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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]

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]

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]

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 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]

• We firstly give an analysis solution of the proximal operator of the L1 over Linf in compressed sensing. • We construct a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) based on the Moreau envelop of the L1/Linf. • We design a specific and convergent variable-splitting scheme of the alternating direction method of multipliers (ADMM) by introducing a simple auxiliary variable. In this paper, we study minimization of the ratio of ℓ 1 and ℓ ∞ norms (ℓ 1 / ℓ ∞) as a nonconvex and sparsity-promoting metric for solving the unconstrained compressed sensing problems. To design some efficient algorithms, we derive a closed-form solution to the proximal operator of the ℓ 1 / ℓ ∞ function. With the newly variant of the shrinkage operator, we propose a novel variant of fast iterative shrinkage-thresholding algorithm (FISTA) and a specific variable-splitting scheme of the alternating direction method of multipliers (ADMM) which is guaranteed to have sub-sequential convergence. Experimentally, we construct extensive numerical simulations to demonstrate the efficiency of these two proposed approaches over the state-of-the-art algorithms in sparse recovery. [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]

In this paper, a general framework is proposed for the analysis and characterization of observability and diagnosability of finite state systems. Observability corresponds to the reconstruction of the system’s discrete state, while diagnosability corresponds to the possibility of determining the past occurrence of some particular states, for example faulty states. A unifying framework is proposed where observability and diagnosability properties are defined with respect to a critical set, i.e. a set of discrete states representing a set of faults, or more generally a set of interest. These properties are characterized and the involved conditions provide an estimation of the delay required for the detection of a critical state, of the precision of the delay estimation and of the duration of a possible initial transient where the diagnosis is not possible or not required. Our framework makes it possible to precisely compare some of the observability and diagnosability notions existing in the literature with the ones introduced in our paper, and this comparison is presented. [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]

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]

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]

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]

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]

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]

This paper studies the stabilization problem for a class of unified chaotic systems subject to uncertainties and input nonlinearity. Based on the sliding mode control theory, we present a new method for the sliding mode controller design and the control law algorithm for such systems. In order to achieve the goal of stabilization unified chaotic systems, the presented controller can make the movement starting from any point in the state space reach the sliding mode in limited time and asymptotically reach the origin along the switching surface. Compared with the existing literature, the controller designed in this paper has many advantages, such as small chattering, good stability, and less conservative. The analysis of the motion equation and the simulation results all demonstrate that the method is effective. [ABSTRACT FROM AUTHOR]

Harvesting daylight for improving building energy performance has become an important issue in recent years. Most energy simulation tools use a Daylight Factor (DF) based algorithm to capture the complexity of daylight behaviors. However, DF has a limitation for fully accounting realistic daylight conditions, and several modifications or integrations with other supporting tools were developed to compensate for this limitation. This paper uses the Kriging method to predict daylight conditions for a whole year, and couples this method with energy simulation tools. Kriging is a group of geo-statistical techniques used to interpolate the value of a random field at an unobserved location from discrete observed values at nearby locations. In previous research, the Kriging method showed promising outcomes; that result was significantly better than current daylight modeling in energy simulation tool, and very close to the results from advanced physics-based daylight simulation tool. However, Kriging also comes with certain limitations that require further investigation. One major hurdle was Kriging’s long computational time compared to current daylight models in energy simulation tools. For this reason, this paper aims to find possibilities to reduce computational time to realistic levels that can be applicable in practice. Furthermore, it proposes a method to integrate Kriging-based daylight model with an energy simulation tool that can plot hourly indoor light distribution. This proposal obtains more realistic results than current energy simulation daylight models, while reducing computational expense. [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 the context of Intermittently Connected Mobile Networks, researchers have proposed different collaborative routing schemes with fundamental objectives to maximize packets delivery, and minimise latency through improving peers' routing decisions. One promising approach adopted by researchers to improve peers' routing decisions is a trust-based routing mechanism. Most current trust-based protocols in the literature enable peers to make routing decisions based on trust relationships that manifest among peers but with less concern about the peers' attributes. This further made the design of efficient, collaborative routing protocols a challenging task due to the high dynamics of peers' characteristics and mobility. In this paper, we hypothesise that in a trust-based routing protocol, it will be additional trust evaluation reliability if peers can take into consideration their partners attributes' status in addition to the trust relationships. We propose DATM protocol; a trust-based scheme to enforce collaborative behaviour in wireless mobile networks taking into consideration the peers' attributes for an efficient routing scheme. We introduce a personalised similarity algorithm for peers' attributes modelling as a scaling factor for trust evaluation. The relative comparison of the DATM and PROPHET protocol using simulation shows the improved performance of our proposed model in terms of messages delivery, messages overhead and average latency. [ABSTRACT FROM AUTHOR]

The principal function of circuit breakers (CBs) is to isolate a portion of the power network from the rest of it in a timely manner following an opening command. Any failure in the opening operation of CBs, especially following a fault condition, will almost certainly result in a catastrophic event. Therefore, the issue of failure detection of CBs is essential and has a vital role in power system protection. This paper presents a novel power-based algorithm for failure detection of CBs. The power loss of CBs due to an arcing event increases as the arcing time gets longer. The arcing time of CBs increases with defective operations originating from any failure or malfunction for different reasons. The proposed method uses the power difference between the input and output terminals of CBs to calculate the power loss due to an arcing event, which can then be used to estimate the arcing energy. A trip signal is initiated whenever the estimated arcing energy for a single opening operation reaches a predefined trip level, which shows a failure in CB operation. This predefined trip value is related to the integration of the maximum power capacity of the CB during an opening operation time. Computer simulation studies conducted to analyze the performance of the proposed technique demonstrate that the algorithm is capable of discriminating between normal and abnormal arcing energies and thus identifying malfunctions or failures in the opening operations of CBs. [ABSTRACT FROM AUTHOR]

The protection characteristics used for the detection of power systems’ oscillations are adjusted according to the recommendations of the equipment manuals and validated through transient stability studies. Once the characteristics are adjusted, the parameters do not change when topological changes occur in the system, which opens up the possibility of malfunction of the protection equipment, either by not considering special cases of failure during the study or by errors in the system. This paper describes the modeling and implementation of a virtual out-of-step protection algorithm based on traditional, unconventional methods. The proposed framework allows the user to implement a customized out-of-step protection algorithm and analyze a real fault event recorded in a COMTRADE file. Furthermore, the programming advantages include the possibility to import COMTRADE files from specific simulations and assess the adequate settings of the protective relay. The result brings a computational tool compatible with different simulation programs capable of reproducing stable and unstable power swing events through playback techniques. By assessing critical cases in which the omission of the tripping is reflected in a generator slip condition, this study proposes an alternative with the instantaneous power theory to analyze the oscillation conditions in the definite time frame. [ABSTRACT FROM AUTHOR]

A synchronous reluctance motor drive exhibits an inherent unlimited speed range capability; in fact, the maximum speed of this kind of machine is bounded only by mechanical limits. In order to exploit this peculiar characteristic of the drive while assuring the lowest power losses, a proper control algorithm has to be used. In particular, while the working speed is increasing, the control must be able to command the reluctance motor under the maximum torque per ampere condition at first, into the flux-weakening region, and then on the maximum torque per voltage trajectory. To this purpose, a novel voltage control loop is presented in this paper. It is combined with a field-oriented current control and profitably exploits a polar coordinate representation of the current reference to operate the reluctance machine in compliance with current and voltage rating constraints in all the different operating conditions. Additional merits of the proposed scheme are the ease of implementation and the simple design. The stability of the proposed control algorithm is proved through a small signal analysis and simulations and experimental tests confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Highlights • A three-phase phase-locked loop algorithm is introduced. • Inherent immunity to subharmonics and interharmonics is achieved. • An adaptive filter is employed to improve disturbance rejection. • A proper comparison with similar approaches is presented. Abstract This paper introduces a synchronization algorithm for the detection of the fundamental positive-sequence component in three-phase systems. The structure is based on the correlation of the input signal with a complex signal generated from the association of an adaptive filter and a phase-locked loop (PLL) method previously presented in literature. Good disturbance rejection is then achieved, in particular to interharmonics and subharmonics. Simulation and experimental results are presented and discussed in order to demonstrate the performance of the proposed solution when compared with that regarding dual second-order generalized integrator frequency locked loop (DSOGI-FLL), which is often considered to present good disturbance rejection. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We proposed and test a DSE based algorithm to detect traveling waves in transmission lines. • Performance was validated with transient simulations and playback testing using Bergeron and frequency dependent parameter models. • The method was compared to legacy protections with TWs differentiator smoother and impedance based relays. • The method showed a successful performance with different sampling rates (lower sampling rates) which is one of the main contributions. • The method was also assessed with different types of fault with the EMTP case study and a real Colombian overhead transmission line. Abstract A dynamic state estimation (DSE) approach is proposed to improve the accuracy to identify the front-wave arrival times of a two-terminal traveling wave protection scheme for a transmission line with low sampling rates. The solution is implemented as an offline method aiming to use existing infrastructure measurements. The work presents the principles of traveling wave (TW) theory and legacy protection schemes with this methodology. The proposed method requires GPS synchronized measurements at both ends of the line and a high fidelity model of the protected line. The paper presents comparisons between the differentiator smoother for arrival time identification, traditional impedance based fault location using 1 or 2 terminals and the proposed DSE-TW. The performance is also assessed with the Bergeron model and the frequency dependent parameter model of the transmission line. Numerical simulations and laboratory tests demonstrate that the method can correctly identify the front-wave arrival time at each end with better accuracy and different sampling rates. This work illustrates the advantages of DSE-TW such as high accuracy of arrival time identification, the minimum error of fault location and better performance with different (lower) sampling rates. [ABSTRACT FROM AUTHOR]

Highlights • Two incidence matrices are proposed for complete modeling of distribution systems. • A review of various distributed generations is provided in power flow studies. • An efficient power flow algorithm for distribution systems is proposed. • Constant power-voltage buses are handled by a new in-program sensitivity matrix. • Accuracy, performance, and robustness of the model are validated by simulations. Abstract Real-time operation, analysis and control of active distribution systems (DSs) mainly rely on accurate power flow (PF) results. Almost all decisions are made considering PF calculations. On the other hand, efficiency of PF algorithm should be updated to comply with new smart grid paradigms which affect traditional management methods of DSs. A new graph theory based PF algorithm is proposed in this paper for active electrical DSs including various distributed generation (DG) models, three phase equipment and meshed topologies. At first, two incidence matrices are proposed for modeling of DSs. These matrices could successfully handle meshed topologies and unbalanced networks. They could also be modified to consider various three phase equipment such as transformers and voltage regulators. Moreover, a comprehensive study of various modeling methods of DGs in PF studies is presented. In order to handle DG models, a new sensitivity matrix is proposed for in-algorithm calculation of reactive power injection of PV buses. Since, the proposed incidence matrices provide a decoupled model of the network, any topology and characteristic change could be simply applied in this PF algorithm. The proposed PF algorithm was tested in several simulations in various test systems. The results of accuracy test, performance test, and robustness test show capability of the proposed PF algorithm for handling various situations in different PF calculations. [ABSTRACT FROM AUTHOR]

Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming. [ABSTRACT FROM AUTHOR]

In this paper, a novel scheme is proposed to optimize the effectiveness of the device-to-device (D2D) enabled vehicular communications (D2D-V), which are underlaid in cellular networks, where the uplink channel allocated to one cellular user (CU) is reused by the multiple D2D-V links. To develop a well-functioning D2D-V system, the sum rate of all D2D-V links is necessary to be maximized, and also the reliability of the co-channel CU to infrastructure has to be guaranteed with the CU interference constraint. Unlike the traditional statical D2D systems, the D2D-V system suffers with high mobility and channel uncertainty. Therefore, the CU interference constraint is formulated as a probabilistic function by the Bernstein approximation, the constraint attempts to address the mobile channel fluctuations. Besides, the tractable approximate constraint is reformulated as two separable structure, this reformulated constraint attempts to determine the near-optimal solutions more efficiently. Since the objective function with a logarithmic form is nonconvex, successive convex approximation is applied to transform the nonconvex problem into the convex problem. The dual decomposition is used to determine the near-optimal solutions. After the problem is reformulated, a distributed robust power control algorithm is proposed to perform the chance-constrained optimization. Numerical simulations are used to evaluate the performance of the proposed scheme. The simulation results show the impacts of system performance when applying the proposed scheme in vehicular environments with high mobility. The proposed optimization schemes are further verified by comparing with the existing methods. [ABSTRACT FROM AUTHOR]

Highlights • Simulation of microwave rotating heating is realized by ALE method. • The simulation results compared with conventional methods is more accurate and efficient. • This method can be used for microwave heating simulation of other food movements. Abstract In a particular condition, moving elements are typically used to improve heating uniformity during microwave heating. However, due to the complexity of mathematical models, the simulation of moving elements during microwave heating process remains a challenge. In this paper, a novel continuous algorithm based on Arbitrary Lagrangian-Eulerian method is developed to compute this heating process. By introducing the ALE method, the mesh of moving domain are set as a function of space coordinate and time, the mesh of surrounding domain is free deformation under the action of the moving domain, ensuring the accuracy and continuity of calculation by remesh the deformed configuration. A 2-D model and a 3-D model with a rotating turntable are used to illustrate the proposed method in detail. The change of temperature was measured by experiments to verify the rationality and accuracy of the proposed method. This method can be used in microwave heating simulation of other food movements and assist microwaveable food product development. [ABSTRACT FROM AUTHOR]

Background: Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. Results: In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1. Conclusion: With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions. [ABSTRACT FROM AUTHOR]

Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson's disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews' correlation coefficient (MCC) parameters. [ABSTRACT FROM AUTHOR]

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]