal in Landau''s big O sense). We further determine the author rank distribution in the case where authors can be characterized through a seed number; this is the probability of preceding a fixed author in the byline of an article. Such a seed is determined for alphabetical ranking of authors using the standard western alphabet. [Copyright &y& Elsevier]

Abstract: Cooling tower emissions are harmful for several reasons such as air polluting, wetting, icing and solid particle deposition, but mainly due to human health hazards (i.e. Legionella). There are several methods for measuring drift drops. This paper is focussed on the sensitive paper technique, which is suitable in low drift scenarios and real conditions. The lack of an automatic classification method motivated the development of a digital image process algorithm for the Sensitive Paper method. This paper presents a detailed description of this method, in which, drop-like elements are identified by means of the Canny edge detector combined with some morphological operations. Afterwards, the application of a J48 decision tree is proposed as one of the most relevant contributions. This classification method allows us to discern between stains whose origin is a drop and stains whose origin is not a drop. The method is applied to a real case and results are presented in terms of drift and PM10 emissions. This involves the calculation of the main features of the droplet distribution at the cooling tower exit surface in terms of drop size distribution data, cumulative mass distribution curve and characteristic drop diameters. The Log-normal and the Rosin–Rammler distribution functions have been fitted to the experimental data collected in the tests and it can been concluded that the first one is the most suitable for experimental data among the functions tested (whereas the second one is less suitable). Realistic PM10 calculations include the measurement of drift emissions and Total Dissolved Solids as well as the size and number of drops. Results are compared to the method proposed by the U.S. Environmental Protection Agency assessing its overestimation. Drift emissions have found to be 0.0517% of the recirculating water, which is over the Spanish standards limit (0.05%). [Copyright &y& Elsevier]

Abstract: We study the formation of multi-armed spirals and multi-pairs antispirals in spatial rock–paper–scissors games with mobile individuals. We discover a set of seed distributions of species, which is able to produce multi-armed spirals and multi-pairs antispirals with a finite number of arms and pairs based on stochastic processes. The joint spiral waves are also predicted by a theoretical model based on partial differential equations associated with specific initial conditions. The spatial entropy of patterns is introduced to differentiate the multi-armed spirals and multi-pairs antispirals. For the given mobility, the spatial entropy of multi-armed spirals is higher than that of single armed spirals. The stability of the waves is explored with respect to individual mobility. Particularly, we find that both two armed spirals and one pair antispirals transform to the single armed spirals. Furthermore, multi-armed spirals and multi-pairs antispirals are relatively stable for intermediate mobility. The joint spirals with lower numbers of arms and pairs are relatively more stable than those with higher numbers of arms and pairs. In addition, comparing to large amount of previous work, we employ the no flux boundary conditions which enables quantitative studies of pattern formation and stability in the system of stochastic interactions in the absence of excitable media. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce some notions of invariancy for the Ricci tensor on real hypersurfaces in complex two-plane Grassmannians , namely, -invariant and invariant Ricci tensor. Using these notions, we give non-existence theorem and characterization for the special case among the real hypersurfaces of Type in , respectively. Here the distribution is defined by where and . [Copyright &y& Elsevier]

• The first μPADs-based kinetic matching approach for total polyphenol content measurement. • Identical reaction progress achieved on-chip without external facility assistance. • Rapid detection of total polyphenol content of tea within ten minutes. In this work, a facile kinetic matching approach for total polyphenol content (TPC) measurement was developed based on the adoption of microfluidic paper-based analytical devices with symmetric channel distribution. A set of Folin-Ciocalteu reactions performed on the same paper chip were activated all at the same time through synchronized filling of sodium carbonate solution among individual channels. Gallic acid was found valid as a standard compound for kinetic matching measurement of tea samples. TPC of tea infusions was successfully measured within ten minutes without any complexed time control procedure needed. Under the optimized conditions, the new developed method showed good linearity in the TPC range of 10–100 mg/L (r > 0.9955) and the inter-chip precision was 5.6% (n = 11). The results measured with the new developed approach were in good agreement with those with the conventional FC assay. [ABSTRACT FROM AUTHOR]

To enhance the efficacy of the distributed filter in mitigating heavy-tailed non-Gaussian noise and accommodating intricate environments, this study investigates the utilization of absolute and relative measurement information for the realization of multi-target cooperative positioning and proposes an improved robust distributed Kalman filter in this paper. The method is divided into two phases. Firstly, absolute measurement information undergoes processing via the standard Kalman filter, yielding the Kalman estimation value for the first phase, subsequently serving as the time update value for the second phase. Next, a robust Kalman filter based on Student's t distribution is used to process relative measurement information for measurement updates, resulting in more accurate estimates. Simulation results demonstrate that the proposed approach effectively approximates the posterior probability distribution, leading to enhanced stability and accuracy in positioning within environments characterized by both Gaussian and heavy-tailed non-Gaussian noise. • This paper focuses on multi-target localization, employing a two-phase distributed Kalman filter to integrate absolute measurements of individual target locations with the relative measurements between the targets. This integration enables each target to achieve decentralized independent localization. • Considering complexities of real-world environments, this paper models the relative measurements noise as heavy-tailed non-Gaussian noise. It integrates a robust Kalman filter based on Student's t-distribution with the second phase of distributed Kalman filter to enhance the performance of the localization system. • This paper simplifies the robust Kalman filter by representing measurement noise solely as a Student's t-distribution. The optimization process in the second phase requires only one iteration, thereby enhancing the efficiency of the distributed Kalman filter. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, many results on oscillation criteria of second-order nonlinear neutral delay differential equations with distributed deviating argument have been obtained, but some of these criteria are incorrect due to a mistake in a crucial auxiliary result. We give two counterexamples. [Copyright &y& Elsevier]

Abstract: The recent work Professor Nagode and colleagues is discussed with respect to the use of mixture Weibull models. [Copyright &y& Elsevier]

This paper studies the quantized iterative learning control with encoding–decoding mechanism of a class of impulsive differential inclusion systems with random data dropouts. First, the set-valued mappings in the differential inclusion systems are transformed into single-valued mappings by using the Steiner-type selector. Then, a learning algorithm based on the intermittent update principle is designed to address the data asynchronism problem caused by two-sided data dropouts. If the data are successfully transmitted at the actuator and measurement sides, then the control input is effectively updated. Furthermore, a suitable scaling sequence is introduced to ensure the system output to achieve zero-error tracking performance for a desired trajectory. An upper bound of the quantization level is determined such that the quantization error is always bounded. The results show that the quantization method reduces the burden of network communication at the cost of increasing the amount of computation, and the learning algorithm does not require the data dropouts to satisfy a certain probability distribution. Finally, the effectiveness of the learning algorithm is verified by numerical simulations of the switched reluctance motor system. • This paper enriches the iterative learning control (ILC) results for uncertain systems. • We establish an ILC research framework for continuous-time systems with unreliable networks. • We construct a novel learning algorithm to deal with the data asynchronous problem. [ABSTRACT FROM AUTHOR]

• Dual-wavelength excitable fluorescent Lyocell fibers were prepared by dry-jet wet spinning. • Modified Lyocell fibers exhibited double anti-counterfeiting functions. • Anti-counterfeiting papers were prepared by incorporating modified fibers into papermaking. • Dual-wavelength fluorescent fibers did not influence the brightness and opacity of paper. Two kinds of dual-wavelength excitable fluorescent Lyocell fibers, which can be excited by short-wavelength UV/IR or long-wavelength UV/IR radiation, were prepared by dry-jet wet spinning. These fluorescent Lyocell fibers can emit two different fluorescence wavelengths at two different excitation wavelengths, exhibiting double anti-counterfeiting functions, thereby providing higher security. SEM-EDX analysis showed the uniform phosphors distribution in Lyocell fibers. The fluorescent Lyocell fibers were mixed into pulp for papermaking. Addition of dual-wavelength excitable fluorescent Lyocell fibers had no influence on brightness and opacity of papers, and the mechanical properties of papers were similar or even higher than paper with addition of pure Lyocell fibers, although the introduction of phosphors decreased the mechanical properties of Lyocell fibers slightly. Our results proved that dual-wavelength excitable fluorescent Lyocell fibers can be used not only in textile fibers, but also in papermaking to develop various security paper products. [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]

Probability distributions are very important in describing practical phenomena. Among the available probability distributions, the three-parameter generalized inverse Weibull (for short "GI-Weibull") model has very useful applications in reliability, management, and healthcare studies, among others. This paper considers a weighted adaptation of the proposed new model. We proposed a weighted version of the GI-Weibull distribution, which may be called the weighted generalized inverse Weibull (for short "WGI-Weibull") distribution. The appeal of the WGI-Weibull distribution lies in its altered form of the GI-Weibull distribution, which does not necessitate the incorporation of extra parameters. Various properties of the WGI-Weibull distribution are obtained through mathematical derivation. Finally, a medical application (i.e., a medical data set) is studied and analyzed to illustrate the WGI-Weibull model. A comparison is conducted between the WGI-Weibull distribution and the GI-Weibull and inverse flexible Weibull models utilizing the medical data set. Employing three evaluation metrics, it is shown that the WGI-Weibull model describes the medical phenomena much better than the GI-Weibull and some Weibull models. [ABSTRACT FROM AUTHOR]

This paper presents a newsvendor model with backorders for customers who are willing to wait to be served. Demand follows a normal probability distribution, with the particularity that the expected value depends on the sale price and the variation coefficient is fixed. Three parameters are considered to characterize this dependence on the expected demand and the sale price: the population size of potential customers, the unit production cost of the item and an elasticity parameter with an isoelastic type. Backorders and lost sales are combined with a fixed proportion for the backorders. The quantities to be determined are the sale price and the order quantity. The goal is the maximization of the expected profit. The optimal solution is obtained in a closed form if there are no lost sales. In the case of a mixture of partial backordering and lost sales, a methodological proposal based on a numerical algorithm is given. The study reveals that the maximum expected profit and the optimal quantities to be determined are highly influenced by the unit purchasing cost and the degree of dependence of the demand concerning the sale price. Other parameters, such as the proportion of backorders and the variation coefficient of demand, are less influential. Numerical examples are used to illustrate the model, and a sensitivity analysis of the optimal solution regarding the nine initial parameters is presented. Some managerial insights deduced from the obtained results are also proposed. • A newsboy model with demand fixed partial backlogged is presented. • Demand follows a normal probability distribution with price-dependent mean. • The decision variables are the lot size and the selling price. • A numerical algorithm is given to obtain the optimal policy. [ABSTRACT FROM AUTHOR]

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, we introduce a new two-parameter lifetime distribution based on the type II Topp-Leone-G family and the inverse length biased exponential distribution which is called the type II Topp-Leone inverse length biased exponential (TIITL-ILBE) distribution. Various statistical properties of the proposed distribution, including the ordinary moments, moment generating function, incomplete moments, conditional moments, vitality and mean residual life functions, moments of the residual and reversed residual lifetime functions and Rényi entropy are derived. The method of maximum likelihood for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under complete samples is investigated. Also, The methods of maximum likelihood and Bayesian for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under joint censored samples is investigated. The real life data set results utilizing that the proposed distribution exhibits greater flexibility and evaluated empirically under complete and joint censored data. [ABSTRACT FROM AUTHOR]

In this paper, the effects of frequency on the flash sintering behavior and microstructure of Li 6.4 Ga 0.2 La 3 Zr 2 O 12 (0.2Ga-LLZO) were investigated. The results showed that an increase in frequency had little effect on the onset temperature, but this increase reduced the duration of the weak current stage the incubation stage. Moreover, this phenomenon led to a decrease in the power loss during the constant current stage. Scanning Electron Microscope images showed that the microstructure was symmetrically distributed, and the uniformity increased with increasing frequency. Finite element simulations and an IR thermocamera were used to analyze the temperatures of the specimens. The symmetric distribution of temperature during the flash sintering process was the root cause. The results of AC impedance spectroscopy showed that when the frequency of flash sintering was ≥ 400 Hz, the ionic conductivity of Ga-LLZO was greater than that of conventional sintering. The results of this work demonstrated the important role of frequency in controlling the microstructures of ceramic materials in flash sintering. [ABSTRACT FROM AUTHOR]

This study analyses an approach to consistently recover the second-order convergence of the lattice Boltzmann method (LBM), which is frequently degraded by an improper discretisation of the required source terms. The current work focuses on advection-diffusion models, in which the source terms are dependent on the intensity of transported fields. Such terms can be observed in reaction-type equations used in heat and mass transfer problems or multiphase flows. The investigated scheme is applicable to a wide range of formulations within the LBM framework. All considered source terms are interpreted as contributions to the zeroth-moment of the distribution function. These account for sources in a scalar field, such as density, concentration, temperature or a phase field. Further application of this work can be found in the method of manufactured solutions or in the immersed boundary method. This paper is dedicated to three aspects regarding proper inclusion of the source term in LBM schemes. Firstly, it identifies the differences observed between the ways in which source terms are included in the LBM schemes present in the literature. The algebraic manipulations are explicitly presented in this paper to clarify the observed differences, and to identify their origin. Secondly, it analyses in full detail, the implicit relation between the value of the transported macroscopic field, and the sum of the LBM densities. This relation is valid for any source term discretization scheme. It is a crucial ingredient for preserving the second-order convergence in the case of complex source terms. Moreover, three equivalent forms of the second-order accurate collision operator are presented. Finally, closed form solutions of this implicit relation are shown for a variety of common models, including general linear and second order terms; population growth models, such as the Logistic or Gompertz model and the Allen-Cahn equation. The second-order convergence of the proposed LBM schemes is verified on both linear and non-linear source terms. The pitfalls of the commonly used acoustic and diffusive scalings are identified and discussed. Furthermore, for a simplified case, the competing errors are shown visually with isolines of error in the space of spatial and temporal resolutions. [ABSTRACT FROM AUTHOR]

Abstract: In the papers [1] (Thomas and Oommen, 2013), [2] (Oommen and Thomas, 2014) and [3] (Thomas and Oommen, 2013), and their associated conference versions cited in those papers, we had introduced a new method of so-called “Anti”-Bayesian Pattern Recognition (PR) which achieved the classification using only a few (sometimes as few as two) points distant from the mean. While the PR strategy, in and of itself, is accurate, the claim that it was based on the Order Statistics (OS) of the distributions of the features is not. The PR and classification results are rather founded on the symmetric quantiles and not on the symmetric OSs. This brief paper corrects the flawed claim presented in those papers. [Copyright &y& Elsevier]

In many high-impact applications, it is important to ensure the quality of the output of a machine learning algorithm as well as its reliability in comparison to the complexity of the algorithm used. In this paper, we have initiated a mathematically rigorous theory to decide which models (algorithms applied on data sets) are close to each other in terms of certain metrics, such as performance and the complexity level of the algorithm. This involves creating a grid on the hypothetical spaces of data sets and algorithms so as to identify a finite set of probability distributions from which the data sets are sampled and a finite set of algorithms. A given threshold metric acting on this grid will express the nearness (or statistical distance) of each algorithm and data set of interest to any given application. A technically difficult part of this project is to estimate the so-called metric entropy of a compact subset of functions of infinitely many variables that arise in the definition of these spaces. [ABSTRACT FROM AUTHOR]

Statistical methods have widespread applications in real-life scenarios. Researchers have expressed keen interest in implementing different functions to raise new probability distributions. However, very limited efforts have been made to raise new methodologies by applying trigonometric functions. In this paper, we propose a new method based on the trigonometric function. The suggested trigonometric-based method is called a new trigonometric cosine- G (NTC- G) family to generate flexible models. The new method is developed using the cosine function. A subcase of the NTC- G method termed as a new trigonometric cosine-Weibull (NTC-Weibull) is studied. Certain mathematical properties of the NTC-Weibull model are presented. For the NTC-Weibull model, the point estimators are acquired. Finally, the suitability of the NTC-Weibull distribution is demonstrated in light of a healthcare data set. Using the numerical and graphical approaches, the evaluation results of the NTC-Weibull distribution are compared with the Weibull and its four well-known existing modifications. [ABSTRACT FROM AUTHOR]

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T- X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid numerical approach is presented that couples "ghost cell" based immersed boundary (IB) method with the finite volume lattice Boltzmann method (FVLBM). In the implementation process, the grid cells are classified into three types, i.e., " f l u i d c e l l ", " s o l i d c e l l " and " g h o s t c e l l ", where the " g h o s t c e l l " is the first layer of " s o l i d c e l l " near the " f l u i d c e l l ". As the wall boundary condition is reflected in the " g h o s t c e l l ", the high–accuracy reconstruction of variables in it will be the key point of present approach. Then, a FVLBM scheme is used to update the distribution function in the " f l u i d c e l l ", and a least square interpolation based formulation is derived to construct the distribution functions in it. Further, the bounce–back scheme embedded in the streaming–collision lattice Boltzmann method will be employed as the wall boundary condition. Besides, for the class of moving boundary problems, the construction of the distribution function in the " f r e s h c e l l " is also considered. A number of typical benchmarking incompressible viscous fluid flows over both stationary and moving objects are simulated to justify the present method, using obstacles ranging from a stationary and a translating circular cylinders, a rotationally oscillating cylinder, to two more complex flows around an oscillating cylinder in the stationary fluid and in the free–stream. Besides, the temporal and spatial accuracy also are testified by the simulation of Taylor–Couette flow. The obtained accurate simulation results demonstrate the capability of the present hybrid IB–FVLBM for computer simulations of both stationary and moving boundary problems in the incompressible flow regime. [ABSTRACT FROM AUTHOR]

This paper presents an application of general Tau theory to the modeling and analysis of articulatory trajectories in speech. We evaluated the model using electromagnetic articulometry data from 12 native speakers of English reading a common text, where trajectories of the following sensors were fitted: lower and upper lips, jaw, and three tongue sensors. Additionally, we analyzed trajectories of the lip aperture signal. Our experiments show that the general Tau theory model gives a better fit than existing (i) methods based on critically damped oscillators, and (ii) a method based on sequential target approximation. These findings support the hypothesis of Tau-guided movements of articulators during speech production. In the second part of the paper, our Tau theory analysis shows that articulatory movements follow similar velocity profile distributions across speakers. In particular, the value of the shape parameter κ of the Tau theory equation is identically distributed across speakers, following a unimodal distribution. The statistical mode of the distribution corresponds to the value of κ that generates a symmetric velocity profile. The analysis of the statistical distribution of κ values also reveals that its variance decreases when greater articulatory effort is required, such that produced articulatory effort remains close to that predicted by the theoretical minimal cost function based on forces acting on the moving articulator. This provides new evidence that articulatory effort is optimized during speech production. • General Tau theory is applied to articulatory movements in speech. • General Tau theory equations provide better fits to EMA data than other methods. • Evidence supporting effort minimization of articulatory movements in speech. [ABSTRACT FROM AUTHOR]

In this paper the dynamic performance of Heat-Integrated Distillation Columns (HIDiC) is presented. The dynamic performance was determined for the optimal HIDiC designs optimized previously using a constrained Boltzmann-based estimation of distribution algorithm. Eight close-boiling mixtures, covering a range of relative volatility (α) from 1.12 to 2.4, were used as case studies. The dynamic behavior was obtained under open and closed-loop process analysis. The results obtained showed that the HIDiC columns undergo worse dynamic performance than their equivalent traditional columns for all case studies. Furthermore, it was notorious that the difference in the dynamic behavior of both configurations kept a relatively uniform trend for most systems. However, a marked difference in the dynamic properties was determined for the mixture close to azeotropic behavior, which experienced a considerably larger control effort. Thus, the novel findings disclosed in this paper show that, although the HIDiC sequences experienced worse dynamics than the conventional columns, the HIDiC configurations reached a stable dynamic behavior for all the range of α of the mixtures under study. Nevertheless, high control difficulties were particularly determined for the HIDiC configuration used to separate the mixture with α close to unity (mixture of xylenes), which in fact is the HIDiC sequence with the best energetic and economic benefits. Hence, the energetic and economic potential of the HIDiC columns is not limited by the dynamic behavior for most case studies analyzed, at least in theoretical terms. However, such potential might be particularly reduced or unharnessed in the separation of the mixture with α near the azeotropic behavior due to the dynamics of the HIDiC column for this separation. So, the findings presented in this paper allow to infer that sequential or simultaneous dynamic studies must be achieved along with the optimization of the HIDiC columns to determine the integral performance (energetic, economic and dynamic) of these configurations. [Display omitted] • Dynamic performance of Heat Integrated Distillation Columns is presented. • Eight low relative volatility equimolar mixtures were examined. • Dynamic performance was determined using open and closed-loop process analysis. • HIDiC columns showed worse dynamic performance than the conventional columns. • The mixture with lowest relative volatility showed the worst dynamic performance. [ABSTRACT FROM AUTHOR]

Let Λ be the collection of all probability distributions for (X , X ˜) , where X is a fixed random vector and X ˜ ranges over all possible knockoff copies of X (in the sense of Candes et al. (2018)). Three topics are developed in this paper: (i) A new characterization of Λ is proved; (ii) A certain subclass of Λ , defined in terms of copulas, is introduced; (iii) The (meaningful) special case where the components of X are conditionally independent is treated in depth. In real problems, after observing X = x , each of points (i)–(ii)–(iii) may be useful to generate a value x ˜ for X ˜ conditionally on X = x. • The paper provides some new theoretical insights on the knockoffs procedure introduced by Barber and Candes (2015). Let Λ be the set of all possible knockofffs. In Section 2, a new characterization of Λ is proved. • In Section 3, a certain (proper) subclass Λ 0 ⊂ Λ is introduced. The elements of Λ 0 admit a simple and explicit representation in terms of copulas. • In Section 4, we focus on the case where the variables X 1 , ... , X p are conditionally independent. Under this assumption, we show how to build a knockoff X ˜. [ABSTRACT FROM AUTHOR]

• Summarizing various models of GAN. • Discussing the advantages and applications of GAN in medical image fusion field. • Analyzing the challenges faced by GAN and envisioning the future directions of GAN. Generative Adversarial Network (GAN) is a research hotspot in deep generative models, which has been widely used in the field of medical image fusion. This paper summarizes GAN models from the following four aspects: firstly, the basic principles of GAN are expounded from two aspects: basic model and training process; secondly, variant GAN models are summarized into three directions (Probability Distribution Distance, Overall Network Architecture, Neural Network Structure), from the methods based on f-divergence, the methods based on IPM, Single-Generator and Dual-Discriminators GAN, Multi-Generators and Single-Discriminator GAN, Multi-Generators and Multi-Discriminators GAN, Conditional Constraint GAN, Convolutional Neural Network structure GAN and Auto-Encoder Neural Network structure GAN are eight dimensions to summarize the typical models in recent years; thirdly, the advantages and application of GAN models in the field of medical image fusion are explored from three aspects; fourthly, the main challenges faced by GAN and the challenges faced by GAN models in medical image fusion field are discussed and the future prospects are given. This paper systematically summarizes various models of GAN, advantages and challenges of GAN models in medical image fusion field, which is very important for the future research of GAN. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model's proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis. Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip's destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode's travel time to the selected destination zone. Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 performed in Stockholm containing 10819 individuals and days. [ABSTRACT FROM AUTHOR]

• A probabilistic approach is presented to fit composite failure model parameters. • It implements the MLE method and controlled numerical sampling techniques. • It enables the introduction of safety factors for uncertainty in material properties. • It is completed by an automatic DoE for an informative mechanical test search. • The method is demonstrated via fit on mechanical experiments on UD tapes. In this paper, a probabilistic approach is presented to simultaneously evaluate all strength and additional fitting parameters of a pre-selected composite first-ply failure model using known uniaxial and multiaxial failure stress states. The method processes all available mechanical test results in terms of failure stress states as one batch of data. For parameter fitting it implements the Maximum Likelihood Method and controlled numerical sampling techniques. The present work introduces the theoretical background as well as investigates the effect of the selection of the underlying strength distribution function type. The biggest benefit of this approach is the simultaneous handling of all available test data as well as the generality in terms of assumed strength distribution function and failure model type. In addition, the automatic evaluation of the uncertainty of all strength parameters enables introduction of a safety factor quantification related to uncertainty in material properties as one of the main contributors to scatter sources. A local sensitivity-based stress-state determination process is also introduced to design a set of input failure stresses that guarantee the identifiability of all parameters thus, ensuring robustness of the fitted constants. The entire methodology is demonstrated through the example of the Tsai-Wu model by processing experimental data and by comparing the results against the literature. [ABSTRACT FROM AUTHOR]

• Potential induced on pipelines during geomagnetic storms by probabilistic approach. • Most probable geoelectric field direction. • Even at mid-latitudes, potentials may exceed several tens of Volts. • Risk of damage for equipment connected to the pipeline. The paper presents a probabilistic approach for the determination of the potential induced along a pipeline route during a geomagnetic storm; particular emphasis is given to intense events such as 100-year and 10-year storms. By assuming that the induced geoelectric field orientation at the ground level and at a given latitude can be treated as a random variable with normal probability distribution, by means of a Monte Carlo algorithm it is possible to build up the empirical probability distribution of the induced potential along the pipeline. The main result is that, in case of an extreme event such as a 100-year storm, even at middle latitudes, potentials of several tens of Volts have a not negligible probability of being induced, at least one time during the pipeline life, along the plant route with possible consequences of damages to apparatuses and equipment connected to the pipeline itself. [ABSTRACT FROM AUTHOR]

In this paper, we considered a new probability distribution with new applications in the field of engineering, in particular, in music engineering. The new probability distribution is mainly based on the Weibull distribution and cosine function. We call this the weighted cosine flexible Weibull distribution. The point estimators for the new distribution are obtained. We evaluate these point estimators for three sets of parameter values. The illustration of the weighted cosine flexible Weibull distribution is provided for two practical data set which are drawn from the reliability and music engineering. In addition, to dual robust machine learning approaches, we implement the Lasso (Least Absolute Shrinkage and Selection Operator) and Elastic Net (ENet) to improve the predictive performance of the data. This was done in response to the existence of outliers in the same datasets. We provide comparative analysis to see how well these approaches performed in comparison to the Step Indicator Saturation (SIS) method. [ABSTRACT FROM AUTHOR]

In this work, a multiple-relaxation-time lattice Boltzmann (MRT-LB) model based on discrete Hermite moments is proposed for nonlinear convection-diffusion equations (NCDEs). First, we use the block-weighted orthogonality of Hermite matrix to give a general expression of equilibrium distribution function (EDF) with adjustable parameter d 0. This expression is suitable for NCDEs and Navier-Stokes equations and can be easily extended to higher order form. In this paper, we mainly construct the discrete Hermite moments based MRT-LB (HMRT-LB) model for NCDEs. Different from the previous Hermite moment LB model, the collision matrix of the present model consists of a block-weighted orthogonal Hermite matrix with d 0. By the direct Taylor expansion, our model is recovered to NCDEs macroscopically. Then, we analyze the choice of order of EDF and the influence of d 0 on the stability region of the model. At the same time, we also give the changes of the stable region of the model with different d 0 when EDF is taken as a higher order form. Finally, the influence of different orders of EDF and d 0 on stability is simulated by numerical experiments, and the numerical results are in agreement with the analytical results. [ABSTRACT FROM AUTHOR]

Lattice Boltzmann method (LBM) is a powerful fluid flow solver. Using this method to solve other PDEs might be a difficult task. The first challenge is to find a suitable local equilibrium distribution function (EDF) capable of recovering the desired PDE. The next difficulty arises from the explicit nature of LBM. The conditional stability of the LBM algorithm affects the numerical solution accuracy. Damped Kuramoto–Sivashinsky (DKS) equation is a fourth–order PDE that recently challenged many numerical methods' abilities. This equation is highly sensitive to a parameter that causes three states of solutions in a small interval of freedom. In this paper, we challenged LBM to solve the two–dimensional DKS equation by finding EDF using the Chapman–Enskog analysis up to the fourth–order. Also, the von Neumann analysis and a simple genetic algorithm are applied to find reliable values for the free parameters. Furthermore, a modification on image-based ghost node method is proposed for implementation of the boundary conditions in the complex geometries. [ABSTRACT FROM AUTHOR]

Probability distributions are frequently used for statistical modeling of real-life phenomena in every field of life. Most often, real-life data sets are skewed in nature, and therefore, asymmetrical probability distributions are very competent for such scenarios. In the recent gains in distribution theory, researchers are focusing on developing new symmetrical and asymmetrical trigonometric-based probability distributions for data modeling in various fields. In the literature, however, there is no published work on statistical modeling of the transaction confirmation times on blockchain technology using trigonometric-originated probability distributions. For the first time covering the aforesaid research gap, this paper introduces a new trigonometric-based probabilistic approach for modeling the transaction confirmation times on blockchain technology. The new approach is called a new sine- G family of distributions. Using the proposed method, a new probability distribution called a new sine-Weibull distribution is studied. The proposed model is very flexible and obeys the symmetrical and asymmetrical shapes of its density function. The estimators of the new sine-Weibull distribution are derived, and their evaluation is tested through a simulation study. The applicability of the new sine-Weibull distribution is demonstrated by analyzing the average waiting time until the Bitcoin transaction is confirmed on blockchain technology. [ABSTRACT FROM AUTHOR]

In the agri-food supply chain, the perishable nature of the commodities opens a series of challenges at all decision levels: strategical, tactical and operational. This paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. To deal with this source of uncertainty, the conditional value-at-risk (CVaR) has been adopted as a risk measure, and then the model has been applied to the agri-food supply chain under investigation. The model is the core of a dynamic approach for the optimal distribution and inventory planning over a short-medium horizon. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Italy show the effectiveness of the proposed approach for evaluating different risk attitudes. [ABSTRACT FROM AUTHOR]

• A probability distribution model of platoon size of connected vehicles is proposed. • A fundamental diagram model of the mixed traffic flow is developed. • An analytical model of the capacity of mixed traffic flow is proposed. • A numerical experiment is designed to discuss the impact of penetration rate and maximum platoon size on road capacity. This paper proposes a capacity analysis model for a mixed traffic flow environment that considers the heterogeneity and maximum platoon size of connected vehicles. Firstly, we explore how the organization mode of platoon affects car-following characteristics in mixed traffic flow with connected and automated vehicles, connected human-driven vehicles, automated vehicles, and human-driven vehicles. Different car-following models are used to describe the car-following characteristics of different types of vehicles. Secondly, the probability distribution model is developed for the size of platoons considering the penetration rate and the maximum platoon size of connected and automated vehicles, connected human-driven vehicles. Thirdly, the fundamental diagram of mixed traffic flow is further derived based on the probability distribution of platoon size and car-following models. Then, the capacity model of mixed traffic flow is proposed. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates and maximum platoon sizes of connected and automated vehicles, connected human-driven vehicles on the capacity of the mixed traffic flow. The results show that (1) with a low penetration rate of connected and automated vehicles, connected human-driven vehicles, the change in the maximum platoon size has no noticeable effect on the distribution of platoon size of connected and automated vehicles, connected human-driven vehicles. (2) Road capacity increases with the maximum platoon size and the penetration rate of connected vehicles. (3) Compared with connected human-driven vehicles, the penetration rate of connected and automated vehicles has a more significant impact on road capacity. [ABSTRACT FROM AUTHOR]

So far in the literature, the two-parameter Weibull distribution and its other extensions are frequently implemented to analyze the wind speed energy data sets. However, based on our study of the literature, there is no published work about analyzing the wind speed energy data sets using new probability distributions that are developed via trigonometric functions. In this paper, I attempt to cover this amazing and interesting research gap. Therefore, I incorporate a trigonometric function, especially, the sine function to introduce a new statistical distributional method. The proposed method is called a new modified sine- G family of distributions. Some distributional properties of the new modified sine- G method are obtained. Using the new modified sine- G method, a new extension of the Weibull distribution called a new modified sine-Weibull distribution is studied. The new modified sine-Weibull distribution is applied to analyze four wind speed energy data sets. All three data sets are taken from the weather station at Sotavento Galicia in the Canary Islands, Spain, located at 43.3544 North and 7.8812 West. [ABSTRACT FROM AUTHOR]

This paper is a continuation of our previous paper (Liu and Pagès, 2020). In this paper, we establish the monotone convex order (see further (1.1)) between two R -valued McKean–Vlasov processes X = (X t) t ∈ [ 0 , T ] and Y = (Y t) t ∈ [ 0 , T ] defined on a filtered probability space (Ω , F , (F t) t ≥ 0 , P) by d X t = b (t , X t , μ t) d t + σ (t , X t , μ t) d B t , X 0 ∈ L p (P) with p ≥ 2 , d Y t = β (t , Y t , ν t) d t + θ (t , Y t , ν t) d B t , Y 0 ∈ L p (P) , where ∀ t ∈ [ 0 , T ] , μ t = P ∘ X t − 1 , ν t = P ∘ Y t − 1. If we make the convexity and monotony assumption (only) on b and | σ | and if b ≤ β and | σ | ≤ | θ | , then the monotone convex order for the initial random variable X 0 ⪯ mcv Y 0 can be propagated to the whole path of processes X and Y. That is, if we consider a non-decreasing convex functional F defined on the path space with polynomial growth, we have E F (X) ≤ E F (Y) ; for a non-decreasing convex functional G defined on the product space involving the path space and its marginal distribution space, we have E G (X , (μ t) t ∈ [ 0 , T ] ) ≤ E G (Y , (ν t) t ∈ [ 0 , T ] ) under appropriate conditions. The symmetric setting is also valid, that is, if Y 0 ⪯ mcv X 0 and | θ | ≤ | σ | , then E F (Y) ≤ E F (X) and E G (Y , (ν t) t ∈ [ 0 , T ] ) ≤ E G (X , (μ t) t ∈ [ 0 , T ] ). The proof is based on several forward and backward dynamic programming principles and the convergence of the truncated Euler scheme of the McKean–Vlasov equation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a theoretical sub-critical wait-time experiment that may be used to validate stochastic nuclear reactor kinetics models which compute the neutron population probability distribution. The regulation governing many zero-power reactors prohibits super-critical nuclear reactor operation. This paper shows that a desirable wait-time probability distribution can be computed by only modifying the neutron source strength. An analytical model, which ignores the contribution of delayed neutron precursors, is presented. Additionally, a six-delayed neutron precursor group model using the Pál–Bell equations is used to investigate the relationship between the nuclear reactor system parameters and the resulting wait-time probability distribution. Finally, this paper shows that two neutron energy group models can be collapsed down to an equivalent single (one speed or mono-energetic) neutron energy group model which accurately reproduce the same wait-time probability distribution. [ABSTRACT FROM AUTHOR]