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With the exponential growth in the number of academic researchers, it is crucial for editors of scientific journals to identify the highest-quality papers. While several measures exist to evaluate a paper's impact post-publication, the challenge of determining the potential impact of a manuscript during the review process remains an understudied issue. In this paper, we propose a reviewer-reputation ranking algorithm to identify high-quality papers based on paper citations, where a reviewer's reputation is computed from the correlation between their past ratings and the current number of citations received by the papers they have evaluated. During the review process, reviewers with high reputation scores are given more weight to determine the quality of papers. We test the algorithm on an artificial network with 200 reviewers and 600 papers, as well as on the American Physical Society (APS) data set, including in the analysis 308,243 papers and 274,154 mutual citations. We compare our approach with two existing methods, demonstrating that our algorithm significantly outperforms the others in identifying manuscripts with the highest quality. Our findings can help improve the impact of scientific journals, thereby contributing to academic and scientific progress. • We propose an algorithm to identify the papers with the highest quality from a large number of submissions. • We compare our new algorithm with other existing methods of aggregating user ratings in various online services. • We test our algorithm both with an artificial network and with the empirical data of the APS data set. • We show that our algorithm outperforms the other methods in identifying the papers with the highest quality. [ABSTRACT FROM AUTHOR]

Detecting plagiarism in academic papers is crucial for maintaining academic integrity, preserving the originality of published work, and safeguarding intellectual property. While existing applications excel at text plagiarism detection, they fall short when it comes to image plagiarism. This paper introduces a novel algorithm, named "VIBRANT-WALK," designed to detect image plagiarism in academic manuscripts. The challenge of identifying plagiarized images is formidable, requiring a unique approach. Traditional Computer Vision algorithms, proficient in image similarity tasks, face limitations in determining whether an image has been previously used in an article. To address this, the proposed algorithm leverages a repository of all published article pages, focusing on absolute identicality rather than image similarity. The algorithm comprises two stages. In the first stage, a "Vibrancy Matrix" is created through image preprocessing, aiding in contour determination. The second stage involves pixel-by-pixel comparison with images from published manuscripts. To enhance efficiency, the algorithm initiates comparisons from the pixel with the highest score in the Vibrancy Matrix, followed by pixel comparisons through random walks, significantly reducing complexity. To conduct the study, a custom dataset was compiled from 69 research articles, capturing snapshots of each page and figure. Overall, we present 485 unique test cases where we can test the accuracy and efficiency of the algorithm. The lack of publicly available datasets necessitated this approach. The proposed algorithm outperformed the existing models and algorithms in this field by achieving an overall accuracy of 94.8% on the collated dataset, identifying 460 instances of plagiarism out of the 485 test cases. The algorithm also demonstrated a 100% accuracy rate in avoiding false positives. [ABSTRACT FROM AUTHOR]

Abstract: More than just being a substrate, paper can also provide evidence for the provenance of documents. An earlier paper described a method to compare paper structure, based on the Fourier power spectra of light transmission images. Good results were obtained by using the 2D correlation of images derived from the power spectra as a similarity score, but the method was very computationally intensive. Different comparison algorithms are evaluated in this paper, using information theoretical criteria. An angular invariant algorithm turned out to be as effective as the original one but 4 orders of magnitude faster, making the use of much larger databases possible. [Copyright &y& Elsevier]

Accurate characterization is an important issue in paper currency recognition system. This paper proposes a robust paper currency recognition method based on Hidden Markov Model (HMM). By employing HMM, the texture characteristics of paper currencies are modeled as a random process. The proposed algorithm can be used for distinguishing paper currency from different countries. A similarity measure has been used for the classification in the proposed algorithm. To evaluate the performance of the proposed algorithm, experiments have been conducted on more than 100 denominations from different countries. The results indicate 98% accuracy for recognition of paper currency. [Copyright &y& Elsevier]

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese Algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese Algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese Algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese Algorithm problem-solving capabilities. The primary objective of the Snow Geese Algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese Algorithm is publicly available at https://github.com/stones3421/SGA-project. • Snow Geese Algorithm: A novel meta-heuristic approach inspired by snow geese flight behavior, tackling optimization problems. • Two-stage Strategy: Mimics snow geese exploration and exploitation patterns, setting it apart from traditional methods. • Performance Evaluation: Multiple algorithms are assessed on different problems. • Algorithm Features: Experimental results validate the effectiveness of the Snow Geese Algorithm. • Practical Applications: Snow Geese Algorithm shows potential in engineering optimization, aiding accurate decision-making. [ABSTRACT FROM AUTHOR]

In this paper, based on the Lagrange multiplier method, we construct a maximum principle preserving scheme for the time-fractional Allen-Cahn equation of 2- α (0 < α < 1) order. The correction energy of this scheme is increased by a term compared to the original energy, which is O (τ α). We prove that our scheme is unconditionally stable related to the corrected energy and verify the convergence, maximum principle, and energy conservation properties of the algorithm through numerical examples. We also find that the larger the α , the faster the evolution of the time-fractional Allen-Cahn equation. [ABSTRACT FROM AUTHOR]

Diffeomorphic registration is a widely used technique for finding a smooth and invertible transformation between two coordinate systems, which are measured using template and reference images. The point-wise volume-preserving constraint det ⁡ (∇ φ (x)) = 1 is effective in some cases, but may be too restrictive in others, especially when local deformations are relatively large. This can result in poor matching when enforcing large local deformations. In this paper, we propose a new bi-variant diffeomorphic image registration model that introduces a soft constraint on the Jacobian equation det ⁡ (∇ φ (x)) = f (x) > 0. This allows local deformations to shrink and grow within a flexible range 0 < κ m < det ⁡ (∇ φ (x)) < κ M. The Jacobian determinant of transformation is explicitly controlled by optimizing the relaxation function f (x). To prevent deformation folding and improve the smoothness of the transformation, a positive constraint is imposed on the optimization of the relaxation function f (x) , and a regularizer is used to ensure the smoothness of f (x). Furthermore, the positivity constraint ensures that f (x) is as close to one as possible, which helps to achieve a volume-preserving transformation on average. We also analyze the existence of the minimizer for the variational model and propose a penalty-splitting algorithm with a multilevel strategy to solve this model. Numerical experiments demonstrate the convergence of the proposed algorithm and show that the positivity constraint can effectively control the range of relative volume without compromising the accuracy of the registration. Moreover, the proposed model generates diffeomorphic maps for large local deformations and outperforms several existing registration models in terms of performance. • A novel bi-variant diffeomorphic image registration model with relaxed Jacobian determinant constraints is proposed. • The existence of the minimizer for the variational model is proved. • A penalty splitting algorithm with a multilevel strategy is designed to solve the new model. • Numerical experiments show that the proposed algorithm is convergent and the new model has good performance. [ABSTRACT FROM AUTHOR]

In this paper, we devise a stochastic extragradient-like algorithm incorporated with inertial terms, which requires a single projection onto our feasible set and employs a stochastic approximation version of an Armijo-type line search scheme along a feasible direction, for solving pseudomonotone stochastic variational inequalities. In the algorithm, two different stepsize strategies are employed to update steplength sequences without using the prior knowledge of the Lipschitz constant of involved operators. In the process of the stochastic approximation, we iteratively reduce the variance of stochastic errors. The almost sure convergence, the complexity analysis, and rates are provided in a dimensional space under reasonable conditions. Finally, some numerical experiments with graphical illustrations are reported to demonstrate the applicability and the efficiency of our algorithm in comparison with some projection type methods in the literature. [ABSTRACT FROM AUTHOR]

Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

• Dynamics of multi-layer membrane structure with elastic spacers is studied. • An optimization criterion to eliminate the crossing modes is proposed. • The optimal spacer positions are calculated by Particle Swarm Optimizer algorithm. In recent years, there has been a growing interest in the dynamics of space membrane structures. However, existing research primarily focuses on single-layer membrane structures, leaving a significant research gap in the study of multi-layer structures with elastic spacers. To address this gap, this paper studies the dynamics and interlayer spacing accuracy of multi-layer membrane structures with elastic spacers. Specifically, the research focuses on the placement optimization of elastic spacers. A novel optimization criterion for the elastic spacers has been proposed, and the Particle Swarm Optimizer (PSO) algorithm has been utilized to calculate the optimal spacers positions. Results show that optimally placed elastic spacers can eliminate the crossing modes and improve the interlayer spacing accuracy during vibration. [ABSTRACT FROM AUTHOR]

Two key challenges in optimal control include efficiently solving high-dimensional problems and handling optimal control problems with state-dependent running costs. In this paper, we consider a class of optimal control problems whose running costs consist of a quadratic on the control variable and a convex, non-negative, piecewise affine function on the state variable. We provide the analytical solution for this class of optimal control problems as well as a Hopf-type representation formula for the corresponding Hamilton-Jacobi partial differential equations. Finally, we propose efficient numerical algorithms based on our Hopf-type representation formula, convex optimization algorithms, and min-plus techniques. We present several high-dimensional numerical examples, which demonstrate that our algorithms overcome the curse of dimensionality. We also describe a field-programmable gate array (FPGA) implementation of our numerical solver whose latency scales linearly in the spatial dimension and that achieves approximately a 40 times speedup compared to a parallelized central processing unit (CPU) implementation. Thus, our numerical results demonstrate the promising performance boosts that FPGAs are able to achieve over CPUs. As such, our proposed methods have the potential to serve as a building block for solving more complicated high-dimensional optimal control problems in real-time. [ABSTRACT FROM AUTHOR]

This paper analyzes a parareal approach based on fractional-step methods for the nonstationary Navier-Stokes equations. As an efficient parallel computing framework, the coarse propagator often determines the performance of the parareal algorithm. We present a parareal algorithm using the fractional-step method, a very efficient time discrete scheme for the Naiver-Stokes equations, as the coarse propagator for the Navier-Stokes equations. Then we give the specific stability and convergence analysis of this specific parareal algorithm. Finally, numerical experiments are done to show efficiency and illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, elliptic control problems with the integral constraint on the gradient of the state and the box constraint on the control are considered. The optimality conditions for the problem are proved. To numerically solve the problem, a finite element duality-based inexact majorized accelerated block coordinate descent (FE-dABCD) algorithm is proposed. Specifically, both the state and the control are discretized by piecewise linear functions. An inexact majorized ABCD algorithm is employed to solve the discretized problem via its dual, which is a multi-block unconstrained convex optimization problem, but the primal variables are also generated in each iteration. Thanks to the inexactness of the FE-dABCD algorithm, the subproblems at each iteration are allowed to be solved inexactly. For the smooth subproblem, we use the preconditioned generalized minimal residual (GMRES) method to solve it. For the two nonsmooth subproblems, one of them has a closed form solution through introducing an appropriate proximal term, and another one is solved by the line search Newton's method. Based on these efficient strategies, we prove that our proposed FE-dABCD algorithm enjoys O (1 k 2 ) iteration complexity. Moreover, to make the algorithm more efficient and further reduce its computation cost, based on the mesh-independence of ABCD method, we propose an FE-dABCD algorithm with a warm-start strategy (wFE-dABCD). Some numerical experiments are done and the numerical results show the efficiency of the FE-dABCD algorithm and wFE-dABCD algorithm. [ABSTRACT FROM AUTHOR]

Dynamic Mode Decomposition (DMD) is an equation-free method that aims at reconstructing the best linear fit from temporal datasets. In this paper, we show that DMD does not provide accurate approximation for datasets describing oscillatory dynamics, like spiral waves, relaxation oscillations and spatio-temporal Turing instability. Inspired by the classical "divide and conquer" approach, we propose a piecewise version of DMD (pDMD) to overcome this problem. The main idea is to split the original dataset in N submatrices and then apply the exact (randomized) DMD method in each subset of the obtained partition. We describe the pDMD algorithm in detail and we introduce some error indicators to evaluate its performance when N is increased. Numerical experiments show that very accurate reconstructions are obtained by pDMD for datasets arising from time snapshots of certain reaction-diffusion PDE systems, like the FitzHugh-Nagumo model, a λ - ω system and the DIB morpho-chemical system for battery modeling. Finally, a discussion about the overall computational load and the future prediction features of the new algorithm is also provided. [ABSTRACT FROM AUTHOR]

This paper investigates the convergence behavior of the Dirichlet-Neumann and Neumann-Neumann waveform relaxation algorithms for time-fractional sub-diffusion and diffusion-wave equations. The algorithms are applied to regular domains in 1D and 2D for multiple subdomains, and the impact of different constant values of the generalized diffusion coefficient on the algorithms' convergence is analyzed. The convergence rate of the algorithms is analyzed as the fractional order of the time derivative changes. The paper demonstrates that the algorithms exhibit slow superlinear convergence when the fractional order is close to zero, almost finite step convergence (exact finite step convergence for wave case) when the order approaches two, and faster superlinear convergence as the fractional order increases in between. The transitional nature of the algorithms' behavior is effectively captured through estimates with changes in the fractional order, and the results are verified by numerical experiments. [ABSTRACT FROM AUTHOR]

• A novel combined interval prediction algorithm is proposed in this paper. • Hybrid feature selection strategy is designed in the proposed system. • Multi-objective optimization mechanism reflects strong search capabilities. • Four interval prediction models cover the inherent modes of sequence. Traditional point prediction approaches can not reflect the uncertainty, which brings greater risks to decision-makers. To fill this gap, this paper extends a feature selection strategy that relies solely on correlation and redundant feature judgment, proposes a novel combined interval prediction algorithm, 3-Mcip (Combined Interval Prediction Based on Maximize Relevancy, Minimize Redundancy and Maximize Synergy) system, and solves the tradeoff between prediction accuracy and interval width. This system first designs a hybrid feature selection strategy to optimally select candidate variables and reduce model input redundancy. Secondly, the structure of the four ANN models is improved to accommodate the results of feature selection, and an optimization mechanism is introduced to search for the Pareto optimal solution set. In order to measure the comprehensive performance of the 3-Mcip system, hourly power load data and related candidate variables from Pittsburgh and Washington, D.C are considered. The numerical results show that the 3-Mcip system has coverage rates of 53.3333, 90.1667, and 99.4479 for Site 1 at different levels of interval width coefficients, which not only achieves perfect prediction of power load but also analyzes uncertainty. It is also helpful for power system managers to better capture the fluctuation range of future load and improve the flexibility of smart grid dispatching. [ABSTRACT FROM AUTHOR]

In this paper, a novel non-convex fractional-order image denoising model is proposed to suppress the staircase effect produced by the TV model while maintaining a neat contour. The model combines ℓ q (0 < q < 1) quasi-norm and fractional-order regularization, and employs a diffusion coefficient with a faster convergence rate to preserve more image edges and details. Additionally, an adaptive regularization parameter is designed to adjust the denoising performance of the algorithm. To obtain the optimal approximate solution of the model, an enhanced primal-dual algorithm is adopted and the complexity and convergence of the algorithm are theoretically analyzed. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. • A new non-convex FOTV model is proposed. • A new diffusion coefficient is introduced to retain more edge details of the image. • The existing primal-dual algorithm is improved, and the convergence of this algorithm is analyzed. • An adaptive regularization parameter is designed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a second-order adaptive DLN algorithm with different subdomain variable time steps for the 3D closed-loop geothermal system. Theoretically, the stability and convergence of the algorithm in the case of variable time steps are analyzed. In numerical experiments, the DLN algorithm is combined with an adaptive algorithm to verify not only its effectiveness and second-order convergence under any arbitrary sequence of time steps but also to improve computational efficiency. We overcome the difficulty of 3D programming, and verify the applicability and accuracy of the proposed algorithm by simulating the flow characteristics and heat transfer of the closed-loop geothermal system with 3D convection. [ABSTRACT FROM AUTHOR]

In this paper, the multi-parameter time fractional radially symmetric anomalous diffusion model used in porous media with initial singularity is considered. Both the direct numerical solution problem and the multi-parameter identification inverse problem are studied. Given the singularity in the initial time, a stable numerical scheme on nonuniform grid mesh is derived by using the L 2 − 1 σ method. To conduct the multi-parameter inversion problem, a novel hybrid Black Widow Optimization and Cuckoo Search (BWOCS) algorithm is proposed to combine the advantages of both the BWO algorithm and the CS algorithm, in order to improve the convergence speed and to achieve high-accuracy optimal results. Numerical examples are given to verify the efficiency and accuracy of the proposed numerical scheme and parameter inversion algorithm. Results show that the nonuniform grid L 2 − 1 σ scheme is efficient to deal with the time fractional radially symmetric anomalous diffusion problem with initial singularity, and the hybrid BWOCS algorithm has high precision and well convergence speed, compared with both the BWO and CS algorithms, which can be extended to other fractional inverse problems. [ABSTRACT FROM AUTHOR]

In this paper, several local and parallel finite element algorithms are proposed and analyzed for the 2D/3D stationary inductionless incompressible magnetohydrodynamic (MHD) equations. The core concept is to guarantee the charge-conservation property by choosing mixed finite element spaces in H 0 (div , Ω) × L 0 2 (Ω) to approximate (J , ϕ) , meantime combining the idea of domain decomposition method to realize parallel operation. The characteristic of the proposed algorithms is that the computational complexity is greatly reduced while ensuring the accuracy of the numerical simulation. With the local a prior estimate as the technical means of theoretical analysis, we give the error estimates of the algorithms. Finally, several numerical experiments are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]

• Bi-level hierarchical distribution of resources is reformulated. • Hooke-Jeeves and Scatter Search algorithms are implemented. • A multi-objective perspective is introduced to tackle the problem. • A multi-objective scatter search algorithm delivers best results for both objectives. • Tailored-made instances were crafted for the problem. The decision regarding the hierarchical distribution of resources is crucial for many organizations that want to allocate such resources efficiently. At the upper level of the hierarchy, a decision-maker may have an objective and a set of feasible solutions partially determined by the lower level. Nevertheless, the upper level can influence but not control the decision-maker at the lower level. Moreover, the objectives of the upper and lower levels are conflicting. Hence, the hierarchical distribution of resources can be formulated as a bi-level programming model. This paper reformulates the hierarchical allocation of resources. The Hooke-Jeeves (HJ) algorithm and the hybrid scatter search–Nelder-Mead (SSNM) method are proposed to solve the newly formulated problem. The problem is also analyzed from a multi-objective perspective to equally prioritize the upper and lower levels while subjecting each to an interdependent set of constraints; a multi-objective scatter search algorithm (MSSA) was developed for that purpose. Tailor-made instances were also designed for the implementation of the developed algorithms. The findings demonstrated that the HJ algorithm provides the best solution according to the upper-level objective function. However, it does not guarantee the best result for the lower level. In comparison, MSSA has a set of best possible solutions for both objectives. The hybrid SSNM method could not match the results provided by the former methods. [ABSTRACT FROM AUTHOR]

Exploring special multi-scroll chaotic systems is meaningful work. This paper studies an expanded multi-scroll chaotic system consisting of eight terms with one nonlinearity. It is generated by modifying the nonlinear term of the newly constructed chaotic system by a polynomial function. The unique mathematical model makes the unstable index-2 equilibria increase in four dimensions, which contributes to the number of scrolls expanding in each phase plane unidirectionally. Dynamic analysis finds that the system can yield complex nested multi-scroll attractors and has single-parameter-based synchronization control of amplitude and offset boosting behavior. Moreover, circuit implementation verifies the physical existence of the proposed system. Also, an image encryption algorithm for remote sensing images is established. Permutation and diffusion operations are performed using new pixel coordinates derived from the data of the chaotic matrix, which comes from the same position as the pixel matrix. Relevant tests suggest that the algorithm is secure and able to resist undesirable interference. • Simple multiscroll chaotic system with various signal control features is proposed. • Complex dynamics, circuit implementation of the proposed system is studied. • New chaos-based image encryption algorithm with high security is designed. [ABSTRACT FROM AUTHOR]

This paper presents and analyzes a parallel finite element post-processing algorithm for the simulation of Stokes equations with a nonlinear damping term, which integrates the algorithmic advantages of the two-level approach, the partition of unity method and post-processing technique. The most valuable highlights of the present algorithm are that (1) a global continuous approximate solution is generated via the partition of unity method; (2) by adding an extra coarse grid correction step, the smoothness of the approximate solution is improved; (3) it has a good parallel performance since there requires little communication in solving a series of residual problems in the subdomain of interest. We theoretically derive the L 2 -error estimates both for the approximate velocity and pressure and H 1 -error estimate for the velocity under some necessary conditions. Meanwhile, we numerically perform various test examples to validate the theoretically predicted convergence rate and illustrate the high efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: We introduce an algorithm for approximating a 2-manifold 3D mesh by a set of developable surfaces. Each developable surface is a generalized cylinder represented as a strip of triangles not necessarily taken from the original mesh. Our algorithm is automatic, creates easy-to-assemble pieces, and provides global error bounds. The algorithm consists of three stages. In the first stage, the mesh is segmented into meaningful components. The second stage approximates each of the mesh components in 3D by a set of triangle strips with smooth (non-jagged) boundaries that guarantee a maximal user-defined error relative to the original mesh. Finally, these strips are unfolded to the plane, resulting in flat patterns, which may be cut and assembled from paper. The approximation quality is controlled by a user-supplied parameter specifying the allowed Hausdorff distance between the input mesh and its piecewise-developable approximation. The strips generated by our algorithm may be parameterized to conform with the parameterization of the original mesh, if given, to facilitate texture mapping. We demonstrate this by physically assembling paper-craft models from the strips generated by our algorithm when run on several polygonal 3D mesh data sets. [Copyright &y& Elsevier]

Abstract: In this paper we provide a computational approach to the shape of curves which are rational in polar coordinates, i.e. which are defined by means of a parametrization where both , are rational functions. Our study includes theoretical aspects on the shape of these curves, and algorithmic results which eventually lead to an algorithm for plotting the “interesting parts” of the curve, i.e. the parts showing the main geometrical features. [Copyright &y& Elsevier]

Abstract: In a recent paper (Computer Aided Geometric Design 23 (3), 243–260), Yang presented a novel kind of nonlinear and geometry driven subdivision scheme for curve interpolation. The author declared that this scheme was shape preserving when all free parameters were explicitly selected by some specific rules. In this note, we demonstrate that the author (a) ignored the consistency of the definition to straight edges, and also (b) did not give a perfect scheme and proof for the shape preserving subdivision. [Copyright &y& Elsevier]

Most classification algorithms are “passive”, in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can—at some cost—obtain the values of some unspecified attributes, before deciding upon a class label. This can be useful, for instance, when deciding whether to gather information relevant to a medical procedure or experiment. The expected utility of using an active classifier depends on both the cost required to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification. This paper analyzes the problem of learning optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework, we show that this task can be achieved efficiently when the active classifier is allowed to perform only (at most) a constant number of tests. We then show that, in more general environments, this task of learning optimal active classifiers is often intractable. [Copyright &y& Elsevier]

Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively. • The cumulative sum flood fill approach is introduced in topology optimization. • The approach works as a filter and successfully eliminates enclosed voids. • Direct control can be achieved over the amount, location, and size of access channels. • Due to its geometric nature it adds little computational effort. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a novel approximate algorithm to calculate the top-k closest pairs join query of two large and high dimensional data sets. The algorithm has worst case time complexity and space complexity and guarantees a solution within a factor of the exact one, where t ∈{1,2,…,∞} denotes the Minkowski metrics L t of interest and d the dimensionality. It makes use of the concept of space filling curve to establish an order between the points of the space and performs at most d +1 sorts and scans of the two data sets. During a scan, each point from one data set is compared with its closest points, according to the space filling curve order, in the other data set and points whose contribution to the solution has already been analyzed are detected and eliminated. Experimental results on real and synthetic data sets show that our algorithm behaves as an exact algorithm in low dimensional spaces; it is able to prune the entire (or a considerable fraction of the) data set even for high dimensions if certain separation conditions are satisfied; in any case it returns a solution within a small error to the exact one. [Copyright &y& Elsevier]

In this paper, a novel spectral method is presented and studied for the fourth order problem with mixed boundary in a cylindrical domain. The basic idea of our approach is to reduce the original problem into a series of decoupled two-dimensional fourth-order problems first, by using the cylindrical coordinate transformation and Fourier expansion, and then adopt the standard spectral method to solve the decoupled problems. A new essential pole condition is proposed to overcome the difficulty caused by the introduction of singularity and variable coefficients in cylindrical coordinate transformation. Existence and uniqueness of the weak solution and the discrete numerical solution are proved, and error estimates of the spectral method are derived. Furthermore, the efficient implementation of our algorithm is discussed, where a set of effective basis functions are constructed to ensure the sparsity of the mass matrix and stiffness matrix. Numerical examples are presented to validate the theoretical findings and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-grid lumped mass finite element algorithm for 2D Allen-Cahn equations, where Crank-Nicolson scheme and piecewise linear element are utilized for temporal and spatial discretization, respectively. Both the maximum-norm boundedness and H 1 -norm error estimates of the proposed two-grid scheme are discussed. Finally, a numerical example is given to verify the theoretical results. By comparing with the finite element scheme and the two-grid finite element scheme, we found that our scheme has a great advantage in calculation time. [ABSTRACT FROM AUTHOR]

• A novel three-dmensional chaotic system NewSysm is constructed for keystream. • A fresh model TransM is built for the production of plaintext keys. • A new model GetM is established for computing of the initial values of NewSysm. • Three bit planes of the cipher image are decomposed for hidding respectively. In this paper, a three-dimensional chaotic system is proposed. Based on this chaotic system and two-dimensional compressive sensing, an asymmetric visually meaningful image compression-hiding algorithm is presented. Firstly, in the keystream generation stage, a novel parameter transformation model is constructed to pick up the feature information from the plain image as the plaintext key. Then, Rivest-Shamir-Adleman algorithm is employed to encrypt the plaintext key into the ciphertext key seen as public key. Before generating the initial values for the chaotic system, a new initial value getting model is designed to transform both the plaintext and the ciphertext keys. After solving the chaotic system, the keystream is produced which is then used in the image encryption process. Secondly, in the compression and encryption phase, a novel keystream pre-processing model is built to generate new sequences with a confusion performed on the plain image. Then, a newly constructed measurement matrix is designed to do two-dimensional compressive sensing on confusing the image to get measurements. Before obtaining the cipher image, a double diffusion operation is applied on these measurements. Thirdly, in the image hiding stage, the carrier image is performed by integer wavelet transformation to obtain coefficient matrices. Then, the cipher image is decomposed in decimal, getting the ones, tens and hundreds of pixels to form three bit matrices, of which are embedded into the three medium-high coefficient matrices of integer wavelet transformation, respectively. Finally, after performing inverse integer wavelet transformation, the carrier image containing secrets, i.e., visually meaningful encrypted image, is obtained. Experimental results also show that at a low compression ratio of 0.25, the normalized correlation coefficient between the original plain image and the recovered image is almost equal to one, while the peak signal-to-noise ratio between the carrier image containing secrets and its original carrier can reach as high as 42 dB. In addition, the proposed image compression-hiding algorithm performs good ability consideing the brute force attack and the cropping attack. [ABSTRACT FROM AUTHOR]

• Space debris detection; • Payload camera breadboard for space surveillance; • Star tracking and space debris detection algorithms; • Hybrid time-index image algorithm. Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth's orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms' tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net , to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost. [ABSTRACT FROM AUTHOR]

• A computing method for constellation-to-ground coverage is investigated. • The maximum and minimum coverage regions are calculated. • Simulation results demonstrate the validity and effectiveness of the proposed algorithm. The issue of constellation-to-ground coverage is a research focus in Earth observation applications. Traditional calculations often rely on grid point methods or their derivatives, but these can be limited in their application, relatively costly, inefficient, and often do not account for potential errors. This paper proposes a novel, efficient method based on latitude strip division for calculating the ground area coverage of satellite constellations, capable of providing the upper and lower bounds of coverage ratio for any ground area. Initially, the ground target area is divided into several latitude strips, and the target area range is utilized to determine the longitude range of each latitude strip. Subsequently, the upper and lower bounds of coverage of each strip are calculated according to the satellite ground coverage range. On this basis, the coverage boundary function is defined and the coverage ratio is derived through comprehensive statistics analysis. Finally, depending on the accuracy of the latitude strip division, the precise coverage area and coverage ratio with upper and lower bounds are determined for instantaneous, continuous, and cumulative coverage problems. Numerical simulation experiments were carried out and compared with the traditional grid point method to validate the effectiveness and computational efficiency of this algorithm in addressing the coverage issue for arbitrarily shaped ground areas. When compared to the longitude strip method, it was confirmed that for ground areas where the longitude exceeds the latitude range, this approach offers superior computational efficiency than the latitude strip method. [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes a second-order decoupled Backward Euler method plus time filter for the evolutionary Stokes/Darcy model, which allows different variable time steps in the free fluid flow region and the porous media flow region. Furthermore, this algorithm, which is a combination of first order Backward Euler method and time filter scheme, uncouples the Stokes/Darcy model into the Stokes and Darcy problems per time step. In particular, adaptive algorithm is constructed to improve the computational efficiency. Moreover, we mainly deduce the stability and error estimation in theoretical analysis. In addition, numerical experiments are used to verify the effectiveness, second order convergence and efficiency. [ABSTRACT FROM AUTHOR]

Shadow detection is a common computer vision task that identifies the location of shadows. It has been used as a key primitive for many high-level tasks such as object segmentation and video tracking. Due to the complexity and variety of shadows, it is challenging to design a one-size-fits-all shadow detection algorithm in real-world scenarios. Instead, it is often necessary in practice to pick the best shadow detection algorithm for a particular vision application. While shadow detection algorithms can be assessed by comparing the detected shadow with the ground truth, such ground truth is often unavailable in real-world vision systems consistently generating new data. In this paper, we propose a no-reference shadow detection quality assessment network (NSDQA-Net) to rank shadow detection algorithms without needing the ground truth. For a given image, the proposed assessment network first learns a reference map that compensates the false positives and false negatives of multiple input shadow detection algorithms. The reference map, as well as the original image and the input shadow detection results, are then used to regress the quality scores of the shadow detection methods. Our framework supports the assessment of one, two, or multiple algorithms in a single run, making it easier for deployment in practical scenarios. Experiments results show that the proposed network achieves optimal accuracy in no-reference shadow detection assessment. Relying on it for adaptive algorithm selection also improves the performance of shadow detection using a fixed algorithm on benchmark datasets. [Display omitted] • We identify a task of no-reference quality assessment in shadow detection. • We design a novel no-reference shadow detection quality assessment network (NSDQA-Net). • We experimentally demonstrate the state-of-the-art performance of the proposed network. [ABSTRACT FROM AUTHOR]

• A continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. • Combination of modal truncation order and sensor layout optimization by modal strain energy. • The proposed algorithm has better accuracy and noise immunity than SSM and GKFM. • For continuous systems, the proposed algorithm has a solution time of 0.38s and is insensitive to the time step. • The proposed algorithm has good accuracy in experimental validation. Dynamic load identification based on structural responses is an important problem in the field of engineering and plays an important role in the condition assessment of mechanical structures. Current popular load identification methods such as the state-space method (SSM) and the Green's kernel function method (GKFM) are implemented on discrete systems with outstanding performance, but when dealing with complex continuous systems, there are some limitations such as low efficiency and inaccuracy. In this paper, a continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. Using modal coordinate transformation and modal truncation methods, the number of infinite vibration differential equations of a continuous system in physical space are converted to a finite number of vibration differential equations in modal space, where the modal truncation order and the optimal layout of the sensors are combined by modal strain energy. The Newmark-β method in modal space is derived and thus combined with SSM to obtain a load identification model Y =HF called SSM-Newmark-β method, which reduces the size of the transfer matrix H. The solution time of the proposed algorithm is 0.38 s for continuous systems, which is rather shorter than that for discrete systems. Furthermore, the simulations show that it has better accuracy and noise immunity than SSM and GKFM in the identification of sinusoidal load, impact load, and random load. The effect of time step is also discussed which reveals that the larger time step has less effect on the proposed algorithm. An experimental study is carried out on a cantilever beam system. The result verifies that the SSM-Newmark-β algorithm has better accuracy in load identification for the continuous system. This research provides a new sight for the real-time identification of dynamic loads for complex structures. [ABSTRACT FROM AUTHOR]

Narrowband SETI algorithms have been in use for many decades. A key processing step in the usual SETI pipeline called "De-Doppler" involves non-coherent integration of linearly-drifting lines in a spectrogram. De- Doppler (DD) is needed because a transmitted constant-frequency tone will be received as a chirp, or a tone with a near-constant frequency rate, due to relative accelerations between the transmitter and receiver. The frequency drift may be an important discriminator against certain types of radio-frequency interference. This paper examines the standard Taylor DD algorithm and finds there are significant signal losses in the integration process. Part of the loss is caused by imperfect indexing in the algorithm. Additional loss is associated with the effect of an FFT filter bank on chirp signals; DD performance is intertwined with the filter bank. Filter bank improvements involving polyphase filter banks are examined. A new DD algorithm called "fastDD" is proposed, as well as extensions to the Taylor algorithm. They both offer significant performance gains and higher resolution in frequency and drift rate in the DD detection plane. The loss mechanisms are now understood. Computational cost trade-offs for various filter bank and DD parameters are presented based on C code functions. Several "best" options are given which offer significant SNR improvements ranging from 2 to 4 dB for modest levels of additional computation. These options have the potential to dramatically improve detection rates. • Goal: (i) Understand and improve the De-Doppler (DD) function for narrowband SETI. (ii) Taylor DD – the gold standard for over a decade – has not been examined in detail. (iii) Need to look at DD as part of an overall end-to-end narrowband SETI detection system. • Results: (i) Significantly improved detection performance is possible: 2–4 dB improved sensitivity, depending on computation and memory budget; Potential SETI detection rate improvements of 2–4 times. (ii) Gains arise from possible use of overlapped polyphase filter banks and higher resolution in the DD plane. (iii) New algorithms have been defined (fastDD and extended Taylor DD) and evaluated. (iv) SNR Gain vs. Computation cost trades have been examined using C language functions: Best options of filter bank and DD parameters are identified. [ABSTRACT FROM AUTHOR]

Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications. • Deep Learning is used extensively for monocular spacecraft pose estimation. • Deployability is an important limitation of the current DL-based solutions. • Lack of space-borne images during training leads to the domain gap problem. [ABSTRACT FROM AUTHOR]

Facial information plays an important role in human communication, e.g., rich and nuanced facial expressions effectively convey emotions. Traditional data such as 2D facial images and videos are susceptible to perturbation from lighting and occlusion, while 3D static data such as mesh models lack the temporal information which is necessary to describe facial dynamics. Therefore, researches on 4D facial data (3D facial models together with time as the fourth dimension) have received considerable attention in recent years. 4D data can simultaneously reflect the complex facial temporal and space information. To fully explore these characteristics, we present a systematic overview of the 4D facial research in this paper. We give a review in terms of historical development of 4D facial datasets, acquisition process of these datasets, related algorithms and applications, and discussion of outstanding issues. We also analyze the 4D facial research works by summarizing and comparing them; in particular, we present the results on three kinds of tasks conducted on the 4D facial datasets, i.e., expression-related tasks (AU detection, expression recognition and retrieval), generation tasks (3D facial reconstruction, facial expression and facial animation generation), and other tasks (facial registration, facial recognition and facial disease diagnosis). Finally, we summarize the future research directions. [Display omitted] • We provide a comprehensive overview of a 4D facial dataset. • We classified and elaborated on the different facial studies conducted using 4D facial datasets. • We summarize the future research direction. [ABSTRACT FROM AUTHOR]

Accurate prediction of re-entry time and location of an uncontrolled re-entering space object is crucial but challenging. This paper presents in-house re-entry prediction algorithms: RSMGA, STKOptim, STKLTOptim, and ABPro. Case studies on re-entry prediction of Starlink-26 (COSPAR ID 2019-029F or NORAD ID 44240) and CZ-5B (COSPAR ID 2021-035B or NORAD ID 48275) during the IADC re-entry test campaigns 2021/1 and 2021/2, respectively are presented. Simultaneous application of these re-entry prediction algorithms facilitates decision-making on automation of switching between these algorithms during various phases (short term or long-term) of the re-entry campaign. The effectiveness and applicability of these algorithms are studied based on percentage error. The study focuses on the overall and time-based performance of the four methods. Six more re-entered objects are selected (three Geosynchronous transfer orbit (GTO) objects and 3 Low-Earth orbit (LEO) objects), and their re-entry time prediction has been carried out using the four methods for different time instances before re-entry. Prediction estimates are compared among the methods for all the time instances in terms of percentage error. Also, the study helps in fixing the suitability of the re-entry time prediction algorithm for objects from GTO and LEO. [ABSTRACT FROM AUTHOR]

There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial. [Copyright &y& Elsevier]

Abstract: For positive integers , an -labeling of a digraph D is a function f from into the set of nonnegative integers such that if x is adjacent to y in D and if x is of distance two to y in D. Elements of the image of f are called labels. The -labeling problem is to determine the -number of a digraph D, which is the minimum of the maximum label used in an -labeling of D. This paper studies -numbers of digraphs. In particular, we determine -numbers of digraphs whose longest dipath is of length at most 2, and -numbers of ditrees having dipaths of length 4. We also give bounds for -numbers of bipartite digraphs whose longest dipath is of length 3. Finally, we present a linear-time algorithm for determining -numbers of ditrees whose longest dipath is of length 3. [Copyright &y& Elsevier]

Abstract: In this paper, we will derive a solver for a symmetric strongly nonsingular higher order generator representable semiseparable plus band matrix. The solver we will derive is based on the Levinson algorithm, which is used for solving strongly nonsingular Toeplitz systems. In the first part an solver for a semiseparable matrix of semiseparability rank p is derived, and in a second part we derive an solver for a band matrix with bandwidth . Both solvers are constructed in a similar way: firstly a Yule–Walker-like equation needs to be solved, and secondly this solution is used for solving a linear equation with an arbitrary right-hand side. Finally, a combination of the above methods is presented to solve linear systems with semiseparable plus band coefficient matrices. The overall complexity of this solver is plus lower order terms. In the final section numerical experiments are performed. Attention is paid to the timing and the accuracy of the described methods. [Copyright &y& Elsevier]

Consistency control at the outlet of blow tanks and hi-density towers, the so-called double-dilution problem, is an area where further control development is needed. The task is to coordinate the manipulation of two input variables, coarse and fine dilution, to control the consistency, while simultaneously mid-ranging the fine dilution to keep it from saturating. This is made difficult by process nonlinearities, complex dynamics and tight manipulated variable saturation constraints. To address these problems, a model-based predictive controller (MPC) was applied to blow tank consistency control in a pulp mill. One of the unique features of the proposed MPC algorithm is that it is well suited to run on standard distributed control system (DCS) hardware of the type that already exists in most process plants. Several control strategies were tested, including MPC and the previously implemented scheme, with and without a ratio control scheme aimed at removing the dominant process nonlinearity. The results of a mill trial indicate that both controllers, when implemented as ratio controllers, give excellent disturbance rejection for production rate changes, keeping the consistency within 0.1% of target. However, MPC outperformed the conventional strategy in its response to setpoint changes and unmeasured disturbances, and was able to cope with actuator failure. [Copyright &y& Elsevier]

Objective: Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.Methods: Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab(®)-a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.Results: The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4-6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1-4.6).Discussion: Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab(®), user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.Conclusion: Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia. [ABSTRACT FROM AUTHOR]

• This paper constructs and analyzes a composite s -sub-step explicit method. • Seven explicit schemes in the present method are developed and compared. • Each explicit scheme is second-order accurate and provides maximal stability bound. • Each explicit scheme (s > 1) can control numerical dissipation at the bifurcation point. This paper constructs a composite s -sub-step explicit method and analyzes second-order accuracy, conditional stability, and dissipation control at the bifurcation point. In the present s -sub-step method, each explicit scheme achieves identical second-order accuracy for analyzing general structures and provides maximal stability bound, that is 2 × s where s denotes the number of sub-steps. Except for the single-sub-step case, each explicit scheme achieves dissipation control at the bifurcation point. After registering second-order accuracy and controllable numerical dissipation, the composite multi-sub-step explicit method should be well-designed to reach maximal stability bound. The analysis reveals that as the number of sub-steps increases, the developed explicit schemes can reduce numerical low-frequency dissipation and enlarge stability. Under the same computational cost, the advantage of reducing low-frequency dissipation and enlarging stability is gradually weakened with the increase of sub-steps, so the first seven explicit schemes are only developed and compared in this paper. Some typical experiments are provided to confirm the methods' numerical performance. The proposed explicit schemes are more accurate and efficient for some models than existing second-order algorithms of that class. [ABSTRACT FROM AUTHOR]