Search

The BL17B beamline at the Shanghai Synchrotron Radiation Facility was first designed as a versatile high‐throughput protein crystallography beamline and one of five beamlines affiliated to the National Facility for Protein Science in Shanghai. It was officially opened to users in July 2015. As a bending magnet beamline, BL17B has the advantages of high photon flux, brightness, energy resolution and continuous adjustable energy between 5 and 23 keV. The experimental station excels in crystal screening and structure determination, providing cost‐effective routine experimental services to numerous users. Given the interdisciplinary and green energy research demands, BL17B beamline has undergone optimization, expanded its range of experimental methods and enhanced sample environments for a more user‐friendly testing mode. These methods include single‐crystal X‐ray diffraction, powder crystal X‐ray diffraction, wide‐angle X‐ray scattering, grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), and fully scattered atom pair distribution function analysis, covering structure detection from crystalline to amorphous states. This paper primarily presents the performance of the BL17B beamline and the application of the GIWAXS methodology at the beamline in the field of perovskite materials. [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]

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 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]

The bounded confidence model assumes simple continuous opinion dynamics in which agents ignore opinions which are too far from their own. The two initial variants—Hegselmann–Krause (HK) and Deffuant–Weisbuch (DW)—of the model have attracted significant attention since the early 2000s. This paper revisits the version of the HK model applied to a probability distribution, earlier studied by Jan Lorenz. It shows that the bifurcation diagram depends on the parity of the size of the discretisation and that adding a small noise to the initial conditions leads to complex transitions involving several phases. [ABSTRACT FROM AUTHOR]

Accurate assessment and prediction of water shortage risk are essential prerequisites for the rational allocation and risk management of water resources. However, previous water shortage risk assessment models based on copulas have strict requirements for data distribution, making them unsuitable for extreme conditions such as insufficient data volume and indeterminate distribution shapes. These limitations restrict the applicability of the models and result in lower evaluation accuracy. To address these issues, this paper proposes a water shortage risk assessment model based on kernel density estimation (KDE) and copula functions. This approach not only enhances the robustness and stability of the model but also improves its prediction accuracy. The methodology involves initially utilizing kernel density estimation to quantify the random uncertainties in water supply and demand based on historical statistical data, thereby calculating their respective marginal probability distributions. Subsequently, copula functions are employed to quantify the coupled interdependence between water supply and demand based on these marginal probability distributions, thereby computing the joint probability distribution. Ultimately, the water shortage risk is evaluated based on potential loss rates and occurrence probabilities. This proposed model is applied to assess the water shortage risk of the Yuxi water receiving area in the Central Yunnan Water Diversion Project, and compared with existing models through experimental contrasts. The experimental results demonstrate that the model exhibits evident advantages in terms of robustness, stability, and evaluation accuracy, with a rejection rate of 0 for the null hypothesis of edge probability fitting and a smaller deviation in joint probability fitting compared to the most outstanding model in the field. These findings indicate that the model presented in this paper is capable of adapting to non-ideal scenarios and extreme climatic conditions for water shortage risk assessment, providing reliable prediction outcomes even under extreme circumstances. Therefore, it can serve as a valuable reference and source of inspiration for related engineering applications and technical research. [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]

Reasonable distribution of braking force is a factor for a smooth, safe, and comfortable braking of trains. A dynamic optimal allocation strategy of electric-air braking force is proposed in this paper to solve the problem of the lack of consideration of adhesion difference of train wheelsets in the existing high-speed train electric-air braking force optimal allocation strategies. In this method, the braking strategy gives priority to the use of electric braking force. The force model of a single train in the braking process is analyzed to calculate the change of adhesion between the wheel and rail of each wheelset after axle load transfer, and then the adhesion of the train is estimated in real time. Next, with the goal of maximizing the total adhesion utilization ratio of trailer/ motor vehicles, a linear programming distribution function is constructed. The proportional coefficient of adhesion utilization ratio of each train and the application upper limit of braking force in the function is updated according to the change time point of wheelset adhesion. Finally, the braking force is dynamically allocated. The simulation results of Matlab/Simulink show that the proposed algorithm not only uses the different adhesion limits of each trailer to reduce the total amount of braking force undertaken by the motor vehicle, but also considers the adhesion difference of each wheelset. The strategy can effectively reduce the risk and time of motor vehicles during the braking process and improve the stability of the train braking. [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]

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]

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]

Recently, there has been interest in determining the viscoelastic properties of polymeric liquids and other complex fluids by means of Diffusing Wave Spectroscopy (DWS). In this technique, light-scattering spectroscopy is applied to highly turbid fluids containing optical probe particles. The DWS spectrum is used to infer the time-dependent mean-square displacement and time-dependent diffusion coefficient D of the probes. From D, values for the storage modulus G ′ (ω) and the loss modulus G ′ ′ (ω) are obtained. This paper is primarily concerned with the inference of the mean-square displacement from a DWS spectrum. However, in much of the literature, central to the inference that is said to yield D is an invocation g (1) (t) = exp (− 2 q 2 X (t) 2 ¯) of the Gaussian Approximation for the field correlation function g (1) (t) of the scattered light in terms of the mean-square displacement X (t) 2 ¯ of a probe particle during time t. Experiment and simulation both show that the Gaussian approximation is invalid for probes in polymeric liquids and other complex fluids. In this paper, we obtain corrections to the Gaussian approximation that will assist in interpreting DWS spectra of probes in polymeric liquids. The corrections reveal that these DWS spectra receive contributions from higher moments X (t) 2 n ¯ , n > 1 , of the probe displacement distribution function. [ABSTRACT FROM AUTHOR]

This paper proposes a hyperspectral imaging simulation method based on a ray-tracing algorithm. The algorithm combines calculations based on solar and atmospheric visible light radiation as well as the spectral bidirectional reflection distribution function (BRDF) of the target surface material and can create its own scenarios for simulation calculations on demand. Considering the presence of multiple scattering between the target and background, using the ray-tracing algorithm enables the precise computation of results involving multiple scattering. To validate the accuracy of the algorithm, we compared the simulated results with the theoretical values of the visible light scattering intensity from a Lambertian sphere. The relative error obtained was 0.8%. Subsequently, a complex scene of engineering vehicles and grass was established. The results of different observation angles and different coating materials were calculated and analyzed. In summary, the algorithm presented in this paper has the following advantages. Firstly, it is applicable to geometric models composed of any triangular mesh elements and accurately computes the effects of multiple scattering. Secondly, the algorithm combines the spectral BRDF information of materials and improves the efficiency of multiple scattering calculations using nonuniform sampling. The computed hyperspectral scattering data can be applied to simulate airborne or space-borne remote sensing data. [ABSTRACT FROM AUTHOR]

The article presents abstracts of contributed papers submitted for presentation at the Chicago Meeting. "Some Recent Contributions to the Lanchester Theory of Combat," by Ralph E. Bach, Laislav Dolansky and Harlod L. Stubbs explains that since the original formulation of Lanchester's equations pertaining to the combat of two opposing forces, the theory has been extended, organized and applied by many scientists. Certain more recent extensions of this theory, which have been developed under Project Numerics, will be described in this paper. Another paper "Single-Shot Kill Probability As a Measure of Bombing Effectiveness," by Albert M. Johnson and Vincent A. Ciminera points out advantages of using single-shot kill probability as a measure of bombing effectiveness in evaluating weapon systems. Historically the circular probable error has been employed for this purpose. However, with compressed reaction tunes made possible by future systems and the large effective yields of nuclear weapons, it is more desirable to be able to predict the probability that a single bomb will impact within a lethal distance of its intended target. The computation of this statistic, the single shot kill probability, is accomplished for values of the lethal radius and parameters of the distribution which characterize the bombing system.

"Tele," English Edition No 1, 1957, Information from The Royal Board of Swedish Telecommunications, edited by E Malmgren, contains five papers by Conny Palm prefaced by a short article, "Research on Telephone Traffic Carried by Full Availability Groups." The first paper, "Some Propositions Regarding Flat and Steep Distribution Functions," characterizes two classes of distribution functions arising in telephone traffic congestion. In the second paper, "Some Observations on the Erlang Formulae for Busy Signal Systems," an attempt is made at a brief systematic investigation of the form and properties of the most important traffic functions, in addition to some numerical calculations and tabulation procedures. The third paper is called, "Contributions to the Theory on Delay Systems" Unlike a busy-signal system in which a call that finds all channels occupied makes a new try at calling later, a delay system permits a call to wait to obtain service. The fourth paper is called "Waiting Times with Random Served Queue." The fifth paper is entitled "Duration of Congestion States in Delay Systems" To avoid long delays it is desirable first to study the distribution function for the duration of the congestion states.

This study is anchored in the H2020 SEAMLESS project (https://www.seamlessproject.org , last access: 29 January 2024), which aims to develop ensemble assimilation methods to be implemented in Copernicus Marine Service monitoring and forecasting systems, in order to operationally estimate a set of targeted ecosystem indicators in various regions, including uncertainty estimates. In this paper, a simplified approach is introduced to perform a 4D (space–time) ensemble analysis describing the evolution of the ocean ecosystem. An example application is provided, which covers a limited time period in a limited subregion of the North Atlantic (between 31 and 21 ∘ W, between 44 and 50.5 ∘ N, between 15 March and 15 June 2019, at a 1/4 ∘ and a 1 d resolution). The ensemble analysis is based on prior ensemble statistics from a stochastic NEMO (Nucleus for European Modelling of the Ocean)–PISCES simulator. Ocean colour observations are used as constraints to condition the 4D prior probability distribution. As compared to classic data assimilation, the simplification comes from the decoupling between the forward simulation using the complex modelling system and the update of the 4D ensemble to account for the observation constraint. The shortcomings and possible advantages of this approach for biogeochemical applications are discussed in the paper. The results show that it is possible to produce a multivariate ensemble analysis continuous in time and consistent with the observations. Furthermore, we study how the method can be used to extrapolate analyses calculated from past observations into the future. The resulting 4D ensemble statistical forecast is shown to contain valuable information about the evolution of the ecosystem for a few days after the last observation. However, as a result of the short decorrelation timescale in the prior ensemble, the spread of the ensemble forecast increases quickly with time. Throughout the paper, a special emphasis is given to discussing the statistical reliability of the solution. Two different methods have been applied to perform this 4D statistical analysis and forecast: the analysis step of the ensemble transform Kalman filter (with domain localization) and a Monte Carlo Markov chain (MCMC) sampler (with covariance localization), both enhanced by the application of anamorphosis to the original variables. Despite being very different, the two algorithms produce very similar results, thus providing support to each other's estimates. As shown in the paper, the decoupling of the statistical analysis from the dynamical model allows us to restrict the analysis to a few selected variables and, at the same time, to produce estimates of additional ecological indicators (in our example: phenology, trophic efficiency, downward flux of particulate organic matter). This approach can easily be appended to existing operational systems to focus on dedicated users' requirements, at a small additional cost, as long as a reliable prior ensemble simulation is available. It can also serve as a baseline to compare with the dynamical ensemble forecast and as a possible substitute whenever useful. [ABSTRACT FROM AUTHOR]

This scientific paper explores the operation of a multiprocessor task servicing system. Tasks are received into the system at random intervals and are characterized by several stochastic parameters, including the number of processors required for their execution, the maximum allowable busy time for these processors, and the permissible waiting time in the task queue. The organization of task servicing in this system follows a first-in, first-out (FIFO) approach, ensuring uninterrupted processing. The key servicing process involves periodically selecting the first task in the queue and assessing its feasibility for immediate execution. If the task meets the necessary criteria, it is dispatched for processing. This process continues iteratively until a task is found, the parameters of which prevent immediate servicing. It is important to note that tasks in the queue have a limited window of time within which they can be serviced; otherwise, they may exit the system without service. This paper focuses on systems characterized by exponential distributions for random variables related to task arrivals, servicing times, and waiting restrictions. A system of equations is derived that describes the system's steady-state behavior. These equations enable the calculation of probabilities associated with the system's various states. Additionally, the paper provides insights into the probability distributions of virtual waiting times for tasks that arrive in the system at any given moment. [ABSTRACT FROM AUTHOR]

In the paper, we consider a three-dimensional mathematical problem of fluid-solid dynamical interaction, when an anisotropic elastic body occupying a bounded region Ω+ is immersed in an inviscid fluid occupying an unbounded domain Ω- = ℝ³ \ Ω++. In the solid region, we consider the generalized Green--Lindsay's model of the thermo-electro-magnetoelasticity theory. In this case, in the domain Ω+ we have a six-dimensional thermo-electro-magneto-elastic field (the displacement vector with three components, electric potential, magnetic potential, and temperature distribution function), while we have a scalar acoustic pressure field in the unbounded domain Ω-. The physical kinematic and dynamical relations are described mathematically by the appropriate initial and boundarytransmission conditions. Using the Laplace transform, the dynamical interaction problem is reduced to the corresponding boundary-transmission problem for elliptic pseudo-oscillation equations containing a complex parameter τ . We derive the appropriate norm estimates with respect to the complex parameter τ and construct the solution of the original dynamical problem by the inverse Laplace transform. As a result, we prove the uniqueness, existence, and regularity theorems for the dynamical interaction problem. Actually, the present investigation is a continuation of the paper [Chkadua G, Natroshvili D. Mathematical aspects of fluid-multiferroic solid interaction problems. Math Meth Appl Sci. 2021;44(12):9727--9745], where the fluid-solid interaction problems for elliptic pseudo-oscillation equations associated with the above mentioned generalized thermo-electromagneto- elasticity theory are studied by the potential method and the theory of pseudodifferential equations. [ABSTRACT FROM AUTHOR]

The 2023 Mathematical Contest in Modeling (MCM) results are presented in this article. The contest attracted a large number of teams from around the world, including both undergraduate and high school students. The teams worked on interdisciplinary modeling problems, such as species diversity, wildlife management, data analysis, and sailboat pricing. The article highlights the importance of proper citation and attribution, as some teams were disqualified for plagiarism. The text also includes information about the sponsors of the contest and the authors of the article, who are mathematics professors. [Extracted from the article]

A problem with applying machine learning for analyzing power system dynamics is the lack of specific datasets. In this realm, defining a strong methodology to obtain power system dynamic data is an important task prior to the application of any machine learning tool. This is particularly important considering the current growing research in the field of self-healing grids. Thus, this paper presents a well-defined stochastic methodology that can be used to generate dynamic data that can afterwards be analyzed using machine learning tools. The proposed method is based on a Monte Carlo simulation and this paper presents the procedure to perform it. [ABSTRACT FROM AUTHOR]

This paper explores efficient logic cell design strategies for ultralow-energy subthreshold operation in wearable biomedical systems. Traditional logic cell design approaches often lead to suboptimal efficiency due to unbalanced Pull-Up (PU) and Pull-Down (PD) networks. Various techniques are examined, including balanced PU/PD network design using body biasing and statistical distribution analysis of drain-source current. Moreover, the reverse channel (RSC) effect is investigated for device optimization. Single-stage gates such as inverters, NAND, and NOR gates are designed with Mentor Graphics tools to minimize power dissipation and area. Multistage gates like XOR and XNOR gates are also optimized for reduced signal propagation delay. A detailed analysis of D-flip-flop designs with varying transistor counts is presented. Power dissipation, delay, rise time, and fall time characteristics are compared for flip-flop configurations comprising 18, 10, and 5 transistors. The results indicate significant energy optimization with the proposed designs, particularly with the 5-transistor configuration, demonstrating the effectiveness of the proposed methodologies in achieving superior energy efficiency and reduced signal noise in biomedical systems. [ABSTRACT FROM AUTHOR]

For the case that corresponds to removal of smaller surcharge, HT ht in Fig. Considering the scatter in the HT ht data, 0.6-1.0, evident in Fig. 5(a) show that, on average, the HT ht values from the Arup study were about 1.5 times the HT ht values from the original paper. 1 of the original paper for estimating the equivalent preconsolidation stress, HT ht , is also not applicable. [Extracted from the article]

This study investigated rhetorical functions and their associated linguistic realizations in English review article abstracts. The analysis was based on a corpus of 100 English review article abstracts from linguistics and applied linguistics disciplines and followed a corpus-driven discourse analytic top-down approach. MAXQDA and WordSmith were used to code the moves and analyze their associated sub-corpora, respectively. The results of calculating range and frequency distributions showed that English conceptual review article abstracts use a rhetorical structure, different from that of research paper abstracts. This rhetorical organization is realized through a different set of moves, namely ) establishing the territory or area of study, 2) identifying the problem, 3) introducing the present research, 4) organizing the paper, and 5) concluding or reflecting. Moreover, each move was realized through a distinct set of sub-moves. In terms of range, the moves dealing with purpose and structure were the most widely present moves; in terms of frequency distribution, the move dealing with structure was the most frequent. Furthermore, the highly frequent use of plural self-mentions indicates that in review article abstracts, the emphasis is on research as a group activity inclusive of the researcher(s) and objects of study. In addition, the presence of ‘establishing the territory’, together with ‘identifying the problem’ can be seen as an attempt to sell the research. Finally, the prevalent use of the five moves showed that most review article abstracts are indicative-informative in function. [ABSTRACT FROM AUTHOR]

This paper is devoted to the experimental quantitative characterization of the shape and orientation distribution of ice particles in clouds. The characterization is based on measured and modeled elevation dependencies of the polarimetric parameters differential reflectivity and correlation coefficient. The polarimetric data is obtained using a newly developed 35 GHz cloud radar MIRA-35 with hybrid polarimetric configuration and scanning capabilities. The full procedure chain of the technical implementation and the realization of the setup of the hybrid-mode cloud radar for the shape determination are presented. This includes the description of phase adjustments in the transmitting paths, the introduction of the general data processing scheme, correction of the data for the differences of amplifications and electrical path lengths in the transmitting and receiving channels, the rotation of the polarization basis by 45°, the correction of antenna effects on polarimetric measurements, the determination of spectral polarimetric variables, and the formulation of a scheme to increase the signal-to-noise ratio. Modeling of the polarimetric variables is based on existing back-scattering models assuming the spheroidal representation of cloud scatterers. The parameters retrieved from the model are polarizability ratio and degree of orientation, which can be assigned to certain particle orientations and shapes. The developed algorithm is applied to a measurement of the hybrid-mode cloud radar taken on 20 October 2014 in Cabauw, the Netherlands, in the frame of the ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) campaign. The case study shows the retrieved polarizability ratio and degree of orientation of ice particles for a cloud system of three cloud layers at different heights. Retrieved polarizability ratios are 0.43, 0.85, and 1.5 which correspond to oblate, quasi-spherical, and columnar ice particles, respectively. It is shown that the polarizability ratio is useful for the detection of aggregation/riming processes. The orientation of oblate and prolate particles is estimated to be close to horizontal while quasi-spherical particles were found to be more randomly oriented. [ABSTRACT FROM AUTHOR]

The growth rate I r i , another widely used epidemic metric throughout the COVID-19 pandemic, represents the rate at which the epidemic is growing during the exponential phase of epidemic growth. The effective reproduction number I R i has been a headline epidemic metric since the onset of the COVID-19 pandemic. In fact within models, I r i and I R i are related via the generation time of the epidemic: the longer the generation time and higher the epidemic growth rate, higher the value of I R i . [Extracted from the article]

Given the importance of estimating the generation time for COVID-19, it is regrettable that too little attention has been paid to the challenging problem of designing data collection directly targeting the generation time in different strata. As explained in Parag et al. and several other speakers in the meeting, the probability distribution of the generation time, that is the time between infection of a primary case and infection of secondary cases, is a crucial piece of epidemiological information for understanding the dynamics of any infectious disease, which is needed in much of the modelling work, for example for deriving R SB t sb and for linking R SB t sb to the growth rate. (2020) Estimation of incubation period and generation time based on observed length-biased epidemic cohort with censoring for COVID-19 outbreak in China. [Extracted from the article]

Statistical methods have a long history in the analysis of hydrological data for designing, planning, infilling, forecasting, and specifying better models to assess scenarios of land use and climate change in catchments. Among them, the first two papers [[7]] are mainly about model selection and parameter estimation in frequency analysis under stationary conditions, and the last three papers [[9], [11]] mainly focus on frequency analysis under nonstationary conditions caused by changing environment. A case study in Jiangsu province, China shows that the frequency estimations based on this revised regional frequency analysis are in good agreement with observations. 10.3390/w14081261 7 Shao Y., Zhao J., Xu J., Fu A., Wu J. Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China. [Extracted from the article]

Recent advances in computation technology for decision/simulation and uncertainty analyses have revived interest in the triangular distribution and its use to describe uncertainty of bounded input phenomena. The trapezoidal distribution is a generalization of the triangular distribution that allows for the specification of the modal value by means of a range of values rather than a single point estimate. Whereas the trapezoidal and the triangular distributions are restricted to linear geometric forms in the successive stages of the distribution, the generalized trapezoidal (GT) distribution allows for a nonlinear behavior at its tails and a linear incline (or decline) in the central stage. In this paper we develop two novel elicitation procedures for the parameters of a special case of the GT family by restricting ourselves to a uniform (horizontal) central stage in accordance with the central stage of the original trapezoidal distribution. [ABSTRACT FROM AUTHOR]

The present study introduces a quadtree decomposition approach for assessing surface structures. The calculated quadtree variables like the number of blocks (nQT), the mean block size (mQT) and the standard deviation of the block sizes (sQT) seem to be suitable surface descriptors for quantifying the structural changes of the paper surface due to, e.g. production or printing conditions. The graphical representation of the quadtree decomposition is also intuitive and easy to interpret, thus giving a unique opportunity to observe and track structural changes locally. [ABSTRACT FROM AUTHOR]

Discusses the multivariate extreme value theory that emerged in several studies in the 1950s during the popularity of asymptomatic distributions in probability. Examination on the mathematical abstraction of the formulation of the theory; Discovery of the deficiencies of this appraoch during the 1990s; Information on proposed correction to the deficiencies of the approach.

In this paper, we derive the asymptotic distributions of AugmentedDickey-Fuller (ADF) tests under very mild conditions. The tests were originally proposed and investigated by Said and Dickey (1984) for testing unit roots in finite-order ARMA models with i.i.d, innovations, and are based on a finite AR process of order increasing with the sample size. Our conditions are significantly weaker than theirs. In particular, we allow for general linear processes with martingale difference innovations, possibly having conditional heteroskedasticities. The linear processes driven by ARCH type innovations are thus permitted. The range for the permissible increasing rates for the AR approximation order is also much wider. For the usual t-type test, we only require that it increase at order o(n[sup ½]) while they assume that it is of order o(n[sub K]) for some k satisfying 0 < k ≤ 1/3. [ABSTRACT FROM AUTHOR]

Purpose: Stimulated by previous reference, which proposed making straight line of regression to test gear gravimetric wear loss sequence distribution, this paper aims to propose using straight line of regression to fit gear gravimetric wear loss sequence based on stationary random process suppose. Faced to that the stationary random sequence suppose had not been proved by previous reference, and that prediction did not present high precision, this paper proposes a method of fitting non-stationary random process probability distribution function. Design/methodology/approach: Firstly, this paper proposes using weighted sum of Gauss items to fit zero-step approximate probability density. Secondly, for the beginning, this paper uses the method with few Gauss items under low precision. With the amount of points increasing, this paper uses more Gauss items under higher precision, and some Gauss items and some former points are deleted under precision condition. Thirdly, for particle swarm optimization with constraint problem, this paper proposed improved method, and the stop condition is under precision condition. Findings: In experiment data analysis section, gear wear loss prediction is done by the method proposed by this paper. Compared with the method based on the stationary random sequence suppose by prediction relative error, the method proposed by this paper lowers the relative error whose absolute values are more than 5%, except when the current point sequence number is 2, and retains the relative error, whose absolute values are lower than 5%, still lower than 5%. Originality/value: Finally, the method proposed by this paper based on non-stationary random sequence suppose is proved to be the better method in gear gravimetric wear loss prediction. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

Over the last few decades, stochastic thermodynamics has emerged as a framework to study the thermodynamics of small-scaled systems. The relation between entropy production and precision is one of the most prominent research topics in this field. In this paper, I answer the question how much dissipation is needed to follow a pre-determined trajectory. This will be done by deriving a trade-off relation between how precisely a mesoscopic system can follow a pre-defined trajectory and how much the system dissipates. In the high-precision limit, the minimal amount of dissipation is inversely proportional to the expected deviation from the pre-defined trajectory. Furthermore, I will derive the protocol that maximizes the precision for a given amount of dissipation. The optimal time-dependent force field is a conservative energy landscape which combines a shifted version of the initial energy landscape and a quadratic energy landscape. The associated time-dependent probability distribution conserves its shape throughout the optimal protocol. Potential applications are discussed in the context of bit erasure and electronic circuits. Controlling non-equilibrium systems generally incurs a thermodynamic cost. This paper introduces a trade-off relation between the precision at which one can control a system and the entropy production. [ABSTRACT FROM AUTHOR]

Males and females often desynchronise their mating activity during the breeding season (intersexual phenological asynchrony). This suggests that the two sexes differentially use environmental cues to initiate seasonal reproduction, and climate change may cause phenological shifts in a sex‐specific manner, subsequently altering frequency distribution of mating attempts over time and influencing the emergence timing and recruitment success of juveniles. In this article, we tested these hypotheses through literature reviews and theoretical modelling. We found that the sex specificity of environmental cues and phenological shifts remain poorly understood in most sexually reproducing animal taxa because of sex‐specific publication bias and data limitations. Meanwhile, our theoretical model predicted that the ecological consequences of phenological shifts are sexually asymmetric, with earlier mating onset of males having generally greater negative effects on population growth than that of females. We therefore suggest that further research is necessary to collect and incorporate sex‐specific information on seasonal reproduction into the theory of phenological mismatch, for which previous work has mostly focused on interspecific trophic asynchrony. We also outline a future research agenda to better understand the consequences of sex‐specific phenological shifts on seasonal population dynamics and long‐term persistence of species. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of moment closures as a possible path to future quantum hydrodynamics models. Moment closures are known to produce accurate predictions of continuum and non-equilibrium classical gas flows while offering modeling and numerical advantages over other commonly used methods. The maximum-entropy hierarchy of moment closures holds the promise of robustly hyperbolic stable moment equations, however, the predicted distribution function in phase space is positive by design, which is in disagreement with the quasi-distribution function described by the Wigner equation. In this paper, predictions made by an interpolative one-dimensional five-moment system are compared to direct numerical solutions of the Wigner equation for a simple low-energy quantum state in unbounded doublewell potentials. Numerical solutions for a particle in various potentials described by quartic polynomials are presented. The capacity of moment methods to provide accurate and efficient models for quantum systems is explored. [ABSTRACT FROM AUTHOR]

The weathering of rocks influences the geochemistry of the oceans, the erosion of landscapes and manmade structures, and even the global climate. Although a high degree of variance is often observed in rate measurements, little is understood about the statistical characteristics of weathering rate distributions. This preliminary study demonstrates that the weathering rates of limestone, determined from measurements of an ancient eroded limestone edifice, can exhibit highly non-Gaussian behavior. While a Gaussian model produced a poor fit with the data, an alternative model - the generalized extreme value (GEV) framework - was capable of capturing the asymmetric long tailed distribution, in good agreement with the measured curve. Furthermore, the non-Gaussian distribution of these field rates was found to have similar characteristics to the distribution of rates measured over much smaller microscopic regions of limestone surfaces in laboratory experiments. Such similar behavior could be indicative of analogous chemical and mechanical weathering processes acting over a range of different spatial and temporal scales. Moreover, highly asymmetric rate distributions with high variance could be characteristic of rates not only in carbonate rocks, but in other rock types too, suggesting that the use of a small number of measurements to determine field weathering rates may be insufficient to fully characterize the range of rates in natural systems. [ABSTRACT FROM AUTHOR]

This special issue of Applied Stochastic Models in Business and Industry contains 10 papers that were presented at the 36th ASA Quality and Productivity Research Conference QPRC-2019. The author builds upon this algorithm to create a second procedure which analyzes data from multiple periodic processes and then deinterleave the processes. DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no new data were created or analyzed in this study. [Extracted from the article]

The efficiency of material supply for workstations using Automatic Guided Vehicles (AGVs) is largely determined by the performance of the AGV dispatching scheme. This paper proposes a new solution approach for the AGV dispatching problem (AGVDP) for material replenishment in a general manufacturing workshop where workstations are in a matrix layout, and where uncertainty in replenishment time of workstations and stochastic unloading efficiencies of AGVs are dynamic contextual factors. We first extend the literature proposing a mixed integer optimisation model with a delivery satisfaction soft constraint of material orders and two objectives: transportation costs and delivery time deviation. We then develop a new knowledge-guided estimation of distribution algorithm with delivery satisfaction evaluation for solving the model. Our algorithm fuses three knowledge-guided strategies to enhance optimisation capabilities at its respective execution stages. Comprehensive numerical experiments with instances built from a real-world scenario validate the proposed model and algorithm. Results demonstrate that the new algorithm outperforms three popular multi-objective evolutionary algorithms, a discrete version of a recent multi-objective particle swarm optimisation, and a multi-objective estimation of distribution algorithm. Findings of this work provide major implications for workshop management and algorithm design. [ABSTRACT FROM AUTHOR]

Power systems with a high proportion of inverter-based sources like photovoltaics require a substantial short-circuit current ratio to ensure strong voltage support capabilities. However, this also increases the system's short-circuit current capacity and levels, which may potentially affect the safe operation of system equipment and current-carrying conductors. To evaluate the operational risks, this paper proposes a quantitative calculation model for the critical integration proportion of grid-connected inverter-interfaced power sources based on short-circuit current over-limit probability. Firstly, according to the verification criterion about short-circuit current during the selection of the switching equipment and conductors in the power system, the short-circuit current over-limit probability evaluation system with five indices is established. Secondly, considering the impact of the increased grid integration proportion of inverter-based power sources on short-circuit currents, an evaluation process for operational risk probabilities is proposed. Based on this, the critical access proportion for inverter-based power source integration in the power grid can be calculated. Finally, the proposed model is used to assess the operational risk probability for a 220 kV system which includes a high proportion of inverter-based power sources in the Anhui area, China. Through the analysis of cumulative probability distribution curves, the critical grid integration proportion interval for the transition from normal to high proportion stages of inverter-based power sources is determined. [ABSTRACT FROM AUTHOR]

Droplet microfluidics is a highly sensitive and high-throughput technology extensively utilized in biomedical applications, such as single-cell sequencing and cell screening. However, its performance is highly influenced by the droplet size and single-cell encapsulation rate (following random distribution), thereby creating an urgent need for quality control. Machine learning has the potential to revolutionize droplet microfluidics, but it requires tedious pixel-level annotation for network training. This paper investigates the application software of the weakly supervised cell-counting network (WSCApp) for video recognition of microdroplets. We demonstrated its real-time performance in video processing of microfluidic droplets and further identified the locations of droplets and encapsulated cells. We verified our methods on droplets encapsulating six types of cells/beads, which were collected from various microfluidic structures. Quantitative experimental results showed that our approach can not only accurately distinguish droplet encapsulations (micro-F1 score > 0.94), but also locate each cell without any supervised location information. Furthermore, fine-tuning transfer learning on the pre-trained model also significantly reduced (>80%) annotation. This software provides a user-friendly and assistive annotation platform for the quantitative assessment of cell-encapsulating microfluidic droplets. [ABSTRACT FROM AUTHOR]

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one's behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service. [ABSTRACT FROM AUTHOR]

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode. Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line. Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, numerous large-scale workflow are executed in the cloud environment. Therefore, the workflow scheduling in cloud environment has become an emerging topic. This paper focuses on a reliability constrained multi-objective workflow scheduling problem (RCMOWSP) with the objectives of minimum execution cost and time. To solve the RCMOWSP, this paper proposes a knowledge-based multi-objective estimation of distribution algorithm (KMOEDA) with several problem-specific operators. First, an idle time-based decoding scheme is applied to sort the permutation of tasks greedily. In the global search strategy, a probability model is constructed to improve the diversity of population. Based on the problem-specific knowledge, a reliability-aware local search strategy is designed to performs local search around the solutions that violate reliability constraint. An elite enhancement strategy with a task perturbation operator and a resource perturbation operator is introduced to further improve the elite non-dominated solutions in the external archive. A comprehensive experiment is conducted to verify the performance of KMOEDA. The comparative results show that the KMOEDA significantly outperforms several relative multi-objective workflow scheduling approaches in solving the RCMOWSP. [ABSTRACT FROM AUTHOR]

Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)‐based video steganalysis algorithms extract feature sets directly from the MVs, but ignoring the embedding operation may perturb the statistical distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11‐dimensional feature set to detect MV‐based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacities. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios. [ABSTRACT FROM AUTHOR]