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The paper presents a technique for determining the time index of growth of the slope of reverse voltage for double insulated cables, based on the body of the theory of series. It is proved that in the vicinity of the extremum point (maximum) the function of the reverse voltage is approximated by polynomials of the nth power. It is proposed to use second-degree polynomials for practical calculations. The method for calculating relevant indicators is illustrated using real data. Analysis of deviations made it possible to conclude that the calculation method proposed in the paper is far more accurate. In the final part of the study, it was concluded that there is a promising outlook for further development of methodological guidelines for determining complex indices of the remaining life of the cable, including but not limited to the use of various mathematical methods. [ABSTRACT FROM AUTHOR]

The paper presents a technique for determining the time index of growth of the slope of reverse voltage for double insulated cables, based on the body of the theory of series. It is proved that in the vicinity of the extremum point (maximum) the function of the reverse voltage is approximated by polynomials of the nth power. It is proposed to use second-degree polynomials for practical calculations. The method for calculating relevant indicators is illustrated using real data. Analysis of deviations made it possible to conclude that the calculation method proposed in the paper is far more accurate. In the final part of the study, it was concluded that there is a promising outlook for further development of methodological guidelines for determining complex indices of the remaining life of the cable, including but not limited to the use of various mathematical methods. [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]

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]

The article focuses on network virtualization, a technique used to overcome the Internet's perceived ossification. It says that virtualization is used to combine and/or partition into virtual network resources the physical network resources. It says that the virtual networks' actual deployment needs the network operator to map onto physical resources the virtual resources. It mentions that the question on how to map optimally is usually called the Virtual Network Embedding (VNE) problem. It adds that several algorithms proposed for the solution of the problem concentrate on optimizing the application of resources in terms of performance.

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]

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]

The electromagnetic scattering characteristics of foam in Marine environment are studied in this paper. The Elfouhaily sea spectrum model is established, and the dielectric constants of seawater/ocean foam are calculated based on the double Debye dielectric constant model, furthermore, the formula relation between them is given. As an important factor affecting ocean electromagnetic scattering, the thickness of foam is proportional to the wind speed above the sea surface. In this paper, the SMCG method based on the surface current equation is mainly used to calculate the electromagnetic scattering coefficient of the environment, and the correctness of the SMCG algorithm is verified by comparing the calculated results with the actual measured data. Based on the computer simulation, the influence of the existence of ocean foam on the environmental scattering is discussed, and the Marine electromagnetic scattering coefficients under different wind speeds, different scrape angles and different radar bands are calculated. Through the analysis of the calculation results, the influences of wind speed, grazing angle and frequency on the electromagnetic scattering characteristics of the Marine environment containing foam are studied, and the general variation rule is obtained: the intensity of electromagnetic scattering increases with the increase of wind speed, the decrease of incident Angle and the increase of frequency. The results of this paper can be used to guide ocean navigation and fishery, and have reference significance in the military field. [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]

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]

In this paper, link fault tolerant algorithms (LFTAs) are employed for optimization of blocking performance in optical burst switching (OBS) networks for dynamic establishment of wavelength-routed lightpaths. This paper presents two algorithms: link fault tolerance algorithm for least congestion (LFTALC) and link fault tolerance algorithm for optimized blocking (LFTAOB). In former algorithm, the path with least congestion is selected and in the latter algorithm the path with least blocking probability is selected and then both the algorithms check for the availability of fault on the links present in the selected path. The findings indicate that methods of developing link based fault tolerant algorithms keep good performance and fairness among all the connections. The key advantage of these proposed algorithms as compared to routing and wavelength assignment algorithm is that these are very simple in nature and require less service disruption time. [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]

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]

Alpha1-antitrypsin deficiency (AATD) is an inherited condition that predisposes individuals to an increased risk of developing lung and liver disease. Even though AATD is one of the most widespread inherited diseases in Caucasian populations, only a minority of affected individuals has been detected. Whereas methods have been validated for AATD testing, there is no universally-established algorithm for the detection and diagnosis of the disorder. In order to compare different methods for diagnosing AATD, we carried out a systematic review of the literature on AATD diagnostic algorithms. Complete biochemical and molecular analyses of 5,352 samples processed in our laboratory were retrospectively studied using each of the selected algorithms. When applying the diagnostic algorithms to the same samples, the frequency of False Negatives varied from 1.94 to 12.9%, the frequency of True Negatives was 62.91% for each algorithm and the frequency of True Positives ranged from 24.19 to 35.15%. We, therefore, highlighted some differences among Negative Predictive Values, ranging from 0.83 to 0.97. Accordingly, the sensitivity of each algorithm ranged between 0.61 and 0.95. We also postulated 1.108 g/L as optimal AAT cut-off value, in absence of inflammatory status, which points to the possible presence of genetic AATD. The choice of the diagnostic algorithm has a significant impact on the correct diagnosis of AATD, which is essential for appropriate treatment and medical care. The fairly large number of possible false negative diagnoses revealed by the present paper should also warn clinicians of negative results in patients with clinically-suspected AATD. [ABSTRACT FROM AUTHOR]

Based on full domain partition, three parallel iterative finite-element algorithms are proposed and analyzed for the Navier–Stokes equations with nonlinear slip boundary conditions. Since the nonlinear slip boundary conditions include the subdifferential property, the variational formulation of these equations is variational inequalities of the second kind. In these parallel algorithms, each subproblem is defined on a global composite mesh that is fine with size h on its subdomain and coarse with size H (H ≫ h) far away from the subdomain, and then we can solve it in parallel with other subproblems by using an existing sequential solver without extensive recoding. All of the subproblems are nonlinear and are independently solved by three kinds of iterative methods. Compared with the corresponding serial iterative finite-element algorithms, the parallel algorithms proposed in this paper can yield an approximate solution with a comparable accuracy and a substantial decrease in computational time. Contributions of this paper are as follows: (1) new parallel algorithms based on full domain partition are proposed for the Navier–Stokes equations with nonlinear slip boundary conditions; (2) nonlinear iterative methods are studied in the parallel algorithms; (3) new theoretical results about the stability, convergence and error estimates of the developed algorithms are obtained; (4) some numerical results are given to illustrate the promise of the developed algorithms. [ABSTRACT FROM AUTHOR]

Optical burst switching (OBS) is a new emerging network that is developing rapidly. By increasing the amount of traffic on the Internet the necessity for having networks like OBS ones that can carry heavy traffic is needed. These networks have an important problem due to being bufferless. The congestion control of the network is a little confusing for some protocols like transport control protocol (TCP) Vegas as they are appropriate for conventional wired networks not optical ones. When a drop happens in OBS networks it can be because of two reasons: 1. congestion and 2. Contention that has been possible due to the bufferless nature of the network. When a contention happens and a packet drops, TCP thinks that the network is experiencing heavy load however it may not, and it decreases the sending rate, as a result, it leads to a reduction in the performance of the network. In this paper, we are trying to improve the performance of the network by employing a new algorithm that called Fuzzy-RTT Performance Improver. This algorithm is based on fuzzy logic and round-trip time measurements. Simulative results show that this algorithm outperforms TCP Vegas in terms of throughput and packet delivery count. [ABSTRACT FROM AUTHOR]

Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates for different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting. [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]

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]

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]

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors. [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]

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]

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This paper deals with the new algorithm of slope limiting in the Runge-Kutta discontinuous Galerkin (RKDG) method. The slope limiting is applied at each intermediate step of the Runge-Kutta process to guarantee the monotonicity of the resulting RKDG scheme. The standard formulation of the RKDG method assumes a manual prescription of the special parameter used in the limiting procedure. Such definition of the limiter makes the method problem-dependent, which is disadvantageous for practical computations. A new problem-independent way of estimating the limiting parameter is proposed and its performance in the second- and third-order RKDG methods is studied in this paper. [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]

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]

Traditional precise point positioning (PPP) based on undifferenced ionosphere-free linear combination of observations has many advantages such as high accuracy and easy operation. PPP usually uses the Kalman Filter (KF) to estimate state vector. However, the positioning performance depends on the accuracy of the kinematic model and initial value. The inaccurate kinematic model or initial value will lead to filter performance degradation or even divergence. To overcome this problem, this paper proposes a PPP method with an additional baseline vector constraint, which uses the direction information and length information of the baseline to correct the estimated position of the receiver. By reducing the error covariance matrix of the float solution, the algorithm improves the accuracy of the float solution. By using the collected real GPS static and kinematic data, the performance of the traditional model and the proposed model in this paper are compared. It is shown that the additional baseline vector constraint improves the PPP solution effectively in comparison with that of traditional PPP model. Additionally, the contribution of the additional constraint is up to the accuracy of the prior information. [ABSTRACT FROM AUTHOR]

This paper explores Merge and proposes a new form of sideward movement (double sideward movement) carried out by a new application of External Merge. Double sideward movement occurs in the following way: given a syntactic object S containing XP and YP, Merge applies to XP and YP, and creates {XP, YP} outside S, thus causing XP and YP to undergo sideward movement at the same time. It is argued that multiple clefts (cleft sentences with multiple phrases in the focus position) in Japanese are derived by double sideward movement of the multiple focus phrases and that this derivation is responsible for certain surprising properties of Japanese multiple clefts, some well known and others newly discovered, including the lack of island effects and the presence and absence of clausemate effects. Other consequences are discussed for the nature of operator movement and scrambling as well as for restrictions on the application of Merge. [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]

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]

The operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates. [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]

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]

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]

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]

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]

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]

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]

We continue the investigations in this paper which were considered in its first part. In particular, the problem of optimal filtration of one components of solution of nonlinear evolutionary differential equation in a Hilbert space H perturbing by Gaussian random process under observation of the second components of the same equation is considered. Using the results of the first part of this paper, here the algorithm for the calculation of an optimal estimate in the filtration problem is obtained. [ABSTRACT FROM AUTHOR]

Also in the digital age, markets work properly as long as consumers are well informed. What is peculiar of the digital age is that consumers have become very fragile, also because firms can extensively manipulate the information that they produce and distribute to markets. Antitrust authorities may find their way to prosecute business manipulative conduct, as some rulings suggest. However, the enforcement of antitrust law is subject to precise circumstances and requires cumbersome proceedings, especially when dominant firms are involved. Therefore, a simpler and more widespread intervention is needed. Although over the years traditional disclosure regulation has showed its limits, today algorithmic analysis gives room to more effective forms of disclosure regulation. Therefore, the paper maintains that both regulators and antitrust authorities can use these new forms of disclosure regulation to perform better their functions. Thanks to algorithmic analysis, (a) regulators can provide consumers with targeted co-regulated disclosures; (b) while competition authorities, using their advocacy powers, can provide trustworthy rankings and reviews about firms' ability to comply with antitrust and consumer protection laws. [ABSTRACT FROM AUTHOR]

In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations. [ABSTRACT FROM AUTHOR]

This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature. [ABSTRACT FROM AUTHOR]