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Dual polarization quadrature phase shifting keying (DP-QPSK) modulation format along with coherent receiver helps in increasing the data carrying capability of existing optical networks without any major change in existing transmission infrastructure. The various linear and nonlinear fiber effects, frequency and phase errors are corrected in electrical domain at the receiver end with digital back propagation algorithms (DBP) instead of in-line compensation. In such a case the selection of optimum values of system parameters make the task easier for DBP algorithms. This paper highlights the importance of finding optimum operating point of continuous modulus algorithm (CMA) for better adaptive equalization (AE). The paper also discusses the optical pulse shaping using Gaussian optical band-pass filter to improve the spectral characteristics of DP-QPSK signal. [ABSTRACT FROM AUTHOR]

This paper analyses a novel two-step Monte Carlo simulation algorithm to estimate the weighted volume of a polytope of the form A ⁢ z ≤ T . The essential idea is to partition the columns of A into two categories – a lightweight category and a heavyweight category. Simulation is done in a two-step manner where, for every sample of the lightweight category variables we use multiple samples of the heavyweight category variables. Thus, the heavyweight category variables are oversampled with respect to the lightweight category variables and increasing samples of the heavyweight variables at the expense of the lightweight variables will lead to a more efficient Monte Carlo method. In this paper we present a fast heuristic approximate for estimating the optimal oversampling ratio and substantiate with experimental results which confirm the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Ten Gigabit-capable ethernet passive optical network (GEPON) can quickly improve network bandwidth, meet multilevel business needs, and quickly improve the comprehensive competitiveness of the network. It is well known that the 10G EPON access network technology is used to carry IPTV services, VoIP services and high-speed Internet access, known as "triple play", and IPTV services have been particularly popular in recent years. As the demand for IPTV service increases exponentially, a large amount of data will be generated, occupying a large access bandwidth and causing an impact on other services. Therefore, reasonable scheduling of IPTV service queue is an urgent problem to be solved under multiservice environment at present. Therefore, the design of PON bearing IPTV robustness algorithm to meet user experience is the focus of this paper. This paper proposes a Forward Error Correction with Weight Round Robin algorithm (FWRR), which adopts RS error correction code and Weighted Round Robin algorithm based on IP layer. When IPTV packet is lost at optical line terminal (OLT) end, downlink optical network unit (ONU) can recover the lost data through received packets. This mechanism can effectively solve the problem of packet loss caused by the blocking of OLT end, and realize the scheme of PON carrying IPTV multicast service. Simulations results in OPNET show that compared with weighted round robin (WRR) algorithm and round robin (RR) algorithm, FWRR mechanism can reduce packet loss, delay and jitter, in addition, improve the robustness of IPTV service in GEPON. [ABSTRACT FROM AUTHOR]

Conjugate gradient (CG) methods are a popular class of iterative methods for solving linear systems of equations and nonlinear optimization problems. In this paper, a new hybrid conjugate gradient (CG) method is presented and analyzed for solving unconstrained optimization problems, where the parameter β k is a convex combination of β k WYL and β k CD . Under the strong Wolfe line search, the new method possesses the sufficient descent condition and the global convergence properties. The preliminary numerical results show the efficiency of our method in comparison with other CG methods. Furthermore, the proposed algorithm HWYLCD was extended to solve the problem of a mode function. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new algorithm for estimating the lower bounds for the star discrepancy of any arbitrary point sets in [ 0 , 1 ] s . Computing the exact star discrepancy is known to be an NP-hard problem, so we have been looking for effective approximation algorithms. The star discrepancy can be thought of as the maximum of a function called the local discrepancy, and we will develop approximation algorithms to maximize this function. Our algorithm is analogous to the random walk algorithm described in one of our previous papers [M. Alsolami and M. Mascagni, A random walk algorithm to estimate a lower bound of the star discrepancy, Monte Carlo Methods Appl.28 (2022), 4, 341–348.]. We add a statistical technique to the random walk algorithm by implementing the Metropolis algorithm in random walks on each chosen dimension to accept or reject this movement. We call this Metropolis random walk algorithm. In comparison to all previously known techniques, our new algorithm is superior, especially in high dimensions. Also, it can quickly determine the precise value of the star discrepancy in most of our data sets of various sizes and dimensions, or at least the lower bounds of the star discrepancy. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile Internet, high-definition video and cloud computing, users' bandwidth demands are not only larger and larger but also more and more diverse. To solve this problem, there searchers put forward the concept of elastic optical network (EON). EON adopts the transmission mode of elastic grid, which can allocate spectrum resources flexibly and meet high bandwidth and diversity requirements at the same time. Routing and spectrum allocation (RSA) is an important issue in EON. In this paper, we present a heuristic algorithm named constrained-lower-indexed-block (CLIB) allocation algorithm for the RSA problem. The algorithm is based on the K candidate paths. When there are available spectrum blocks on multiple candidate paths, if the increase of the path length does not exceed a given threshold, the lower index spectrum would be selected for the connection request on a longer path. The aim of the algorithm is to concentrate the occupied frequency slices on one side of the spectrum and leave another side of the spectrum to the later arrived connection requests as much as possible, to reduce the blocking probability of connection requests. Simulation results show that comparing with the first-last-fit and hybrid grouping algorithms, the CLIB algorithm can reduce the blocking probability of connection requests. [ABSTRACT FROM AUTHOR]

Bandwidth assignment is a key issue in optical access networks, so an efficient mechanism is required for dynamic distribution of limited resources in the uplink channel among competing subscriber optical network units (ONUs). This paper presents a novel optimized dynamic bandwidth allocation (ODBA) algorithm for hybrid WDM/TDM passive optical networks. ODBA algorithm operates using information frame length of the subscriber's ONUs and their information arrival rate; both of these factors finalize the assigned transmission capacity to the users. The system performance under differing offered traffic loads is evaluated in terms of network throughput, average delay and channel utilization. Further, the proposed ODBA scheme is validated by performing a comparative analysis with well-known algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we will solve a nonlinear time-fractional equation with nonlocal boundary conditions. First, we construct a set of suitable base according to the correlation theory of reproducing kernel space and the nonlocal boundary conditions. Then we introduce F-derivative and Newton iterative to linearize the nonlinear terms of the equation. The appropriate initial iteration value is constructed by boundary conditions. Finally, we use the ɛ-approximate solution theory to solve the system of linear equations and give the corresponding theoretical derivation. In conclusion, several numerical examples illustrate the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

This paper aims to address the Bernoulli factory problem of 2 ⁢ p 2\mathtt{p} coins by analysing the relationship between the negative binomial distributions and binomial distributions generated on the same chain of coin flips. The proposed algorithm requires fewer conditions on the constructed sequences compared with the existing algorithms. The feasibility of obtaining such 2 ⁢ p 2\mathtt{p} -coin based on 횙-coins will be considered as well. [ABSTRACT FROM AUTHOR]

This paper focuses on determining the cable tension required for a cable-driven endoscopic manipulator to reach a given position under an external load at the end effector. The aim is to restore the initial position of the end effector without an external force. A simulation was performed using MATLAB, where mathematical models and algorithms were used to calculate the required tension. The mathematical model, based on Optimized Motion Estimation Analysis, considers the joint stiffness and the influence of gravity, in addition to the cable tensions and external load. The algorithm minimizes the sum of moments to achieve a stable position. An optical tracking system was used to validate the simulation results against experimental data. The manipulator successfully compensated for an external load of 1.96N and returned to its initial position with adjusted cable tensions. The results showed that the manipulator was deformed by an external load, highlighting the need to adjust the cable tension for position compensation. The discrepancies between the experimental and simulation results were due to factors such as marker size, as well as joint imperfections due to manufacturing and friction effects. Nevertheless, the proposed method proved to be effective in compensating for external loads and restoring the manipulator to the desired position. [ABSTRACT FROM AUTHOR]

In the present paper, we reformulate Mœglin's explicit construction of local A-packets of split odd special orthogonal groups and symplectic groups. By this reformulation together with results of the previous paper with Mínguez, we can compute the A-packets explicitly. Also, we give a non-vanishing criterion of our parametrization, and an algorithm to compute certain derivatives. Finally, we prove a formula for the Aubert duals of irreducible representations of Arthur type. [ABSTRACT FROM AUTHOR]

There is a misunderstanding in Optical Burst Switching (OBS) networks about the congestion status in the network that can cause a reduction in the performance of the networks. OBS networks are bufferless in their nature so when a burst drop happens in the network it can be because of the congestion or contention in the network but TCP cannot distinguish it is due to the congestion or contention. TCP wrongly decreases the congestion window size (cwnd) and causes significant reduction of the network performance. In this paper we are trying to employ a new algorithm by using fuzzy logic and some thresholds to divide the network into several areas then we can solve the problem. This new scheme can help us to distinguish a burst drop is because of the congestion or a burst contention in the network. Extensive simulative studies show that the proposed algorithm outperforms TCP Vegas in terms of throughput and packet delivery count. [ABSTRACT FROM AUTHOR]

In this paper, we present three algorithms for simulation of intervals of stationary vector and scalar sequences with partial distributions of their subsequences of fixed length in the form of a mixture of Gaussian distributions. The first algorithm is based on superposition of two Gaussian vector processes and the second and third ones use the method of conditional distributions and the method of superpositions to simulate the mixtures and to select realizations for approximate construction of conditional realizations. Some properties of these algorithms are presented. [ABSTRACT FROM AUTHOR]

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem. [ABSTRACT FROM AUTHOR]

In the present paper, we introduce the Smart Energy System Control Laboratory (SESCL) as a fully-automated and user-oriented research infrastructure for controlling and operating smart energy systems in the context of a microgrid-under-test setting. SESCL's high level of automation and capacity to fully function in a grid-decoupled way allow for the study and evaluation of yet-to-be-developed tools and algorithms for energy technologies and grid control strategies on the edge of system stability, but in a safe environment. In the context of various European Smart Grid Laboratories, the new concept and specifications of SESCL are outlined in depth. The key advantages of SESCL are highlighted as (i) the provisioning of a fully-automated busbar matrix to provide a very flexible and adjustable microgrid topology; (ii) the capability of load shedding or integration of grid participants, as well as changing the microgrid topology on demand; (iii) and the possibility to control and modify setpoints and operating parameters of grid participants during runtime. Inspired by real-world events in island grids, the islanding of a microgrid is utilized as a use case to illustrate the capabilities of the SESCL research infrastructure. [ABSTRACT FROM AUTHOR]

In many Monte Carlo applications, one can substitute the use of pseudorandom numbers with quasirandom numbers and achieve improved convergence. This is because quasirandom numbers are more uniform that pseudorandom numbers. The most common measure of that uniformity is the star discrepancy. Moreover, the main error bound in quasi-Monte Carlo methods, called the Koksma–Hlawka inequality, has the star discrepancy in the formulation. A difficulty with this bound is that computing the star discrepancy is very costly. The star discrepancy can be computed by evaluating a function called the local discrepancy at a number of points. The supremum of these local discrepancy values is the star discrepancy. If we have a point set in [ 0 , 1 ] s with N members, we need to compute the local discrepancy at N s points. In fact, computing star discrepancy is NP-hard. In this paper, we will consider an approximate algorithm for a lower bound on the star discrepancy based on using a random walk through some of the N s points. This approximation is much less expensive that computing the star discrepancy, but still accurate enough to provide information on convergence. Our numerical results show that the random walk algorithm has the same convergence rate as the Monte Carlo method, which is O (N - 1 2 ). [ABSTRACT FROM AUTHOR]

The implementation of algorithms in Courts promises to bring an increase in efficiency to a legal system which is seen as slow and overburdened, but both the literature and governments are aware that there are potential risks of unwanted consequences to the functioning of the legal system. This paper is a case study of how the Brazilian Superior Tribunal of Justice (STJ) justifies the introduction of algorithms into their case management operations, articulating different notions of efficiency as they do so. Analyzing accounts in multiple sources, it is observed how the STJ self-describes these multiple roles, both as part of the legal system and its role as an organization which is part of the public administration – especially when trying to justify these changes to other government agencies, the legal community and society in general. The article shows that the STJ emphasizes its role in the legal system as an initial justification in official accounts, but largely emphasizes managerial gains internally, avoiding engaging with potential risks by preserving the moment of decision-making as exclusive of the judge. [ABSTRACT FROM AUTHOR]

To solve problems of radiation balance, optical sounding, and tomography, it may be necessary to take into account multiple scattering of radiation in a stochastically inhomogeneous medium. In real radiation models, for this purpose, the numerical-statistical 'majorant cross-section method' (MCM, delta-Woodcock tracking) is used based on the alignment of the optical density field by adding an artificial 'delta scattering' event. However, the computation cost of the corresponding unbiased estimate of the averaged problem solution infinitely increases as the correlation scale (correlation radius L) of standard mosaic models for a random medium density decreases. Previously, we constructed the MCM randomization providing asymptotically (for L → 0) unbiased estimates of the required functionals, in which the value of the physical attenuation coefficient is randomly chosen at the end of the particle free path l under condition l > L. Otherwise the value of the physical attenuation coefficient is the same as at the starting point of the particle (CR algorithm). In a more accurate functional correlative randomized algorithm (FCR algorithm), the coefficient remains the same with a probability determined by the correlation function. These correlative randomized algorithms were implemented for a mixture of homogeneous substance (water) and a Poisson ensemble of 'empty' balls. In the present paper, we construct correlative randomized algorithms for problems related to transfer through a 'thick' layer containing a water and a Poisson ensemble of 'empty' layers. A detailed comparative analysis of the results obtained by exact direct simulation (MCM) and approximate algorithms (CR, FCR) for the problems of gamma radiation transfer through a 'thick' water layer containing a Poisson ensemble of 'empty' layers or balls is presented. [ABSTRACT FROM AUTHOR]

In this paper, we define a generalized resolvent operator involving a 풢 ⁢ (⋅ , ⋅) -co-monotone mapping for solving a generalized variational inclusion problem. We show that the resolvent operator under consideration is single-valued as well as Lipschitz continuous. An iterative algorithm is constructed to obtain the approximate solution of our problem. An example is provided to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, we give an algorithm to determine all local 퐴-packets containing a given irreducible representation of a split special odd orthogonal group or a symplectic group over a 푝-adic field. Especially, we can determine whether a given irreducible representation is of Arthur type or not. [ABSTRACT FROM AUTHOR]

With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain. [ABSTRACT FROM AUTHOR]

In NCAA Division III Wrestling, the question arose how to assign schools to regions in a way that optimizes fairness for individual wrestlers aspiring to the national tournament. The problem fell within cluster analysis but no known clustering algorithms supported its complex and interrelated set of needs. We created several bespoke clustering algorithms based on various heuristics (balanced optimization, weighted spatial clustering, and weighted optimization rectangles) for finding an optimal assignment, and tested each against the generic technique of genetic algorithms. While each of our algorithms had different strengths, the genetic algorithm achieved the highest value on our objective function, including when comparing it to the region assignments that preceded our work. This paper therefore demonstrates a technique that can be used to solve a broad category of clustering problems that arise in athletics, particularly any sport in which athletes compete individually but are assigned to regions as a team. [ABSTRACT FROM AUTHOR]

Randomized scalable vector algorithms for calculation of matrix iterations and solving extremely large linear algebraic equations are developed. Among applications presented in this paper are randomized iterative methods for large linear systems of algebraic equations governed by M-matrices. The crucial idea of the randomized method is that the iterations are performed by sampling random columns only, thus avoiding not only matrix-matrix but also matrix-vector multiplications. The suggested vector randomized methods are highly efficient for solving linear equations of high dimension, the computational cost depends only linearly on the dimension. [ABSTRACT FROM AUTHOR]

This paper presents a novel magnetic twisting device with a coaxial double rotor based on non-contact transmission characteristics of magnetic drive technology. When the twisting device rotates one cycle, the yarn can get triple twists. This means the new device can twist three times more than what the traditional single twist does. The structure of the magnetic twisting device is designed according to the twisting principle. The influence of main structural parameters on the magnetic torque is analyzed. To optimize the maximum transmission torque and the minimum magnet volume, the multi-objective optimization design model for the twisting device is established. Main parameters such as the relative angle of active disc assembly and passive disc assembly, the thickness of magnet, and the average radius of the magnet distribution are optimized by NSGA-II algorithm. Optimization results show that the proposed structural optimization design of a twisting device based on the magnetic drive has excellent performance and is effective for industrial application. [ABSTRACT FROM AUTHOR]

This paper is concerned with the use of the focal underdetermined system solver to recover sparse empirical quadrature rules for parametrized integrals from existing data. This algorithm, originally proposed for image and signal reconstruction, relies on an approximated ℓ p {\ell^{p}} -quasi-norm minimization. Compared to ℓ 1 {\ell^{1}} -norm minimization, the choice of 0 < p < 1 {0

With the overwhelming use of the Internet, the wavelength division multiplexing (WDM) optical networks have become the major mode of communication for wide applications. Hence, the routing and wavelength assignment (RWA) for a given traffic in WDM networks is the chief challenge in optical networks. This paper presents the RWA problem in WDM networks using the hybrid algorithm of flower pollination (FP) and simulated annealing (SA) algorithm (FA+SA) with and without wavelength conversion capability at the nodes of the network. Comparative analysis with techniques presented in literature is also carried out, i. e. differential evolution algorithm (DEA), firefly algorithm (FA), particle swarm optimization with less number of informers (Psolbest), SA and genetic algorithm (GA). The results prove that hybrid algorithm of FP and SA is potentially more efficient than algorithms present in literature for the light path establishment in an optical network. [ABSTRACT FROM AUTHOR]

This paper deals with the Constrained Regulation Problem (CRP) for linear continuous-times fractional-order systems. The aim is to find the existence conditions of linear feedback control law for CRP of fractional-order systems and to provide numerical solving method by means of positively invariant sets. Under two different types of the initial state constraints, the algebraic condition guaranteeing the existence of linear feedback control law for CRP is obtained. Necessary and sufficient conditions for the polyhedral set to be a positive invariant set of linear fractional-order systems are presented, an optimization model and corresponding algorithm for solving linear state feedback control law are proposed based on the positive invariance of polyhedral sets. The proposed model and algorithm transform the fractional-order CRP problem into a linear programming problem which can readily solved from the computational point of view. Numerical examples illustrate the proposed results and show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The output power curve of the photovoltaic array presents a multi-peak characteristic under partial shading conditions, which causes the traditional maximum power point tracking technology to fail to guarantee the maximum power output of the photovoltaic cell. In response to this problem, this paper proposes to apply the improved Mayfly Optimization Algorithm (MA) to the maximum power tracking control. By introducing the gravity factor and limiting the search space of male mayfly, the optimization accuracy of the algorithm is enhanced, the vibration of the algorithm near the MPP is reduced, and the occurrence of premature phenomenon is avoided. Three test functions are selected to verify the algorithm, and under the conditions of rapid irradiance changes and complex shadow occlusion, an MPPT model based on the Boost circuit is established to verify the effectiveness of the algorithm. The simulation results show that the improved MA algorithm can effectively converge to MPP under complex shading conditions, and the output efficiency of photovoltaic arrays can be maintained above 99.96%. The average tracking time for different shading patterns is about 0.15 s. [ABSTRACT FROM AUTHOR]

Renewable energy resources based hybrid microgrid has emerged as a significant component of the power system. Nevertheless, the control and management of power, voltage and frequency under transition between grid-connected mode (GCM) and islanded mode (IM) is a major concern. This paper proposes a self-sustained and flexible novel cooperative control strategy (CCS) for smart hybrid microgrid (SHMG) under GCM and IM mode operations. In islanded operation of SHMG, conventional droop offers ease of control but its execution with seamless transition between operating modes poses poor voltage regulation and frequency deviation. For this purpose, an intelligent cooperative controller is proposed. It consists of a modified P/f-Q/V droop control (MDC) algorithm along with soft computing based VF-controller unit (VFCU). The proposed control scheme is implemented to regulate voltage and frequency. It also enhances the power quality with unbalanced non-linear load and source intermittencies. The absence of a communication link reduces cost and also eliminates the risk of single point breakdown. The SHMG has been simulated in MATLAB and implemented in real-time. The obtained experimental results demonstrate the competency and high robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper combines evolutionary game theory and Agent-based modelling for finding out the optimal location and size of multiple solar PVs in an unbalanced distribution system. The use of Agent-based modelling in solar PV placement is new and hence provides a valuable contribution. NashDE algorithm is implemented and simulated using an agent-based modelling framework MESA. In MA-NashDE, the optimization variables, i.e., the power system buses, are clustered into several zones using both the Loss Sensitivity Index and the Voltage Sensitivity Index. Each zone is under the control of a Nash/zonal player, and the strategy adopted by the zonal player will evolve through Multi-Agent Differential Evolution (MADE) algorithm. This is a novel study focusing on the PV placement problem by Agent-based Evolutionary Game Theory. The proposed methodology was tested using IEEE 34 and IEEE 123 bus radial distribution network using the OpenDSS-Python COM interface. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to introduce a new class of continuous generalized exponential distributions, both for the univariate and bivariate cases. This new class of distributions contains some newly developed distributions as special cases, such as the univariate and also bivariate geometric generalized exponential distribution and the exponential-discrete generalized exponential distribution. Several properties of the proposed univariate and bivariate distributions, and their physical interpretations, are investigated. The univariate distribution has four parameters, whereas the bivariate distribution has five parameters. We propose to use an EM algorithm to estimate the unknown parameters. According to extensive simulation studies, we see that the effectiveness of the proposed algorithm, and the performance is quite satisfactory. A bivariate data set is analyzed and it is observed that the proposed models and the EM algorithm work quite well in practice. [ABSTRACT FROM AUTHOR]

We develop a last-passage Monte Carlo algorithm on a conducting surface at non-constant potentials. In the previous researches, last-passage Monte Carlo algorithms on conducting surfaces with a constant potential have been developed for charge density at a specific point or on a finite region and a hybrid BIE-WOS algorithm for charge density on a conducting surface at non-constant potentials. In the hybrid BIE-WOS algorithm, they used a deterministic method for the contribution from the lower non-constant potential surface. In this paper, we modify the hybrid BIE-WOS algorithm to a last-passage Monte Carlo algorithm on a conducting surface at non-constant potentials, where we can avoid the singularities on the non-constant potential surface very naturally. We demonstrate the last-passage Monte Carlo algorithm for charge densities on a circular disk and the four rectangle plates with a simple voltage distribution, and update the corner singularities on the unit square plate and cube. [ABSTRACT FROM AUTHOR]

We further develop in this study the Random Walk on Spheres (RWS) stochastic algorithm for solving systems of coupled diffusion-recombination equations first suggested in our recent article [K. Sabelfeld, First passage Monte Carlo algorithms for solving coupled systems of diffusion–reaction equations, Appl. Math. Lett. 88 2019, 141–148]. The random walk on spheres process mimics the isotropic diffusion of two types of particles which may recombine to each other. Our motivation comes from the transport problems of free and bound exciton recombination. The algorithm is based on tracking the trajectories of the diffusing particles exactly in accordance with the probabilistic distributions derived from the explicit representation of the relevant Green functions for balls and spheres. Therefore, the method is mesh free both in space and time. In this paper we implement the RWS algorithm for solving the diffusion-recombination problems both in a steady-state and transient settings. Simulations are compared against the exact solutions. We show also how the RWS algorithm can be applied to calculate exciton flux to the boundary which provides the electron beam-induced current, the concentration of the survived excitons, and the cathodoluminescence intensity which are all integral characteristics of the solution to diffusion-recombination problem. [ABSTRACT FROM AUTHOR]

In this paper we develop stochastic simulation methods for solving large systems of linear equations, and focus on two issues: (1) construction of global random walk algorithms (GRW), in particular, for solving systems of elliptic equations on a grid, and (2) development of local stochastic algorithms based on transforms to balanced transition matrix. The GRW method calculates the solution in any desired family of prescribed points of the gird in contrast to the classical stochastic differential equation based Feynman–Kac formula. The use in local random walk methods of balanced transition matrices considerably decreases the variance of the random estimators and hence decreases the computational cost in comparison with the conventional random walk on grids algorithms. [ABSTRACT FROM AUTHOR]

Emerging technologies, such as the Internet of things (IoT), machine learning (ML) and machine-to-machine networks encourage deployment of large-scale wireless sensor networks (WSNs). The major problem in WSN is the limited energy of node batteries. Therefore, the efficient use of node energy for data sensing, processing and communication operations is important to maintain a fully operational network for longest period of time. Literature presents a wide range of lifetime maximization techniques for WSN such as resource allocation algorithm, clustering and routing, sleep–wake scheduling, energy harvesting, MIMO technique, Distributed source coding, genetic algorithm and sink mobility. These techniques effectively lessen the energy consumption and enhance the lifetime of the entire wireless sensor network in various applications. Besides energy consumption, the characterization parameters such as coverage and connectivity, communication and modulation schemes, operational environment, network parameters, node parameters and service parameters also have great impact on WSN performance. This paper presents a comprehensive survey of state-of-the-art research works that improves the performance of WSN by optimizing various network characterization parameters and lifetime maximization techniques. These results highlight the key issues which affects WSN performance and provide a roadmap for WSN designers for effective implementation of novel WSN strategies. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of reconstructing a space-dependent coefficient in a linear Benjamin–Bona–Mahony (BBM)-type equation from a single measurement of its solution at a given time. We analyze the well-posedness of the forward initial-boundary value problem and characterize the inverse problem as a constrained optimization one. Our objective consists on reconstructing the variable coefficient in the BBM equation by minimizing an appropriate regularized Tikhonov-type functional constrained by the BBM equation. The well-posedness of the forward problem is studied and approximated numerically by combining a finite-element strategy for spatial discretization using the Python-FEniCS package, together with a second-order implicit scheme for time stepping. The minimization process of the Tikhonov-regularization adopted is performed by using an iterative L-BFGS-B quasi-Newton algorithm as described for instance by Byrd et al. (1995) and Zhu et al. (1997). Numerical simulations are presented to demonstrate the robustness of the proposed method with noisy data. The local stability and uniqueness of the solution to the constrained optimization problem for a fixed value of the regularization parameter are also proved and illustrated numerically. [ABSTRACT FROM AUTHOR]

Research presented in this paper was initiated by the publication [A. D. Agaltsov and R. G. Novikov, Examples of solving the inverse scattering problem and the equations of the Veselov–Novikov hierarchy from the scattering data of point potentials, Russian Math. Surveys74 (2019), 3, 373–386] and is based on its results. Two sets of the complex monopole scattering coefficients are distinguished among the possible values of these coefficients for nonabsorbing inhomogeneities. These sets differ in phases of the scattering coefficients. In order to analyze the features and possibilities of reconstructing the inhomogeneities of both sets, on the one hand, the inverse problem is solved for each given value of the monopole scattering coefficient using the Novikov functional algorithm. On the other hand, the scatterer is selected in the form of a homogeneous cylinder with the monopole scattering coefficient that coincides with the given one. The results obtained for the monopole inhomogeneity and for the corresponding cylindrical scatterer are compared in the coordinate and spatial-spectral spaces. The physical reasons for the similarities and differences in these results are discussed when the amplitude of the scattering coefficient changes, as well as when passing from one set to another. [ABSTRACT FROM AUTHOR]

The Elo rating system, originally designed for rating chess players, has since become a popular way to estimate competitors' time-varying skills in many sports. Though the self-correcting Elo algorithm is simple and intuitive, it lacks a probabilistic justification which can make it hard to extend. In this paper, we present a simple connection between approximate Bayesian posterior mode estimation and Elo. We provide a novel justification of the approximations made by linking Elo to steady-state Kalman filtering. Our second key contribution is to observe that the derivation suggests a straightforward procedure for extending Elo. We use the procedure to derive versions of Elo incorporating margins of victory, correlated skills across different playing surfaces, and differing skills by tournament level in tennis. Combining all these extensions results in the most complete version of Elo presented for the sport yet. We evaluate the derived models on two seasons of men's professional tennis matches (2018 and 2019). The best-performing model was able to predict matches with higher accuracy than both Elo and Glicko (65.8% compared to 63.7 and 63.5%, respectively) and a higher mean log-likelihood (−0.615 compared to −0.632 and −0.633, respectively), demonstrating the proposed model's ability to improve predictions. [ABSTRACT FROM AUTHOR]

We study the average case complexity of the Uniform Membership Problem for subgroups of free groups, and we show that it is orders of magnitude smaller than the worst case complexity of the best known algorithms. This applies to subgroups given by a fixed number of generators as well as to subgroups given by an exponential number of generators. The main idea behind this result is to exploit a generic property of tuples of words, called the central tree property. An application is given to the average case complexity of the Relative Primitivity Problem, using Shpilrain's recent algorithm to decide primitivity, whose average case complexity is a constant depending only on the rank of the ambient free group. [ABSTRACT FROM AUTHOR]

We show that the slice sampler generates Markov chains whose variables are mean independent and thus uncorrelated when the target density is centrally symmetric. Skewness instead boosts correlations. Popular implementation algorithms such as stepping-out and multivariate-sampling-with-hyperrectangles add statistical inefficiency, the first in case of multimodality, the second in all circumstances. A new sampler which exploits these structural and algorithmic characteristics to reduce the variance of Monte Carlo estimates is experimented in several sampling problems. An insight into the properties of the product slice sampler is also provided. [ABSTRACT FROM AUTHOR]

We propose a method to obtain a new class of copulas using a probability generating function (PGF) of positive-integer valued random variable. Some existing copulas in the literature are sub-families of the proposed copulas. Various dependence measures and invariant property of the tail dependence coefficient under PGF transformation are also discussed. We propose an algorithm for generating random numbers from the PGF copula. The bivariate concavity properties, such as Schur concavity and quasi-concavity, associated with the PGF copula are studied. Two new generalized FGM copulas are introduced using PGFs of geometric and discrete Mittag-Leffler distributions. The proposed two copulas improved the Spearman's rho of FGM copula by (−0.3333, 0.4751) and (−0.3333, 0.9573). Finally, we analyse a real dataset to illustrate the practical application of the proposed copulas. [ABSTRACT FROM AUTHOR]

The research of optical network virtualization technology is gradually deepening among optical network virtualization for network cloud service is one of the main research directions of optical network in the future. The dynamic VN mapping algorithm, the most critical part of the virtual network layer, was not studied. As mentioned earlier, optical networks for cloud-oriented services face the challenge of how to respond to the dynamic changes in VN resource requests during virtualization. [ABSTRACT FROM AUTHOR]

In the development of ethernet passive optical networks (EPONs), quality of service (QoS) support and fairness per optical network unit (ONU) are crucial issues. However, making an elaborate analysis of the existing prediction-based bandwidth allocation algorithm, light load penalty, low prediction precision are pointed out. We present an improved dynamic bandwidth pre-allocation algorithm (R-DBA), which employs recurrent neural network (RNN) to predict the high-priority service traffic in EPON. And we introduce mixed integer linear programming (MILP) for optimally building DBA algorithm. This algorithm achieves the prediction of the high-priority service traffic by RNN during the waiting time and supports bandwidth pre-allocation, thus ensuring the fairness of the bandwidth allocation. [ABSTRACT FROM AUTHOR]

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting. These approaches include Akaikes Information Criterion (AIC) as well as a selection rule relying on the component-wise test error computed via cross-validation. We implemented the AIC and cross-validation routines for Generalized Linear Models and evaluated them regarding their variable selection properties and predictive performance. An extensive simulation study revealed improved selection properties whereas the prediction error could be lowered in a real world application with age-standardized COVID-19 incidence rates. [ABSTRACT FROM AUTHOR]

To investigate the association between birth weight to placental weight (BW/PW) ratio, and echocardiographic left ventricle (LV) morphology at birth, while accounting for other relevant perinatal factors. A prospective cohort study was conducted on neonates at NewYork-Presbyterian Brooklyn Methodist Hospital from 2014 to 2018, categorized by their BW/PW percentile. Missing data were imputed with principal component analysis. Chi-squared and one-way analysis of variance were used to compare BW/PW groups and the best regression model was selected using a genetic and backward stepwise algorithm. We analyzed 827 neonates in three BW/PW groups: small (n=16), normal (n=488), and large (n=323). Placental thickness and smallest diameter were positively correlated with several LV parameters, including inter-ventricular septal thickness during diastole (IVSd) (p=0.002, p<0.001) and systole (IVSs) (p=0.001, p<0.001), LV posterior wall thickness at end of diastole (LVPWd) (p=0.003, p<0.001) and systole (LVPWs) (p<0.001, p<0.001), LV mass (p=0.017, p<0.001), and LV mass/volume (p=0.011, p<0.001). The BW/PW ratio correlated with an increased shortening fraction (estimate=0.29, 95 % CI 0.03–0.55, p=0.027). PW correlated with IVSs (p=0.019), while the longest placental diameter was linked to a decrease in LV internal dimension during diastole (LVIDd) (estimate=−0.07, p=0.039), LV mass (estimate=−0.11, p=0.024), and LV mass/volume (estimate=−0.55, p=0.005). This study found that several placental factors, including the BW/PW ratio, can independently affect LV dimension and morphology, highlighting the importance of fetal growth and placental health in the physiological adaptation of the fetal heart. More research is needed to establish causation and inform newborn prevention strategies. [ABSTRACT FROM AUTHOR]

In recent years, the study of fractional differential equations has become a hot spot. It is more difficult to solve fractional differential equations with nonlocal boundary conditions. In this article, we propose a multiscale orthonormal bases collocation method for linear fractional-order nonlocal boundary value problems. In algorithm construction, the solution is expanded by the multiscale orthonormal bases of a reproducing kernel space. The nonlocal boundary conditions are transformed into operator equations, which are involved in finding the collocation coefficients as constrain conditions. In theory, the convergent order and stability analysis of the proposed method are presented rigorously. Finally, numerical examples show the stability, accuracy and effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, very efficient, intelligent techniques have been used to solve the fourth-order nonlinear ordinary differential equations arising from squeezing unsteady nanofluid flow. The activation functions used to develop the three models are log-sigmoid, radial basis, and tan-sigmoid. The neural network of each scheme is optimized with the interior point method (IPM) to find the weights of the networks. The confrontation of the obtained results with the numerical solutions shows good accuracy of the three schemes. The obtained solutions by utilizing the neural network technique of our variables field (velocity and temperature) are continuous contrary to the discrete form obtained by the numerical scheme. [ABSTRACT FROM AUTHOR]

This paper presents a multiscale symbolic transfer entropy (MSTE) to extract the features of gas–liquid two-phase flow and distinguish flow patterns effectively. The role of the MSTE in typical chaotic time series is investigated. Then the characteristics of the flow patterns about three gas–liquid two-phase flows are analyzed from the perspective of causal analysis. The results show that the MSTE can identify different flow patterns and characterize the dynamic characteristics of flow patterns, providing a new method for identifying two-phase flow accurately. In addition, the MSTE reduces the influence of noise to a certain extent and preserves the dynamic characteristics based on simplifying the original sequence. Compared with traditional algorithm, the MSTE has fast calculation speed and anti-interference characteristics and can express the essential features well. [ABSTRACT FROM AUTHOR]

This work presents a pH control approach, robust to model uncertainties including the scaling-up process. The algorithm is based on a master-slave synchronization, where the real process is taken as the slave, and the master is generated by the real-time simulation of the closed-loop mathematical model of the process. In that sense, the objective is to control the pH value, under minimal process information and subject to perturbations and time-varying references ranging between the basic and acid regions, at laboratory and industrial scales. [ABSTRACT FROM AUTHOR]