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Unmanned aerial vehicles (UAVs) have become widespread, raising concerns about security and privacy, making real-time detection of "low, slow, and small" UAVs crucial. Traditional detection systems, such as those based on radio frequency and radar, have limitations. This paper addresses challenges like low detection accuracy, high false alarm rates, and susceptibility to interference by proposing a novel detection method combining background difference and the lightweight Fast-YOLOv7 network(F-YOLOv7). The method first identifies potential UAV targets through background difference preprocessing, then optimizes model parameters with the Faster module to enhance speed and accuracy. Experiments on the DUT Anti-UAV dataset achieved a detection accuracy of 98.8%, outperforming the original YOLOv7 model’s 86.71%. This approach meets the real-time and precise detection requirements for low-altitude UAVs. [ABSTRACT FROM AUTHOR]

Serverless computing has attracted many researchers with features such as scalability and optimization of operating costs, no need to manage infrastructures, and build programs at a higher speed. Serverless computing can be used for real-time machine learning (ML) prediction using serverless inference functions. Deploying an ML serverless inference function involves building a compute resource, deploying an ML model, network infrastructure, and permissions to call the inference function. However, the subject of machine learning inference (MLI) has challenges such as resource management, delay and response time, large and complex models, and security and privacy, not many studies have been conducted in this field. This comprehensive literature review article examines the recent developments in MLI in serverless computing environments. The mechanisms presented in the taxonomy can be summarized in four categories: service level objective SLO-aware, acceleration-aware, framework-aware, and latency-aware. In each category, different methods and algorithms used to optimize inference in serverless environments have been examined along with their advantages and disadvantages. We show that acceleration-aware methods focus on the optimal use of computing resources, and framework-aware methods play an important role in improving system efficiency and scalability by examining different frameworks for inference in serverless environments. Also, SLO-aware and Latency-aware methods, considering time limits and service level agreement, help provide quality and reliable inference in serverless environments. Finally, this article presents a vision of future challenges and opportunities in this field and provides solutions for future research in the field of MLI in serverless. [ABSTRACT FROM AUTHOR]

Zero-inflated regression models are generally accepted for modeling data with inflated amounts of zeros. The principle used in constructing the distribution for these models has been extended to modeling certain types of data with inflation at the upper bound of the support. An excellent example of this is the zero and κ-inflated truncated Poisson regression model which utilizes the Poisson distribution. However, the equi-dispersion nature of the Poisson distribution makes the model less suitable for data that are under-dispersed and over-dispersed. Models based on exponentiated exponential geometric distribution (EEGD) are presented as alternatives for modeling any count data with an upper bound, irrespective of the nature of dispersion. The models are obtained by truncating the EEGD at the top and inflating at either or both zero and κ. A simulation study is conducted to examine the performances of the models under certain conditions. The result shows that the closer the dispersion parameter c is from the equi-dispersion line, the better the estimates for over-dispersed data, while the farther the dispersion parameter from the equi-dispersed line, the better the estimates for an under-dispersed model. The application of the models is illustrated using data from the Behavioral Risk Factor Surveillance System 2011. [ABSTRACT FROM AUTHOR]

This article introduces HODLR3D, a class of hierarchical matrices arising out of N-body problems in three dimensions. HODLR3D relies on the fact that certain off-diagonal matrix sub-blocks arising out of the N-body problems in three dimensions are numerically low rank. For the Laplace kernel in 3D, which is widely encountered, we prove that all the off-diagonal matrix sub-blocks are rank deficient in finite precision. We also obtain the growth of the rank as a function of the size of these matrix sub-blocks. For other kernels in three dimensions, we numerically illustrate a similar scaling in rank for the different off-diagonal sub-blocks. We leverage this hierarchical low-rank structure to construct HODLR3D representation, with which we accelerate matrix-vector products. The storage and computational complexity of the HODLR3D matrix-vector product scales almost linearly with system size. We demonstrate the computational performance of HODLR3D representation through various numerical experiments. Further, we explore the performance of the HODLR3D representation on distributed memory systems. HODLR3D, described in this article, is based on a weak admissibility condition. Among the hierarchical matrices with different weak admissibility conditions in 3D, only in HODLR3D did the rank of the admissible off-diagonal blocks not scale with any power of the system size. Thus, the storage and the computational complexity of the HODLR3D matrix-vector product remain tractable for N-body problems with large system sizes. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology utilizing physics-informed neural network (PINN) as a junction condition for a 1D network model of blood flow in total cavopulmonary connection generated by the Fontan procedure. The technique integrates a 3D mesh generation process based on the parameterization of the junction geometry, along with a sophisticated physically regularized neural network architecture. Synthetic datasets are produced using 3D steady Stokes simulations within fixed boundaries. We use a physically informed feedforward neural network that utilizes a physically regularized loss function, which incorporates the principle of mass conservation. Our PINN achieves a tolerance of 6% on the test set. We develop a 1D-PINN multiscale model based on a previously developed method for multiscale 1D–3D simulations. Comparison with 1D–3D Stokes based model and 3D Navier–Stokes based model verifies the 1D-PINN model. In the first and second comparison, the maximum deviations of the averaged pressures and flows do not exceed 1.48% and 12.26%, respectively. [ABSTRACT FROM AUTHOR]

Serverless computing is one of the latest technologies that has received much attention from researchers and companies in recent years since it provides dynamic scalability and a clear economic model. Serverless computing enables users to pay only for the time they use resources. This approach has several benefits, including optimizing costs and resource utilization; however, cold starts are a concern and challenge. Various studies have been conducted in the academic and industrial sectors to deal with this problem, which poses a significant research challenge. This paper comprehensively reviews recent cold start research in serverless computing. Hence, this paper presents a detailed taxonomy of several serverless computing strategies for dealing with cold start latency. We have considered two main approaches in the proposed classification: Optimizing Loading Times (OLT) and Optimizing Resource Usage (ORU), each including several subsets. The subsets of the primary approach OLT are divided into container-based and checkpoint-based. Also, the primary approach ORU is divided into machine learning (ML)-based, optimization-based, and heuristic-based approaches. After analyzing current methods, we have categorized and investigated them according to their characteristics and commonalities. Additionally, we examine potential challenges and directions for future research. [ABSTRACT FROM AUTHOR]

Nitrogen is one of the most abundant chemical elements on the Earth and plays an important role in global environmental change. Leading Earth system models include coupled carbon and nitrogen cycle modules of varying complexity, but the INM RAS climate model family has not yet included an explicit N-cycle description. This paper presents a parameterization of the terrestrial N-cycle based on a simplification of the JULES-CN model, adapted for coupled use with the INM-CM land C-cycle module. Numerical simulations were carried out with a standalone carbon cycle model with nitrogen feedback disabled and enabled versions for the period 1850–2100. The simulated global pools show good agreement with results of other models with an implemented N-cycle. Taking into account the N-limitation of the C-cycle, the modelled dynamics of total carbon storage in terrestrial ecosystems from 1850 to the mid-20th century is specified. [ABSTRACT FROM AUTHOR]

A numerical method for coupled 3D-1D problems with discontinuous solutions at the interfaces is derived and discussed. This extends a previous work on the subject where only continuous solutions were considered. Thanks to properly defined function spaces a well posed 3D-1D problem is obtained from the original fully 3D problem and the solution is then found by a PDE-constrained optimization reformulation. This is a domain decomposition strategy in which unknown interface variables are introduced and a suitably defined cost functional, expressing the error in fulfilling interface conditions, is minimized constrained by the constitutive equations on the subdomains. The resulting discrete problem is robust with respect to geometrical complexity thanks to the use of independent discretizations on the various subdomains. Meshes of different sizes can be used without affecting the conditioning of the discrete linear system, and this is a peculiar aspect of the considered formulation. An efficient solving strategy is further proposed, based on the use of a gradient based solver and yielding a method ready for parallel implementation. A numerical experiment on a problem with known analytical solution shows the accuracy of the method, and two examples on more complex configurations are proposed to address the applicability of the approach to practical problems. [ABSTRACT FROM AUTHOR]

Many researchers have tried to predict the impact of the COVID-19 outbreak on morbidity, in order to help policy-makers find optimal isolation policies. However, despite the development and use of many models and sophisticated tools, these forecasting attempts have largely failed. We present a model that considers the severity of the disease and the heterogeneity of contacts between the population in complex space–time dynamics. Using mathematical and computational methods, the applied tool was developed to analyze and manage the COVID-19 pandemic (from an epidemiological point of view), with a particular focus on population heterogeneity in terms of age, susceptibility, and symptom severity. We show improved strategies to prevent an epidemic outbreak. We evaluated the model in three countries, obtaining an average mean square error of 0.067 over a full month of the basic reproduction number (R0). The goal of this study is to create a theoretical framework for crisis management that integrates accumulated epidemiological considerations. An applied result is an open-source program for predicting the outcome of an isolation strategy for future researchers and developers who can use and extend our model. [ABSTRACT FROM AUTHOR]

Consider a source detection problem for a diffusion system at its stationary status, which is stated as the inverse source problem for an elliptic equation from the measurement of the solution specified only in part of the domain. For this linear ill-posed problem, we propose to reconstruct the interior source applying neural network algorithm, which projects the problem into a finite-dimensional space by approximating both the unknown source and the corresponding solution in terms of two neural networks. By minimizing a novel loss function consisting of PDE-fit and data-fit terms but without the boundary condition fit, the modified deep Galerkin method (MDGM) is applied to solve this problem numerically. Based on the stability result for the analytic extension of the solution, we strictly estimate the generalization error caused by the MDGM algorithm employing the property of conditional stability and the regularity of the solution. Numerical experiments show that we can obtain satisfactory reconstructions even in higher-dimensional cases, and validate the effectiveness of the proposed algorithm for different model configurations. Moreover, our algorithm is stable with respect to noisy inversion input data for the noise in various structures. [ABSTRACT FROM AUTHOR]

Diarrhea is one of the most common infectious diseases that affect people of all ages and is a serious public health concern around the world. The main causes of diarrhea include food quality, water, indoor meteorological, and outdoor meteorological conditions. In this study, a dew computing-assisted smart monitoring framework is developed to evaluate the relationship among the health, indoor meteorological, and food factors of an individual to predict the cause of diarrhea with the scale of severity. Smart sensors are utilized at the physical layer to collect the targeted parameters of health, indoor meteorological, and food of the individual. The captured events are classified at the cyber layer by utilizing the Probabilistic Weighted-Naïve Bayes (PW-NB) classification approach for quantifying abnormal health events. Furthermore, a Multi-scale Gated Recurrent Unit (M-GRU) is suggested to obtain the scale of severity by analyzing the correlation between irregular health, food, and environmental events. In this manner, the proposed model M-GRU has achieved a high precision value of ( 93.26 % ), whereas, LSTM, RNN, SVM achieved the precision value of ( 89.13 % ), ( 90.43 % ), ( 88.23 % ), respectively. In addition, the precision value of the PW-NB is ( 97.15 % ), which is also higher as compared to KNN ( 93.25 % ) and DT ( 96.91 % ). The outcome of the proposed solutions is shown the higher Precision values on dew computing and cloud computing. Moreover, a comparative analysis defines the prediction effectiveness of the proposed solution over several other decision-making solutions with regards to event classification, severity determination, monitoring stability, and prediction efficiency. [ABSTRACT FROM AUTHOR]

Many studies have considered mixed Poisson distributions as alternatives for fitting count data with overdispersion. However, in some cases, the data have an abundance of zeros and ones which makes modelling using distributions with no consideration to the inflated values less desirable. This study aims to introduce a new distribution for count data with inflated values at zero and one, known as zero–one-inflated Poisson–Lindley distribution. The statistical properties of the proposed distribution are discussed. Furthermore, the maximum likelihood and method of moments for the parameters of the proposed distribution are developed. A simulation study is conducted to investigate the performance of the zero–one-inflated Poisson–Lindley distribution in describing overdispersed data with excess zeros and ones by changing the proportion of zeros and ones in the data. It is found that the fitting of the zero–one-inflated Poisson–Lindley distribution always gives a larger log-likelihood value than the fitting of the zero–one-inflated Poisson distribution. The results from the applications of the real datasets with an overdispersed property as well as a large number of ones and zeros conclude that the proposed distribution provides the best fit compared to other contending distributions in the study. [ABSTRACT FROM AUTHOR]

The paper discusses the strategy-proofness of sports tournaments with multiple group stages, where the results of matches already played in the previous round against teams in the same group are carried over. These tournaments, widely used in handball and other sports, are shown to be incentive incompatible in the sense that a team can be strictly better off by not exerting full effort in a game. Historical examples are presented when a team was ex ante disinterested in winning by a high margin. We propose a family of incentive compatible designs. Their main characteristics are compared to the original format via simulations. Carrying over half of the points scored in the previous round turns out to be a promising policy. [ABSTRACT FROM AUTHOR]

The paper is devoted to one possible way of the model construction for the stationary Gaussian process with given accuracy and reliability in functional space C ⁢ ([ 0 , T ]) . [ABSTRACT FROM AUTHOR]

In this paper, we present a method for the accurate estimation of the derivative (aka. sensitivity) of expectations of functions involving an indicator function by modifying a (stochastic) algorithmic differentiation, replacing the derivative of the indicator function with a suitable operator. We show that we can split this operator into a conditional expectation operator and the density. This allows using different or improved numerical approximation methods for these operators, e.g. regression. The method is an improvement of the approach presented in C.P. Fries [Automatic backward differentiation for American Monte-Carlo algorithms (conditional expectation). Risk, April, 2018; Stochastic automatic differentiation: Automatic differentiation for Monte-Carlo simulations, Quant. Finance 19(6) (2019), pp. 1043–1059]. The finite difference approximation of a partial derivative of a Monte-Carlo integral of a discontinuous function is known to exhibit a high Monte-Carlo error. The issue is evident since the Monte-Carlo approximation of a discontinuous function is just a finite sum of discontinuous functions and as such, not even differentiable. The algorithmic differentiation of a discontinuous function is problematic. A natural approach is to replace the discontinuity by continuous functions. This is equivalent to replacing a path-wise automatic differentiation by a (local) finite difference approximation. The decoupling of the integration of the Dirac delta and the remaining conditional expectation introduced here results in an improvement in terms of variance reduction and implementation design. [ABSTRACT FROM AUTHOR]

Heterogeneous systems providing diverse computational capabilities have unlocked a new pathway in multicore processors. The versatility in applications and their ever-increasing performance demands have brought a paradigm shift to the heterogeneous systems. We use a deep neural network (DNN) based model to maximize performance under power constraints in heterogeneous systems. The dynamic power management technique is implemented in three stages. In the first stage, the core statistics and workload characteristics are collected for the DNN training at the later stage. This step dynamically estimates workload change for the current epoch using dynamic voltage frequency scaling (DVFS) based heuristic algorithm. In the second stage, DNN is trained through collected data points. The third stage uses the trained DNN model to identify a suitable voltage-frequency values to maximize performance under power capping. The power manager controls power-consumption at both per-core and per-chip levels. Our DNN prediction model is trained to address both core types (Large and Small) thus, improving the accuracy of the model. Simulations indicate that the proposed approach can achieve an overall 10.63% reduction in power-consumption and 5.5–6.8% improvement in power-savings when compared with the existing approaches. Besides, the proposed DNN model-based approach is able to maintain power capping with 95.81% accuracy with performance degradation of only 5.38% for a quad-core architecture. [ABSTRACT FROM AUTHOR]

A new discretization approach is presented for the simulation of flow in complex poro-fractured media described by means of the Discrete Fracture and Matrix Model. The method is based on the numerical optimization of a properly defined cost-functional and allows to solve the problem without any constraint on mesh generation, thus overcoming one of the main complexities related to efficient and effective simulations in realistic DFMs. [ABSTRACT FROM AUTHOR]

A three-dimensional mathematical model of the diffusion of impurities from a short-term source taking into account meteorological conditions is presented. The model includes the system of equations of hydrothermodynamics describing regional atmospheric processes. The transfer of multicomponent gas impurities is calculated taking into account microphysical processes, precipitation, and fog. Initial information for model initialization is aerological information in the form of actual or forecast fields of meteorological parameters and characteristics of sources of impurities. A numerical analysis of the diffusion of impurities in the near zone of a cosmodrome from short-term sources such as engines of launch vehicles was performed for various meteorological parameters, including wind in the atmosphere. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional, nonstationary numerical model of convective clouds is presented. Hydrothermodynamical, microphysical, and electrical processes are described in detail; results of studies of changes in thermodynamical, microstructural, and electrical parameters in time are given. The hydrothermodynamical block of the model consists of equations of motion that describe the wet convection in the Boussinesq approximation. The equations of the microphysical block describe the processes of nucleation, condensation, coagulation, sublimation, accretion, aggregation, and freezing of drops, deposition of cloud particles in the gravity field, their transport by air flows, and interaction of cloud particles under the influence of electric fields of clouds. Numerical experiments based on the model developed were carried out to study the formation of convective clouds for various stratifications of the atmosphere and the structure of the wind field in the atmosphere. Thermodynamical and microstructural parameters in the zone of a powerful convective cloud are determined. The system of equations of the model was solved by the methods of splitting in physical processes and component-wise splitting. [ABSTRACT FROM AUTHOR]

For solving the inverse Cauchy problem of a three-dimensional (3D) non-homogeneous sideways heat equation in a cuboid, we first transform it into a partial homogeneous boundary value problem, using a time-dependent two-dimensional (2D) homogenization technique. After a time step-by-step approach of the inverse Cauchy problem, it becomes an inverse Cauchy problem of the 3D modified Helmholtz equation. A sequence of 2D sine functions and a set of second-order ordinary differential equations are derived to determine the expansion coefficients, which can be solved in closed-form. In order to stably solve the inverse Cauchy problem, we simply introduce a spring-damping regularization technique to modify the coefficients, which results in a regularized 2D Fourier sine series solution of the unknown Dirichlet data on the top of the cuboid. The novel method can find the solution in the whole domain, which is quite accurate with small errors, even a large noise up to 20 % is imposed on the given data. The present method together with the analyses of regularization parameter and the stability of solution is novel and shows promise in the 3D sideways problems of the heat equation. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel computational model for simulating the coffee-ring phenomenon. The proposed method is based on a phase-field model and Monte Carlo simulation. We use the Allen–Cahn equation with a pinning boundary condition to model a drying droplet. The coffee particles inside the droplet move according to a random walk function with a truncated standard normal distribution under gravitational force. We perform both two-dimensional and three-dimensional computational experiments to demonstrate the accurate simulation of the coffee-ring phenomenon by the proposed model. [ABSTRACT FROM AUTHOR]

Recent advances in GPUs opened a new opportunity in harnessing their computing power for general purpose computing. CUDA, an extension to C programming, is developed for programming NVIDIA GPUs. However, efficiently programming GPUs using CUDA is very tedious and error prone even for the expert programmers. Programmer has to optimize the resource occupancy and manage the data transfers between host and GPU, and across the memory system. This paper presents the basic architectural optimizations and explore their implementations in research and industry compilers. The focus of the presented review is on accelerating computational science applications such as the class of structured grid computation (SGC). It also discusses the mismatch between current compiler techniques and the requirements for implementing efficient iterative linear solvers. It explores the approaches used by computational scientists to program SGCs. Finally, a set of tools with the main optimization functionalities for an integrated library are proposed to ease the process of defining complex SGC data structure and optimizing solver code using intelligent high-level interface and domain specific annotations. [ABSTRACT FROM AUTHOR]

The dry spinning of fibers can be described by three-dimensional multi-phase flow models that contain key effects like solvent evaporation and fiber-air interaction. Since the direct numerical simulation of the three-dimensional models is in general not possible, dimensionally reduced models are deduced. We recently developed a one-two-dimensional fiber model and presented a problem-tailored numerical solution strategy in Wieland et al. (J Comput Phys 384:326–348, 2019). However, in view of industrial setups with multiple fibers spun simultaneously the numerical schemes must be accelerated to achieve feasible simulation times. The bottleneck builds the computation of the radial concentration and temperature profiles as well as their cross-sectionally averaged values. In this paper we address this issue and develop efficient numerical algorithms. [ABSTRACT FROM AUTHOR]

We give an explicit representation for the transition law of a tempered stable Ornstein–Uhlenbeck process and use it to develop a rejection sampling algorithm for exact simulation of increments from this process. Our results apply to general classes of both univariate and multivariate tempered stable distributions and contain a number of previously studied results as special cases. [ABSTRACT FROM AUTHOR]

In the progressive Type-II censoring with random removal, choosing a distribution for removing the number of unfailed units which is independent from the lifetime distribution, may be a serious challenge and lead to an unreliable inference. Two approaches based on the time distance between consecutive failure times are proposed to determine the removal numbers. Then, the joint probability mass functions of random removals are derived where the underlying distribution of the lifetime data is assumed to be an exponential. Furthermore, The expected test time is investigated under these two approaches and is subsequently considered as an optimality criterion for identifying progressive Type-II censoring schemes. Some statistical inference results are also obtained by using Monte Carlo simulations. The empirical results indicate that the proposed methods work perfectly well in the sense that, reduce the expected total time on the test. Finally, a numerical example, based on a real data set, is presented and analysed to illustrate the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents an improved method for applying smoothed particle hydrodynamics within a nested Lagrangian domain of fluid particles. In our previous implementation, ghost particles, generated by Poisson-disk sampling to enclose the region occupied by fluid particles, transferred necessary physical quantities from the outer domain to the fluid. Using this technique, the local fluid motion agreed with results simulated by SPH over the entire domain. However, the cost of generating ghost particles was burdensome. We propose herein a much less expensive, patch-based sampling method to generate ghost particles. In this new approach, the ghost particles are generated locally around each fluid particle and corresponding physical quantities are determined using the local ghost particles. Furthermore, we introduce a new formulation that determines the physical quantities of ghost particles from the outer domain, and transfers them to the local fluid particles. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic (CSEM) surveying becomes a widespread method for oil and gas exploration, which requires fast and efficient software for inverting large-scale EM datasets. In this context, one often needs to solve sparse systems of linear equations with a large number of sparse right-hand sides, each corresponding to a given transmitter position. Sparse direct solvers are very attractive for these problems, especially when combined with low-rank approximations which significantly reduce the complexity and the cost of the factorization. In the case of thousands of right-hand sides, the time spent in the sparse triangular solve tends to dominate the total simulation time, and here we propose several approaches to reduce it. A significant reduction is demonstrated for marine CSEM application by utilizing the sparsity of the right-hand sides (RHS) and of the solutions that results from the geometry of the problem. Large gains are achieved by restricting computations at the forward substitution stage to exploit the fact that the RHS matrix might have empty rows (vertical sparsity) and/or empty blocks of columns within a non-empty row (horizontal sparsity). We also adapt the parallel algorithms that were designed for the factorization to solve-oriented algorithms and describe performance optimizations particularly relevant for the very large numbers of right-hand sides of the CSEM application. We show that both the operation count and the elapsed time for the solution phase can be significantly reduced. The total time of CSEM simulation can be divided by approximately a factor of 3 on all the matrices from our set (from 3 to 30 million unknowns, and from 4 to 12 thousands RHSs). [ABSTRACT FROM AUTHOR]

Variable fractional order Riccati equation is solved to get the exact solution by mixing Sumudu transform and Adomain decomposition method. This procedure is powerful and takes short computations' as in the illustrative examples. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of record generation when records are taken from a normal population. We propose three new generation algorithms, compare their efficiency and find the most efficient algorithm (Algorithm 2.1). We then compare these algorithms with known generation algorithms presented in the work of Balakrishnan, So, and Zhu (2016). [ABSTRACT FROM AUTHOR]

The Magnus effect is responsible for deflecting the trajectory of a spinning baseball. The deflection at the end of the trajectory can be estimated by simulating some similar trajectories or by clustering real paths; however, previous to this study, there are no reports for a detailed connection between the initial throw conditions and the resulting deflection by using. The only approximation about this is the PITCHf/x algorithm, which uses the kinematics equations. In this work, deflections from simulated spinning throws with random linear and angular velocities and spin axis parallel to the horizontal plane are analyzed in their polar representation. A cardioid function is proposed to express the vertical deflection as response of the angular velocity. This is based on both theoretical arguments from the ball movement equations and from the numerical solution of such equations. We found that the vertical deflection fits a cardioid model as function of the Magnus coefficient and the spin angle, for a set of trajectories with initial linear velocities symmetrically distributed around the direction of motion. A variation of the model can be applied to estimate the radial deflection whereas an extended model should be explored for trajectories with velocities asymmetrically distributed. The model is suitable for many applications: from video games to pitching machines. In addition, the model approaches to the results obatined with the kinematic equations, which serves as validation of the PITCHf/x algorithm. [ABSTRACT FROM AUTHOR]

Continuous inkjet systems are commonly used to print expiry date labels for food products. These systems are designed to print on flat surfaces; however, a lot of food products package have a cylindrical shape (e.g., bottled and canned products) which causes an enlargement in characters at the ends of the label. In this work, we present an algorithm to correct this defect by calculating the extra-distance that an ink drop travels when the printing surface approaches an elliptic cylinder. Each charged ink drop is modeling as a solid particle which is affected by the air drag, Earth's gravitation, and voltage due to the electrical field that causes the perturbation in the ink drop path. Numerical results show the correction of the enlargement mentioned above by varying the electric field along the width of the label. In addition, the equation and the values of a second electric field to correct the printing's inclination caused by the method of the system's operation are presented. [ABSTRACT FROM AUTHOR]

The article deals with numerical solution of the laminar-turbulent transition. A mathematical model consists of the Reynolds-averaged Navier-Stokes equations, which are completed by the explicit algebraic Reynolds stress model (EARSM) of turbulence. The algebraic model of laminar-turbulent transition, which is integrated to the EARSM, is based on the work of Kubacki and Dick (Int. J. Heat Fluid Flow 58, 68–83, 2016) where the turbulent kinetic energy is split in to the small-scale and large-scale parts. The algebraic model is simple and does not require geometry data such as wall-normal distance and all formulas are calculated using local variables. A numerical solution is obtained by the finite volume method based on the HLLC scheme and explicit Runge-Kutta method. [ABSTRACT FROM AUTHOR]

The article deals with the numerical simulation of unsteady flows through the turbine part of the turbocharger. The main focus of the article is the extension of the in-house CFD finite volume solver for the case of unsteady flows in radial turbines and the coupling to an external zero-dimensional model of the inlet and outlet parts. In the second part, brief description of a simplified one-dimensional model of the turbine is given. The final part presents a comparison of the results of numerical simulations using both the 3D CFD method and the 1D simplified model with the experimental data. The comparison shows that the properly calibrated 1D model gives accurate predictions of mass flow rate and turbine performance at much less computational time than the full 3D CFD method. On the other hand, the more expensive 3D CFD method does not need any specific calibration and allows detailed inspections of the flow fields. [ABSTRACT FROM AUTHOR]

To thoroughly test and validate algorithms and systems of autonomous vehicles, a large number of vehicles, many tests and a multitude of datasets are needed. This way of developing and testing is difficult, expensive and sometimes even dangerous. To combine the benefits of real world testing with the scalability and lower cost of simulation based testing, we present a novel methodology for a real-time hybrid simulator that is capable of handling real and simulated vehicles simultaneously with full interaction, in real time. We validated our methodology by assessing its overall performance and real-time capabilities using an F1/10 scale vehicle. The results effectively show the viability of this approach for validation of autonomous vehicles in a cost-efficient and safe manner. [ABSTRACT FROM AUTHOR]

The focus of this paper is to develop a new partial differential equation model for the pattern formation of the human pathogen Staphylococcus aureus, starting from a newly developed model of selected gene regulation mechanisms. In our model, we do not only account for the bacteria densities and nutrient concentrations, but also for the quorum sensing and biofilm components, since they enable bacteria to coordinate their behavior and provide the environment in which the colony grows. To this end, we model the relevant gene regulation systems using ordinary differential equations and therefrom derive our evolution equations for quorum sensing and biofilm environment by time-scale arguments. Furthermore, we compare and validate our model and the corresponding simulation results with biological real data observations of Staphylococcus aureus mutant colony growth in the laboratory. We show that we are able to adequately display the qualitative biological features of pattern formation in selected mutants, using the parameter changes indicated by the gene regulation mechanisms. [ABSTRACT FROM AUTHOR]

We introduce a new approach to wildland fire spread modeling. We evolve a 3-D surface curve, which represents the fire perimeter on the topography, as a projection to a horizontal plane. Our mathematical model is based on the empirical laws of the fire spread influenced by the fuel, wind, terrain slope, and shape of the fire perimeter with respect to the topography (geodesic and normal curvatures). To obtain the numerical solution, we discretize the arising intrinsic partial differential equation by a semi-implicit scheme with respect to the curvature term. For the advection term discretization, we use the so-called inflow-implicit/outflow-explicit approach and an implicit upwind technique which guarantee the solvability of the corresponding linear systems by an efficient tridiagonal solver without any time step restriction and also the robustness with respect to singularities. A fast treatment of topological changes (splitting and merging of the curves) is described and shown on examples as well. We show the experimental order of convergence of the numerical scheme, we demonstrate the influence of the fire spread model parameters on a testing and real topography, and we reconstruct a simulated grassland fire as well. [ABSTRACT FROM AUTHOR]

Methane gas hydrates have increasingly become a topic of interest because of their potential as a future energy resource. There are significant economical and environmental risks associated with extraction from hydrate reservoirs, so a variety of multiphysics models have been developed to analyze prospective risks and benefits. These models generally have a large number of empirical parameters which are not known a priori. Traditional optimization-based parameter estimation frameworks may be ill-posed or computationally prohibitive. Bayesian inference methods have increasingly been found effective for estimating parameters in complex geophysical systems. These methods often are not viable in cases of computationally expensive models and high-dimensional parameter spaces. Recently, methods have been developed to effectively reduce the dimension of Bayesian inverse problems by identifying low-dimensional structures that are most informed by data. Active subspaces is one of the most generally applicable methods of performing this dimension reduction. In this paper, Bayesian inference of the parameters of a state-of-the-art mathematical model for methane hydrates based on experimental data from a triaxial compression test with gas hydrate-bearing sand is performed in an efficient way by utilizing active subspaces. Active subspaces are used to identify low-dimensional structure in the parameter space which is exploited by generating a cheap regression-based surrogate model and implementing a modified Markov chain Monte Carlo algorithm. Posterior densities having means that match the experimental data are approximated in a computationally efficient way. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss algorithms of generation of weak records. These generation algorithms are based on two different methods. In the case, when the inverse function for the underlying distribution function can be obtained explicitly, the corresponding generation algorithms are built on the inverse-transform method. In the case, when the inverse function cannot be obtained explicitly, the algorithms are based on the rejection method. Generation algorithms of our paper are supplied with illustrative examples. [ABSTRACT FROM AUTHOR]

In this article, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy-tailed alternative to the normal distribution.We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution. [ABSTRACT FROM AUTHOR]

Computer simulation for pedestrian dynamics is at the same time an application area in which research has completed its cycle and an active and lively research context in which contributions from different disciplines still produce advancements on the state of the art. The study of effects of the presence of groups in the simulated population is object of growing interest in the community. While previous results have started to investigate the phenomenon, implying conflicting tendencies for pedestrians, mechanisms for the flexible management of cohesion among group members are still investigated both in continuous and discrete modelling efforts. The present effort is aimed at extending previous modelling efforts, preserving and improving the capability to generate overall plausible aggregated dynamics, while at the same time improving the precision in the microscopic group dynamics, with particular attention to the shape of dyads. The paper presents validated results based on empirical data from a controlled experiment, also discussing qualitative results in different common situations (bends and bottlenecks). [ABSTRACT FROM AUTHOR]

This paper deals with the numerical solution of Volterra-Fredholm integral equations. In this work, we approximate the unknown functions based on the shifted orthonormal Bernstein polynomials, in conjunction with the least-squares approximation method. The method is using a simple computational manner to obtain a quite acceptable approximate solution. The merits of this method lie in the fact that, on the one hand, the problem will be reduced to a system of algebraic equations. On the other hand, the efficiency and accuracy of the method for solving these equations are high. The convergence analysis of proposed method have been discussed through some theorems. Moreover, we will obtain an estimation of error bound for this algorithm. Finally, some examples are given to show the capability of presented method in comparison with four well-known algorithms in the literature namely the Legendre collocation method, Taylor collocation method, Taylor polynomial method and Lagrange collocation method. [ABSTRACT FROM AUTHOR]

Online advertising has become an important marketing instrument for many enterprises, and the impact of enterprises’ online advertising has been increasing rapidly. Significant long-term enterprise profits are dynamically determined by the continuous online advertisement investment strategies implemented. This paper formulates the investment cost function which reflects the characteristics and impacts of online advertising spillover effect to enterprises. Then the improved Lanchester model is used based on the investment cost function. According to the model, our research calculates the Nash equilibrium and does the numerical analysis under open-loop strategy and closed-loop strategy. (1) With the change of time and under the condition of open-loop and closed-loop, when the spillover effect level is higher, the investment amount on fixed-position online advertisement by the enterprises becomes opposite. (2) When the level of spillover effect is strong, the change in the market share of competitive enterprises is related to the initial market share under the condition of open-loop. Under the condition of closed-loop, the market share does not change in accordance with the level of spillover effect. (3) No matter under the open-loop strategy or the closed-loop strategy, the competitive enterprises with lower initial market share should increase the investment of the online advertising in order to attract new customers as early as possible. [ABSTRACT FROM AUTHOR]

Modern partial differential equation (PDE) models across scientific disciplines require sophisticated numerical methods resulting in complex codes as well as large numbers of simulations for analysis like parameter studies and uncertainty quantification. To evaluate the behavior of the model for sufficiently long times, for instance, to compare to laboratory time scales, often requires long-time simulations with small time steps and high mesh resolutions. This motivates the need for very efficient numerical methods and the use of parallel computing on the most recent modern architectures. We use complex code resulting from a PDE model of calcium dynamics in a heart cell to analyze the performance of the recently released Intel Xeon Phi Knights Landing (KNL). The KNL is a second-generation many-integrated-core (MIC) processor released in 2016 with a theoretical peak performance of over 3 TFLOP/s of double-precision floating-point operations for which complex codes can be easily ported because of the x86 compatibility of each KNL core. We demonstrate the benefit of hybrid MPI+OpenMP code when implemented effectively and run efficiently on the KNL including on multiple KNL nodes. For multi-KNL runs for our sample code, it is shown to be optimal to use all cores of each KNL, one MPI process on every other tile, and only two of the maximum of four threads per core. [ABSTRACT FROM AUTHOR]

Cloud computing brings new technologies and concepts that support communication services and data storage. Services like OneDrive, Google Drive and DropBox increase data availability and provide new features as synchronization and collaboration. These services require high availability and performance characteristics like high throughput and low probability that a timeout occurs, since it is fundamental to guarantee both business continuity and uninterrupted public services. In this research, we aim at evaluating availability and performance-related metrics for private cloud storage services. A hierarchical model-based strategy is proposed to evaluate distinct metrics by means of the composition of continuous-time Markov chains, reliability block diagrams and stochastic Petri nets. A case study is presented to illustrate the applicability of the proposed models through a cloud storage service hosted in the Eucalyptus platform. We also adopt availability importance index to identify the most critical components in relation to the system availability. Our numerical analyses indicate that, for instance, the adoption of redundant components reduces the probability that timeouts occur and the probability that users are attended due to failures. Furthermore, the results obtained from the stochastic models show that the proposed approach is indeed a good approximation to the measures obtained from the experiments conducted in a real cloud environment. [ABSTRACT FROM AUTHOR]

In this paper, we present a new hybrid binary version of dragony and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Dragonfly Enhanced Particle Swarm Optimization Algorithm(HBDESPO). In the proposed HBDESPO algorithm, we combine the dragony algorithm with its ability to encourage diverse solutions with its formation of static swarms and the enhanced version of the particle swarm optimization exploiting the data with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBDESPO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. Further, we use a set of assessment indicators to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBDESPO algorithm to search the feature space for optimal feature combinations. [ABSTRACT FROM AUTHOR]

This work approximates the unknown functions based on the two-dimensional shifted Legendre polynomials operational matrix method (2D-SLPOM) for the numerical solution of two-dimensional fractional integral equations. The present method reduces these equations to a system of algebraic equations and then this system will be solved numerically by Newton’s method. Moreover, an estimation of the error bound for this algorithm will be shown by preparing some theorems. Some examples are presented to demonstrate the validity and applicability of the proposed method with respect to the two-dimensional block pulse functions method (2D-BPFs) and two-dimensional Bernstein polynomials operational matrix method (2D-BPOM). [ABSTRACT FROM AUTHOR]

We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents. [ABSTRACT FROM AUTHOR]

The aim of this paper is to produce a new hybrid algorithm to solve minimax problems by combining the bat algorithm with direct search methods. The proposed algorithm is called hybrid bat direct search algorithm (HBATDS). In HBATDS, the global exploration and the local exploitation process are balanced. The bat algorithm has a good ability to make exploration and exploitation search. The exploitation capability of the proposed algorithm is increased by invoking the pattern search method as a local search method instead of the random walk method in the standard bat algorithm. In the final stage of the algorithm, the Nelder-Mead method is applied in order to refine the best found solution from the bat and pattern search method instead of running the algorithm more iterations without any improvements in the fitness function value. The performance of the HBATDS algorithm is investigated by applying it on 10 minimax problems and comparing it against 8 benchmark algorithms. The experimental results indicate that HBATDS is a promising algorithm and outperforms the other algorithms in most cases. [ABSTRACT FROM AUTHOR]