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In this paper, a previous coarse-grain model [J. T. Padding and W. J. Briels, J. Chem. Phys. 117, 925 (2002)] to simulate melts of linear polymers has been adapted to simulate polymers with more complex hierarchies. Bond crossings between highly coarse-grained soft particles are prevented by applying an entanglement algorithm. We first test our method on a virtual branch point inside a linear chain to make sure it works effectively when linking two linear arms. Next, we apply our method to study the diffusive and rheological behaviors of a melt of three-armed stars. We find that the diffusive behavior of the three-armed star is very close to that of a linear polymer with the same molecular weight, while its rheological properties are close to those of a linear chain with molecular mass equal to that of the longest linear sub-chain in the star. [ABSTRACT FROM AUTHOR]

In the following paper we present a new type of optimization algorithms adapted for neural network training. These algorithms are based upon sequential operator splitting technique for some associated dynamical systems. Furthermore, we investigate through numerical simulations the empirical rate of convergence of these iterative schemes toward a local minimum of the loss function, with some suitable choices of the underlying hyper-parameters. We validate the convergence of these optimizers using the results of the accuracy and of the loss function on the MNIST, MNIST-Fashion and CIFAR 10 classification datasets. [ABSTRACT FROM AUTHOR]

In order to solve the problem that communication and computing resources are consumed in the first order multiagent system circle formation with communication delay, this paper introduced an event-driven control mechanism, and designed the control law for arbitrary circle formation, which was coupling with state dependent and state independent event-driven conditions. Event-triggered function was based on state error, made the information transmission between two neighbor agents and the controller updates driven by properly defined events. It proved the convergence of the control law strictly and theoretically, and the numerical simulation validated the effectiveness of control algorithm. The simulation results also s how that the updating frequency of the controller and the resource consumption of the agent are reduced with the desired system performance. [ABSTRACT FROM AUTHOR]

A hybrid cryptosystem based on plaintext-related ghost imaging and elliptic curve public-key cryptographic algorithm (ECC) is proposed in this paper. Thereinto, the ECC algorithm is used to protect and distribute session keys and the optical encryption system with session keys is used to encrypt and decrypt images. Thousands of phase-only mask keys can be replaced by parameter keys in this method, which successfully reduces the pressure of key storage. With dynamic updating session keys, the attacks are effectively prevented and the security resilience is enhanced. In addition, the utilization of an ECC cryptosystem solves the inevitable problem of key management and dispatch in conventional ghost imaging encryption system. The feasibility, security and robustness of the method are further analyzed through computer simulations. [ABSTRACT FROM AUTHOR]

A log-likelihood ratio (LLR) test on the single Mth check node of an LDPC decoder is utilised to develop an improved low complexity stopping criterion that overall outperforms well-known stopping criteria. The average number of iterations required at low SNRs is significantly reduced without sacrificing the BER performance. At high SNRs, the proposed algorithm can be modified to adaptively switch off to minimise its impact on the BER. Simulation results are presented for the rate 1/2 (288, 576) and rate 1/2 (1152, 2304) WiMAX 802.16e LDPC codes with binary phase shift keying (BPSK) over an AWGN channel for well-known criteria and the proposed algorithms in this paper. The performance of a Genie algorithm that would with hindsight stop the decoder to minimise the BER is used to compare all algorithms. [ABSTRACT FROM AUTHOR]

The rigid graph needs to be decomposed to solve the multi-equilibrium problem of the multi-agent formation control based on navigation function method. In this paper, a theorem and a scalable algorithm based on the Henneberg sequence of graphs are proposed for the decomposition of minimally rigid graph. The theorem demonstrates that if graph G (V, E) is a minimally rigid graph, then it can be decomposed as G = Gt ∪ Gc, where G t is a spanning tree of G and G c contains the remaining edges and their vertices. Moreover, the scalable algorithm is given to construct G t = (V t , E t ) and G c = (V c , E c ), and assign the edges in E c to n -- 2 distinct vertices in a scalable way via communication. Furthermore, a lemma is given to show when the number of vertices is less than eight, any edge can be chosen as the initialized edge, and the scalable algorithm mentioned above is always feasible. Finally, the effectiveness of the scalable algorithm is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper proposes an image encryption scheme using the combination of chaos, hyper-chaos, and DNA sequence operation. The proposed scheme performs three stages of encryption operations. Those are selection-level hyper-chaotic sequence based DNA-shuffling operation, key-image based DNA-diffusion operation, and hyper-chaotic sequence based DNA-shuffling operation. The advantages of this scheme are higher key space, higher confusion or randomness of pixels, higher sensitivity to the keys and plaintext pixels, stronger resistivity to the noises, and lossless encryption and decryption. Moreover, selection-level hyper-chaotic sequence based DNA-shuffling operation generates more complexities in the confusion process which increases the strength of encryptions and decryptions. The computer simulation and security analyses confirm the good encryption results of the proposed scheme and strong resistivity to the commonly used attacks. [ABSTRACT FROM AUTHOR]

Multiple imputation by chained equations (MICE) has emerged as a leading strategy for imputing missing epidemiological data due to its ease of implementation and ability to maintain unbiased effect estimates and valid inference. Within the MICE algorithm, imputation can be performed using a variety of parametric or nonparametric methods. Literature has suggested that nonparametric tree-based imputation methods outperform parametric methods in terms of bias and coverage when there are interactions or other nonlinear effects among the variables. However, these studies fail to provide a fair comparison as they do not follow the well-established recommendation that any effects in the final analysis model (including interactions) should be included in the parametric imputation model. We show via simulation that properly incorporating interactions in the parametric imputation model leads to much better performance. In fact, correctly specified parametric imputation and tree-based random forest imputation perform similarly when estimating the interaction effect. Parametric imputation leads to slightly higher coverage for the interaction effect, but it has wider confidence intervals than random forest imputation and requires correct specification of the imputation model. Epidemiologists should take care in specifying MICE imputation models, and this paper assists in that task by providing a fair comparison of parametric and tree-based imputation in MICE. [ABSTRACT FROM AUTHOR]

Task planning involves automating the creation of the routes for vessels with known coordinates in a confined space. The management of vessel release in a given area affects the time required for a vessel to complete its voyage, and maximizing vessel performance involves identifying the shortest route. A key issue in automating the generation of the optimal (shortest) routes is selecting the appropriate mathematical apparatus. This paper considers an optimization method based on a recursive algorithm using Bellman-Ford routing tasks for large dimensions. Unlike other optimization techniques, the proposed method enables the shortest path to be assessed in a network model with a complex topology, even if there are arcs with negative weights. The practical implementation of the modified Floyd algorithm was demonstrated using a sample automated build and using it to calculate a network model with a complex topology, using an iterative procedure for a program prepared in MATLAB. Implementation of the computer model is simple, and unlike existing models, it eliminates restrictions associated with the presence of negative weights and cycles on a network and automates search shortcuts in ground branch functional means in MATLAB. To confirm the accuracy of the obtained results, we performed an example calculation using the network. The proposed algorithm and recursive procedure are recommended for finding energy-efficient solutions during the management of mobile objects on waterways. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to detect non-stationary frequency modulated signals under noise process uncertainty. The proposed method uses the combination of adaptive directional time–frequency distribution and modified Viterbi algorithm to robustly estimate the instantaneous frequency (IF) of the given signal. This IF information is then used to remove the frequency modulation from the given signal thus converting it into temporally correlated signal. This temporal correlation is then exploited to detect signals under noise power uncertainty. The effectiveness of the proposed detector is shown through numerical simulations. [ABSTRACT FROM AUTHOR]

It has long been conjectured that the tight upper bound for the mean steady-state waiting time in the GI/GI/1 queue given the first two moments of the interarrival-time and service-time distributions is attained asymptotically by two-point distributions. The two-point distribution for the interarrival time has one mass point at 0, but the service-time distribution involves a limit; there is one mass point at a high value, but that upper mass point must increase to infinity while the probability on that point must decrease to 0 appropriately. In this paper, we develop effective numerical and simulation algorithms to compute the value of this conjectured tight bound. The algorithms are aided by reductions of the special queues with extremal interarrival-time and extremal service-time distributions to D/GI/1 and GI/D/1 models. Combining these reductions yields an overall representation in terms of a D/RS(D)/1 discrete-time model involving a geometric random sum of deterministic random variables (the RS(D)), where the two deterministic random variables in the model may have different values, so that the extremal steady-state waiting time need not have a lattice distribution. Efficient computational methods are developed. The computational results show that the conjectured tight upper bound offers a significant improvement over established bounds. [ABSTRACT FROM AUTHOR]

Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ0 -based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of ℓ2 -penalized regression method for sHDMSS time-to-event data. Specifically, we extend the ℓ0 -based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ2 -penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time-to-event data by leveraging existing efficient algorithms for massive ℓ2 -penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time-to-event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates. [ABSTRACT FROM AUTHOR]

Layered transmission is studied in this paper to accommodate various device capabilities and channel conditions. The problem of efficiently broadcasting a common set of layer-encoded data from a single base station to multiple users over quasi-static fading channels is examined. Considering the maximum tolerable delay, a block-level utility is defined in terms of average probability of successful reception of each layer. Based on non-orthogonal multiple access scheme, we propose a complete algorithm to jointly optimize power allocation and transmission rates. By utilizing the idea of opportunistic scheduling, we first derive the success probability of one single layer in homogeneous scenarios, then extend the results to heterogeneous scenarios where users are subject to different channel statistics. The multi-layer optimization is realized by iteratively repeating the single-layer operations. Starting from the base layer, we first choose an optimal subset of users as the corresponding target group, then apply the single-layer allocation process until no power resources are left. Computer simulation and discussion confirm that our method maximizes the block-level utility at the same time balances the tradeoff between multiuser diversity gain and multicast gain. [ABSTRACT FROM AUTHOR]

Understanding the limitation of solely relying on statistical significance, researchers have proposed methods to draw biomedical conclusions based on clinical significance. The minimal clinically important significance is one of the most fundamental concepts to study clinical significance. Based on an anchor question usually available in the patients' reported outcome, Hedayat et al. presented a method to estimate minimal clinically important significance using the classification technique. However, their method implicitly requires that the binary outcome of the anchor question is equally likely, i.e. the balanced outcome assumption. This assumption cannot be guaranteed a priori when one designs the study; hence, it cannot be satisfied in general. In this paper, we propose a data adaptive method, which can overcome this limitation. Compared to Hedayat et al., our method uses a faster gradient based algorithm and adopts a more flexible structure of the minimal clinically important significance at the individual level. We conduct comprehensive simulation studies and apply our method to the chondral lesions and meniscus procedure study to demonstrate its usefulness and also its outperformance. [ABSTRACT FROM AUTHOR]

In developing products for rare diseases, statistical challenges arise due to the limited number of patients available for participation in drug trials and other clinical research. Bayesian adaptive clinical trial designs offer the possibility of increased statistical efficiency, reduced development cost and ethical hazard prevention via their incorporation of evidence from external sources (historical data, expert opinions, and real-world evidence), and flexibility in the specification of interim looks. In this paper, we propose a novel Bayesian adaptive commensurate design that borrows adaptively from historical information and also uses a particular payoff function to optimize the timing of the study's interim analysis. The trial payoff is a function of how many samples can be saved via early stopping and the probability of making correct early decisions for either futility or efficacy. We calibrate our Bayesian algorithm to have acceptable long-run frequentist properties (Type I error and power) via simulation at the design stage. We illustrate our approach using a pediatric trial design setting testing the effect of a new drug for a rare genetic disease. The optimIA R package available at https://github.com/wxwx1993/Bayesian_IA_Timing provides an easy-to-use implementation of our approach. [ABSTRACT FROM AUTHOR]

It is well-known that the stability of a first-order autonomous system can be determined by testing the symmetric positive definite solutions of associated Lyapunov matrix equations. However, the research on the constrained solutions of the Lyapunov matrix equations is quite few. In this paper, we present three iterative algorithms for symmetric positive semidefinite solutions of the Lyapunov matrix equations. The first and second iterative algorithms are based on the relaxed proximal point algorithm (RPPA) and the Peaceman–Rachford splitting method (PRSM), respectively, and their global convergence can be ensured by corresponding results in the literature. The third iterative algorithm is based on the famous alternating direction method of multipliers (ADMM), and its convergence is subsequently discussed in detail. Finally, numerical simulation results illustrate the effectiveness of the proposed iterative algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two different kinds of parameters, a vector and a matrix, the gradient algorithm cannot be used directly. Therefore, we decompose the original system model into two sub-models and proceed the identification problem by the collaboration between the two sub-models. By employing the gradient search and determining the optimal step-sizes, we present an auxiliary model based two-stage projection algorithm. However, in order to alleviate the sensitivity to the noise, we reselect the step-sizes and derive the auxiliary model based two-stage stochastic gradient (AM-2S-SG) algorithm. Based on the AM-2S-SG algorithm, an auxiliary model based two-stage multi-innovation stochastic gradient algorithm is proposed to generate more accurate estimates. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network. [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]

Clustered survival data in the presence of cure has received increasing attention. In this paper, we consider a semiparametric mixture cure model which incorporates a logistic regression model for the cure fraction and a semiparametric regression model for the failure time. We utilize Archimedean copula (AC) models to assess the strength of association for both susceptibility and failure times between susceptible individuals in the same cluster. Instead of using the full likelihood approach, we consider a composite likelihood function and a two-stage estimation procedure for both marginal and association parameters. A Jackknife procedure that takes out one cluster at a time is proposed for the variance estimation of the estimators. Akaike information criterion is applied to select the best model among ACs. Simulation studies are performed to validate our estimating procedures, and two real data sets are analyzed to demonstrate the practical use of our proposed method. [ABSTRACT FROM AUTHOR]

This research is based on the Derivative/Integration of the Power Spectrum for the reduction of noise in a periodic signal. The performed procedure, joined to a convolution process, results in the detection of the original values of amplitude, phase and frequency of the spectral components of the periodic signal. In this paper, second-order statistics foundations are presented and the validation of the proposed algorithm is exposed in both theoretical and practical sense. [ABSTRACT FROM AUTHOR]

Numerical methods that approximate the solution of the Vlasov-Poisson equation by a low-rank representation have been considered recently. These methods can be extremely effective from a computational point of view, but contrary to most semi-Lagrangian or Eulerian Vlasov solvers, they do not conserve mass and momentum, neither globally nor in respecting the corresponding local conservation laws. This can be a significant limitation for intermediate and long time integration. In this paper we propose a numerical algorithm that overcomes some of these difficulties and demonstrate its utility by presenting numerical simulations. [ABSTRACT FROM AUTHOR]

Of the wide palette of 5G features, ultra-wide bandwidth and large-scale antenna configuration are regarded as the essential enabling technology components for millimeter wave (mmWave) communication. Accurate knowledge of delay and angle information of multipath components is essential for many applications in mmWave systems. There is a strong need for a low computation-cost channel estimation algorithm for such systems, where typically adopted far-field and narrowband assumptions might be violated. In this paper, a generic yet novel beamspace uniform circular array (UCA) beamforming algorithm with successive cancellation scheme is proposed to jointly detect the impinging azimuth angle and delay of each multipath component. The proposed algorithm is computationally cheap and it works for ultra-wideband UCA systems in the near-field conditions as well as the traditional narrowband systems in the far-field conditions. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm, compared to the state-of-art works. [ABSTRACT FROM AUTHOR]

In this paper, we consider numerical approximations for solving the anisotropic Cahn–Hilliard model. We combine the Scalar Auxiliary Variable (SAV) approach with the stabilization technique to arrive at a novel Stabilized-SAV approach, where three linear stabilization terms, which are shown to be crucial to remove the oscillations caused by the anisotropic coefficient, are added to enhance the stability while keeping the required accuracy. The schemes are very easy-to-implement and fast in the sense that all nonlinear terms are treated in a semi-explicit way, and one only needs to solve three decoupled linear equations with constant coefficients at each time step. We further prove the unconditional energy stabilities rigorously and present numerous 2D and 3D numerical simulations to demonstrate the stability and accuracy. • We propose a novel Stabilized-SAV approach for solving the anisotropic Cahn–Hilliard model. • The scheme is linear, second-order accurate, provably unconditionally energy stable, and non-iterative. • Three crucial linear stabilization terms are added to remove oscillations caused by anisotropy. • One only need to solve three decoupled linear equations at each time step. • We further prove the unconditional energy stabilities rigorously and present numerous simulations. [ABSTRACT FROM AUTHOR]

In this paper, we present time-reversible simulation algorithms for rigid bodies in the quaternion representation. By advancing a time-reversible algorithm [Y. Kajima, M. Hiyama, S. Ogata, and T. Tamura, J. Phys. Soc. Jpn. 80, 114002 (2011)] that requires iterations in calculating the angular velocity at each time step, we propose two kinds of iteration-free fast time-reversible algorithms. They are easily implemented in codes. The codes are compared with that of existing algorithms through demonstrative simulation of a nanometer-sized water droplet to find their stability of the total energy and computation speeds. [ABSTRACT FROM AUTHOR]

We propose a parameter-free algorithm for the identification of nearest neighbors. The algorithm is very easy to use and has a number of advantages over existing algorithms to identify nearest-neighbors. This solid-angle based nearest-neighbor algorithm (SANN) attributes to each possible neighbor a solid angle and determines the cutoff radius by the requirement that the sum of the solid angles is 4π. The algorithm can be used to analyze 3D images, both from experiments as well as theory, and as the algorithm has a low computational cost, it can also be used 'on the fly' in simulations. In this paper, we describe the SANN algorithm, discuss its properties, and compare it to both a fixed-distance cutoff algorithm and to a Voronoi construction by analyzing its behavior in bulk phases of systems of carbon atoms, Lennard-Jones particles and hard spheres as well as in Lennard-Jones systems with liquid-crystal and liquid-vapor interfaces. [ABSTRACT FROM AUTHOR]

Using computer simulations to model the folding of proteins into their native states is computationally expensive due to the extraordinarily low degeneracy of the ground state. In this paper, we develop an efficient way to sample these folded conformations using Wang Landau sampling coupled with the configurational bias method (which uses an unphysical 'temperature' that lies between the collapse and folding transition temperatures of the protein). This method speeds up the folding process by roughly an order of magnitude over existing algorithms for the sequences studied. We apply this method to study the adsorption of intrinsically disordered hydrophobic polar protein fragments on a hydrophobic surface. We find that these fragments, which are unstructured in the bulk, acquire secondary structure upon adsorption onto a strong hydrophobic surface. Apparently, the presence of a hydrophobic surface allows these random coil fragments to fold by providing hydrophobic contacts that were lost in protein fragmentation. [ABSTRACT FROM AUTHOR]

A controlled quantum system possesses a search landscape defined by the target physical objective as a function of the controls. This paper focuses on the landscape for the transition probability Pi → f between the states of a finite level quantum system. Traditionally, the controls are applied fields; here, we extend the notion of control to also include the Hamiltonian structure, in the form of time independent matrix elements. Level sets of controls that produce the same transition probability value are shown to exist at the bottom Pi → f = 0.0 and top Pi → f = 1.0 of the landscape with the field and/or Hamiltonian structure as controls. We present an algorithm to continuously explore these level sets starting from an initial point residing at either extreme value of Pi → f. The technique can also identify control solutions that exhibit the desirable properties of (a) robustness at the top and (b) the ability to rapidly rise towards an optimal control from the bottom. Numerical simulations are presented to illustrate the varied control behavior at the top and bottom of the landscape for several simple model systems. [ABSTRACT FROM AUTHOR]

Most existing photoacoustic tomography techniques require collecting complete projection data that are acquired on a defined circle surrounding the object. However, in clinical application, the object can only be approached from a limited angle mostly. Furthermore, with the incomplete projection data acquired in limited view, the general filtered backprojection algorithm will streak image artifacts nearby the reconstructed location of absorbers. In this paper, we present a limited-view-scanning photoacoustic imaging system with a linear transducer array and develop a filtered mean-backprojection-iteration (FMBPI) algorithm to reconstruct the absorbed optical deposit distribution. The FMBPI algorithm combines the terseness of the filtered backprojection algorithm with the accuracy of the iterative reconstruction algorithm. Numerical simulation and experimental results validate that the algorithm can effectively reconstruct high-quality image with limited-view data. It is also demonstrated that with the FMBPI algorithm, the limited-view-scanning multielement photoacoustic imaging system has a great potential to be applied in fast noninvasive clinic diagnosis of breast cancer at the early stage. [ABSTRACT FROM AUTHOR]

Density functional theory as proposed by Rosenfeld [Phys. Rev. Lett. 63, 980 (1989)] is used to study hard sphere mixture exposed by cylindrically symmetric external field. Exploiting the symmetry of the system, explicit formulas for the weighted densities are derived. The resulting density profiles are compared with new grand canonical Monte Carlo simulations. The comparison reveals very good agreement between the predicted and simulated results even at high densities and very narrow pores. Finally, simple algorithms for computing complete elliptic functions of the first and second kinds that occur in the derived formulae are presented to make the paper self-contained. [ABSTRACT FROM AUTHOR]

This paper examines a private distributed online convex optimization problem in which each agent strives to minimize the sum of objective functions while also tending to keep their individual objective functions confidential. We use differential privacy as the metric to safeguard each agent's privacy and offer a distributed online mirror descent technique that is differentially private. We demonstrate that for strongly convex objective functions, our proposed algorithm satisfies the expected regret bound of O ln T while maintaining differential privacy, where T is the number of iterations. The established expected regret bound matches the optimal theoretical regret bound with respect to T. We further illuminate the trade-off between the extent of privacy-preserving and the expected regret bound through numerical simulations. [ABSTRACT FROM AUTHOR]

• Improved iterative method based on Gerchberg-Saxton (GS) algorithm. • Red, green, and blue channels of images is recorded in three holograms. • Constraint that consists of modified amplitudes with a weighting factor. • Reconstruction for three layer images. Depth-division multiplexing (DDM) is distinguished for generating full-color holograms. However, this method is confronted with such issues as poor quality and severe crosstalk when reconstructing multiplane color images. In this paper, we propose an improved iterative method based on Gerchberg-Saxton (GS) algorithm to generate multiplane full-color phase-only holograms (POHs), and the information of red (R), green (G), and blue (B) channels of images is recorded in three POHs, respectively. In the iterative process, we introduce an improved hologram plane constraint that consists of modified amplitudes given a static weighting factor, and display the reconstruction results for three-layer images, which ultimately improve the quality of the reconstructed images and effectively avoid the crosstalk among different color channels. And compared with GS algorithm, our method can effectively balance the reconstruction quality of three-color channels of each image. The feasibility of the method is verified by numerical simulations and optical experiments. [ABSTRACT FROM AUTHOR]

A novel theory of an equation of state based on excluded volume and formulated in two preceding papers for gases and gaseous mixtures is extended to the entire density range by considering higher (beginning from the third) approximations of the theory. The algorithm of constructing higher approximations is elaborated. Equations of state are deduced using the requirement of maximum simplicity and contain a single free parameter to be chosen by reason of convenience or simplicity or to be used as a fitting parameter with respect to the computer simulation database. In this way, precise equations of state are derived for the hard-sphere fluid in the entire density range. On the side, the theory reproduces most known earlier equations of state for hard spheres and determines their place in the hierarchy of approximations. Equations of state for van der Waals fluids are also presented, and their critical parameters are estimated. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

In this paper, we present computer simulation results concerning interdiffusion of fully compatible components in symmetric binary (AB) polymer mixtures in solutions. The simulation is performed in two dimensions using the algorithm based on the dynamic lattice liquid model. The solvent molecules are taken into account explicitly. The evolution of the concentration profiles in time at an interface is studied for chain lengths N=2,4,8,16 for three polymer concentrations [lowercase_phi_synonym]=0.1,0.5,0.9. The tracer diffusion coefficients for polymer chains and for the solvent are obtained by monitoring the mean square displacements of their center of mass. The relationships between coefficients of interdiffusion and self-diffusion are tested. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green’s function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time. [ABSTRACT FROM AUTHOR]

Background: With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user.Methods: The dataset used in this study consisted of several months of biometric time-series data gathered by seven users independently. Four recurrent neural network (RNN) architectures-as well as a deep neural network and a simple regression model-were proposed to investigate the performance on predicting the activeness of the user under various length-related hyper-parameter settings. In addition, the learned model was tested to predict the time period when the user's activeness falls below a certain threshold.Results: A preliminary experimental result shows that each type of activeness data exhibited a short-term autocorrelation; and among the three types of data, the consumed calories and the number of footsteps were positively correlated, while the heart rate data showed almost no correlation with neither of them. It is probably due to this characteristic of the dataset that although the RNN models produced the best results on modeling the user's activeness, the difference was marginal; and other baseline models, especially the linear regression model, performed quite admirably as well. Further experimental results show that it is feasible to predict a user's future activeness with precision, for example, a trained RNN model could predict-with the precision of 84%-when the user would be less active within the next hour given the latest 15 min of his activeness data.Conclusions: This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user. [ABSTRACT FROM AUTHOR]

Psychological factors affect the movement of people in the competitive or panic mode of evacuation, in which the density of pedestrians is relatively large and the distance among them is small. In this paper, a crowd is divided into groups according to their social relations to simulate the actual movement of crowd evacuation more realistically and increase the attractiveness of the group based on social force model. The force of group attraction is the synthesis of two forces; one is the attraction of the individuals generated by their social relations to gather, and the other is that of the group leader to the individuals within the group to ensure that the individuals follow the leader. The synthetic force determines the trajectory of individuals. The evacuation process is demonstrated using the improved social force model. In the improved social force model, the individuals with close social relations gradually present a closer and coordinated action while following the leader. In this paper, a grouping algorithm is proposed based on grid density and relationship via computer simulation to illustrate the features of the improved social force model. The definition of the parameters involved in the algorithm is given, and the effect of relational value on the grouping is tested. Reasonable numbers of grids and weights are selected. The effectiveness of the algorithm is shown through simulation experiments. A simulation platform is also established using the proposed grouping algorithm and the improved social force model for crowd evacuation simulation. [ABSTRACT FROM AUTHOR]

A hybrid analytical/numerical conducted an electromagnetic interference (EMI) modeling approach, which adopts both numerical method and analytical method, is proposed in this paper. In this model, the noise source is calculated directly with the original data from oscilloscopes. The noise path at different operating status is extracted from the original converter topology and its analytical expression is then derived. Also, according to the EMI receiver detector mode, the algorithm of the EMI receiver is proposed in this paper. Peak (PK), average (AV), and quasipeak (QP) values of the EMI can then be calculated to analyze the impact of each relevant component. The analytical method is provided to carry out PK, AV, and QP simulations accurately and fast. The simulation and experimental results for common mode leakage currents validate this EMI model. Furthermore, two analysis examples are given to illustrate how this model is applied for engineers. [ABSTRACT FROM AUTHOR]

In a previous paper, Duchêne and Rigo introduced the notion of invariance for take-away games on heaps. Roughly speaking, these are games whose rulesets do not depend on the position. Given a sequence S of positive tuples of integers, the question of whether there exists an invariant game having S as set of P -positions is relevant. In particular, it was recently proved by Larsson et al. that if S is a pair of complementary Beatty sequences, then the answer to this question is always positive. In this paper, we show that for a fairly large set of sequences (expressed by infinite words), the answer to this question is decidable. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based optimal control algorithm for the clamp switch of a zero-voltage switching (ZVS) bidirectional DC-DC converter. The bidirectional DC-DC converter (BDC) can accomplish the ZVS operation using the clamp switch. The minimum current for the ZVS operation is maintained, and the inductor current is separated from the input and output voltages by the clamp switch in this topology. The clamp switch can decrease the inductor current ripple, switching loss, and conduction loss of the system. Therefore, the optimal control of the clamp switch is significant to improve the efficiency of the system. This paper proposes a model-based optimal control algorithm using phase shift in a micro-controller unit. The proposed control algorithm is demonstrated by the results of PSIM simulations and an experiment conducted in a 1-kW ZVS BDC system. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) give users the opportunity to access different access points(APs), which will simultaneously affect user performance and system performance, so user association in HetNets plays a critical role in enhancing the load balancing and the system sum-throughput of networks. Meanwhile, the incremental sum-throughput currently fails to meet the escalating data demands. Besides, ensuring fairness amongst users constitutes another urgent issue in the radio resource management (RRM) of HetNets. What is more, few works consider the maximum service user number constraint in femtocell access points (FAPs). To solve the aforementioned problem, this paper associates users to APs by considering system sum-throughput and fairness at the same time in HetNets under a maximum service user number constraint of FAPs; accordingly, the user association problem is formulated. By releasing constraint, the optimal user association algorithm is obtained by Lagrangian function, and based on this optimal solution, a low complexity suboptimal user association algorithm is proposed. At last, this paper investigates the relationship between system sum-throughput and maximum service user number of FAPs. Numerical simulation results show that the proposed algorithm can improve sum-throughput and fairness at the same time at a specific maximum service user number of FAPs. [ABSTRACT FROM AUTHOR]

This paper proposes a new pretension simulation algorithm and examines the pretension feasibility for a new type of cable dome: a negative Gaussian curvature cable dome. First, the paper gives a pretension simulation algorithm based on the fuzzy relationship between the prestress and internal force. Second, a negative Gaussian curvature cable dome is proposed and the corresponding experimental model is designed and pretensioned according to the simulation results from the proposed simulation algorithm. The results indicate that the pretension simulation algorithm is efficient and accurate for simulating the pretension process and guiding the practical pretension. And the results also confirm that the negative Gaussian curvature cable dome can be pretensioned successfully, thus it can be constructed as a new type of cable dome. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

This paper presents an optimal battery management algorithm for photovoltaic (PV) stations capacity firming using dynamic programming-based optimization and control architecture. The objective is to manage the output of battery energy storage system (BESS) so that PV station output swings are reduced significantly. First, BESS and PV station located in a medium-voltage distribution network on a North American power grid is modeled along with the reduced-order feeder model. Then, the proposed algorithm is designed and implemented on the power grid distribution feeder based on the feeder data streamed through utility communication infrastructure to the research laboratory. The theoretical formulation, simulation, and implementation results are discussed in detail in this paper. [ABSTRACT FROM AUTHOR]

This paper concerns a special type of eddy-current sensor in the form of inductive loops. Such sensors are applied in the measuring systems classifying road vehicles. They usually have a rectangular shape with dimensions of 1×2 m, and are installed under the surface of the traffic lane. The wide Point Spread Function (PSF) of such sensors causes the information on chassis geometry, contained in the measurement signal, to be strongly averaged. This significantly limits the effectiveness of the vehicle classification. Restoration of the chassis shape, by solving the inverse problem (deconvolution), is also difficult due to the fact that it is ill-conditioned. An original approach to solving this problem is presented in this paper. It is a hardware-based solution and involves the use of inductive loops with an asymmetrical PSF. Laboratory experiments and simulation tests, conducted with models of an inductive loop, confirmed the effectiveness of the proposed solution. In this case, the principle applies that the higher the level of sensor spatial asymmetry, the greater the effectiveness of the deconvolution algorithm. [ABSTRACT FROM AUTHOR]

Owing to current transformer (CT) saturation caused by a decaying direct current component in the magnetizing inrush current, the restricted earth fault (REF) protection of a power transformer could maloperate during magnetizing inrush. This paper presents a new transformer restricted earth fault relay algorithm that can prevent false relay tripping and provide proper action during the energization of a faulty transformer. The integral of the product of the signals that are compared is calculated over 1 half-period of the signals. The performance of the proposed algorithm is tested and verified using a large number of transformer energization trials and fault cases. The signals required for testing of the algorithm were obtained by laboratory measurements, and then the algorithm was tested off-line. The inuence of different levels of CT saturation due to different remanent uxes in CT cores during power transformer energization was investigated. By applying computer simulations, the signals required for algorithm testing were generated. The investigation results presented in this paper show that the new algorithm provided sensitive protection for the power transformer for phase to ground faults and high security and stability during magnetizing inrush conditions in conjunction with CT saturation. [ABSTRACT FROM AUTHOR]