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Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor's bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles' number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented. [ABSTRACT FROM AUTHOR]

In this paper, we study the complexity of the edge monitoring problem. A vertex v monitors an edge e if both endpoints together with v form a triangle in the graph. Given a graph G = (V , E) and a weight function on edges c where c (e) is the number of monitors that needs the edge e , the problem is to seek a minimum subset of monitors S such that every edge e in the graph is monitored by at least c (e) vertices in S. Therefore, we study the edge monitoring problem on several graph classes such as complete graphs, block graphs, cographs, split graphs, interval graphs and planar graphs. We also generalize the problem by adding weights on vertices. [ABSTRACT FROM AUTHOR]

Consider an information diffusion process on a graph G that starts with k > 0 burnt vertices, and at each subsequent step, burns the neighbors of the currently burnt vertices, as well as k other unburnt vertices. The k-burning number of G is the minimum number of steps b k (G) such that all the vertices can be burned within b k (G) steps. Note that the last step may have smaller than k unburnt vertices available, where all of them are burned. The 1-burning number coincides with the well-known burning number problem, which was proposed to model the spread of social contagion. The generalization to k -burning number allows us to examine different worst-case contagion scenarios by varying the spread factor k. In this paper we prove that computing k -burning number is APX-hard, for any fixed constant k. We then give an O ((n + m) log ⁡ n) -time 3-approximation algorithm for computing k -burning number, for any k ≥ 1 , where n and m are the number of vertices and edges, respectively. Finally, we show that even if the burning sources are given as an input, computing a burning sequence itself is an NP-hard problem. [ABSTRACT FROM AUTHOR]

k-median with service installation costs is a frequently encountered problem in applications involving multiple service requirements, which generalizes the standard k -median problem in that each client is associated with a specific service and can only be connected to the facilities where the service is installed. In this paper, we give a (13.349 + ϵ) -approximation algorithm for the k -median with service installation costs problem, improving upon the previous best approximation ratio of 18. We propose a new deterministic rounding approach to deal with the challenges caused by the service requirements, which is the crucial step in getting the improved ratio. • We give a (13.349 + ϵ) -approximation algorithm for the k -median with service installation costs problem. • We obtain a well-behaved fractional solution to the problem based on the framework of Lagrangian relaxation. • We give a new deterministic rounding approach to deal with the challenges caused by the service requirements. [ABSTRACT FROM AUTHOR]

Online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, “bad actors” are increasingly exploiting these platforms, which is a key challenge for their administrators, businesses and society in general. The spread of fake news is a classical example of the abuse of social networks by these bad actors. While some have advocated for stricter policies to control the spread of misinformation in social networks, this often happens in detriment of their democratic and organic structure. In this paper, we aim to limit the influence of a target group in a social network via the removal of a few users/links. We formulate the influence limitation problem in a data-driven fashion, by taking into account past propagation traces. More specifically, our algorithms find critical edges to be removed in order to decrease the influence of a target group based on past data. The idea is to control the diffusion processes while minimizing the amount of disturbance in the network structure. Moreover, we consider two types of constraints over edge removals, a budget constraint and also a, more general, set of matroid constraints. These problems lead to interesting challenges in terms of algorithm design. For instance, we are able to show that influence limitation is APX-hard and propose deterministic and probabilistic approximation algorithms for the budgeted and the matroid version of the problem, respectively. Experiments show that the proposed approaches outperform several baselines. [ABSTRACT FROM AUTHOR]

In connection with applications, the solution of a system of logical equations plays an important role in computational mathematics and in many other areas. As a result, many new directions and algorithms for solving systems of logical equations are being developed. One of these directions is transformation into the real continuous domain. The real continuous domain is a richer domain to work with because it features many algorithms, which are well designed. In this study, firstly, we transformed any system of logical equations in the unit n -dimensional cube K n into a system of polylinear–polynomial equations in a mathematically constructive way. Secondly, we proved that if we slightly modify the system of logical equations, namely, add no more than one special equation to the system, then the resulting system of logical equations and the corresponding system of polylinear–polynomial equations in K n + 1 is equivalent. The paper proposes an algorithm and proves its correctness. Based on these results, further research plans are developed to adapt the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we introduce and formally study the problem of k -clustering with faulty centers. Specifically, we study the faulty versions of k -center, k -median, and k -means clustering, where centers have some probability of not existing, as opposed to prior work where clients had some probability of not existing. For all three problems we provide fixed parameter tractable algorithms, in the parameters k , d , and ε , that (1 + ε) -approximate the minimum expected cost solutions for points in d dimensional Euclidean space. For Faulty k -center we additionally provide a 5-approximation for general metrics. Significantly, all of our algorithms have only a linear dependence on n. [ABSTRACT FROM AUTHOR]

The Maximum Vertex Coverage problem (abbreviated as MVC) is to maximum the number of edges covered by a set of vertices of size exactly K on a graph. This problem is the dual of the vertex cover problem and has attracted a lot of interests in the literature of approximation algorithm. So far, the best approximation factor for the MVC problem is 3/4, which is obtained using an LP-rounding method. The main results of this paper are new approximation algorithms for MVC on cubic graphs and 3-bounded graphs (the vertex degrees are at most 3). The approximation factor on cubic graphs is 79/90 (≈0.878), which is tight by analyzing the existence of a feasible solution for a linear programming system. This algorithm can also be extended to 3-bounded graphs and guarantees an approximation factor of 19/24 (≈0.792). [ABSTRACT FROM AUTHOR]

Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user's private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity. [ABSTRACT FROM AUTHOR]

Accuracy of solar cell model parameters is now a requirement for the manufacturing of appropriate photovoltaic models. Consequently, the methodologies of the parameters had already attracted immense interest among researchers over the years. This paper deals about the approximation of the characteristics curves of a solar cell using the Bézier curve method. This technique is selected to overcome the problems of the non-linearity, complexity, multivariate and multimodal of the characteristic's curves. The Bézier curves method is a parametric curve used throughout computer graphics and related disciplines. It is a robust methodology for constructing free-form curves, surfaces, shape design, and geometric representation of various products. Six different cases are carried out to test the validity of the proposed method, they are: (i) organic flexible dual junction amorphous silicon solar cell, (ii) silicon solar cell, (iii) Schutten Solar STP6-120/36 polycrystalline photovoltaic module, (iv) Schutten Solar STM6-40/36 monocrystalline photovoltaic module, (v) Photowatt-PWP 201 polycrystalline photovoltaic module and (vi) 230-W multi-silicon photovoltaic module under partial shading conditions. The technique is implemented and adapted by the mean of the De Casteljo algorithm to optimize the control points and minimize the distance between the experimental and calculated points. It is based on subdividing the curve into various segments and controlled by control points using the De Casteljo algorithm to minimize the distance between the experimental and theoretical points. The performance of the approximated characteristics curves is compared with experimental data as well as recent algorithms and techniques from the literature. For more validity, statistical error metrics from literature are computed and compared to ensure the accuracy of the approximated results and the performance of the proposed method. For instance, the comparison is carried out by resorting to the Individual Absolute Error (IAE) and the Relative Error (RE). Further, the Bias metrics and accuracy are calculated using the Autocorrelation Function (ACF), Tracking Signal (TS), and Normalized Forecast Metric (NFM). The proposed method provides a low error for the six case studies and better than that obtained by the other algorithms. [Display omitted] • Approximation of I-V and P-V characteristics curves of a solar cell using Bézier curve method. • Bézier curve method is powerful technique to overcome the problems of the non-linearity and complexibility. • The technique is implemented and adapted by the mean of the De Casteljo algorithm. • Six different cases are carried out to test the validity of the proposed Bézier curve method. • The Bézier curve method provides a low error in six case studies. [ABSTRACT FROM AUTHOR]

• We study the approximation of the Double Traveling Salesman Problem with Multiple Stacks. • We study the complexity of two important subproblems. • We provide approximation results for both standard and differential approximation. • Approximation results are derived from reductions to the TSP in the general case. • Algorithms based on optimal matchings and completions are analyzed for the case with two stacks. The Double Traveling Salesman Problem with Multiple Stacks, DTSPMS , deals with the collect and delivery of n commodities in two distinct cities, where the pickup and the delivery tours are related by LIFO constraints. During the pickup tour, commodities are loaded into a container of k rows, or stacks, with capacity c. This paper focuses on computational aspects of the DTSPMS , which is NP-hard. We first review the complexity of two critical subproblems: deciding whether a given pair of pickup and delivery tours is feasible and, given a loading plan, finding an optimal pair of pickup and delivery tours, are both polynomial under some conditions on k and c. We then prove a (3 k) / 2 standard approximation for the Min Metric k DTSPMS , where k is a universal constant, and other approximation results for various versions of the problem. We finally present a matching-based heuristic for the 2 DTSPMS , which is a special case with k = 2 rows, when the distances are symmetric. This yields a 1 / 2 − o (1) , 3 / 4 − o (1) and 3 / 2 + o (1) standard approximation for respectively Max 2 DTSPMS , its restriction Max 2 DTSPMS (1 , 2) with distances 1 and 2, and Min 2 DTSPMS (1 , 2) , and a 1 / 2 − o (1) differential approximation for Min 2 DTSPMS and Max 2 DTSPMS. [ABSTRACT FROM AUTHOR]

In this paper, we suggest basing the development of classification methods on traditional techniques but approximating them in whole or in part with artificial neural networks (ANNs). Compared to a direct ANN approach, the underlying traditional method is often easier to analyse. Classification failures can be better understood and corrected for, while at the same time faster execution can be obtained due to the parallel ANN structure. Furthermore, such a two‐step design philosophy partly eliminates the 'guesswork' associated with the design of ANNs. The expected gain is thus that the benefits from the traditional field, and the ANN field can be obtained by combining the best features from both fields. We illustrate our approach by working through an explicit example, namely, a Nearest Neighbour classifier applied to a subset of the MNIST database of handwritten digits. Two different approaches are discussed for how to translate the traditional method into an ANN. The first approach is based on a constructive implementation which directly reflects the original algorithm. The second approach uses ANNs to approximate the whole, or part of, the original method. An important part of the approach is to show how improvements can be introduced. In line with the presented philosophy, this is done by extending the traditional method in several ways followed by ANN approximation of the modified algorithms. The extensions are based on a windowed version of the nearest neighbour algorithm. We show that the improvements carry over to the ANN implementations. We further investigate the stability of the solutions by modifying the training set. It is shown that the errors do not change significantly. This also holds true for the ANN approximations providing confidence that the two‐step strategy is robust. [ABSTRACT FROM AUTHOR]

This paper features the study of global optimization problems and numerical methods of their solution. Such problems are computationally expensive since the objective function can be multi-extremal, nondifferentiable, and, as a rule, given in the form of a "black box". This study used a deterministic algorithm for finding the global extremum. This algorithm is based neither on the concept of multistart, nor nature-inspired algorithms. The article provides computational rules of the one-dimensional algorithm and the nested optimization scheme which could be applied for solving multidimensional problems. Please note that the solution complexity of global optimization problems essentially depends on the presence of multiple local extrema. In this paper, we apply machine learning methods to identify regions of attraction of local minima. The use of local optimization algorithms in the selected regions can significantly accelerate the convergence of global search as it could reduce the number of search trials in the vicinity of local minima. The results of computational experiments carried out on several hundred global optimization problems of different dimensionalities presented in the paper confirm the effect of accelerated convergence (in terms of the number of search trials required to solve a problem with a given accuracy). [ABSTRACT FROM AUTHOR]

Reliability–redundancy allocation problem (RRAP), a challenging reliability optimization problem, aims to optimize the redundancy level and the component reliability simultaneously for each stage (i.e. subsystem) of the system. The RRAP was mainly studied under binary-state setting on different redundancy strategies, methodologies, and multi-objective formulations. However, the methods are all meta-heuristic algorithms, and no studies focused on RRAP in multi-state flow network (MFN) systems. In this paper, we study the RRAP in MFN considering the minimization of cost or the maximization of reliability under resource constraints. The MFN RRAP is a mixed-integer non-linear programming problem, which is NP-hard. We propose a minimal cut-based approximation scheme to transform it into an integer programming problem. The feasibility guarantee is analyzed to ensure that this approximation scheme can generate a feasible solution with a high probability. The posterior check is conducted to reduce the conservative theoretical sample size empirically. The numerical experiments on MFNs illustrate the tradeoff performance between the accuracy of solutions and the computational complexity, and the outperformance of our proposed method compared to a meta-heuristic algorithm. • Reliability–redundancy allocation problem (RRAP) is first considered in multi-state flow network (MFN). • An approximate method is proposed for MFN RRAP via integer linear programming problem. • A simulation method is proposed to evaluate the reliability of MFN by binary-state minimal cuts. • A prior estimate and a posterior check are conducted to ensure a feasible solution with a high probability. [ABSTRACT FROM AUTHOR]

The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original function and the estimation from the piecewise linear function. Here, the piecewise linearization of 2D functions is studied. Each of the employed swarm intelligence algorithms is enhanced by a newly proposed automatic detection of the number of piecewise linear parts that determine the discretization points to calculate the distance between the original and piecewise linear function. The original algorithms and their enhanced variants are compared on several examples of piecewise linearization problems. The results show that the enhanced approach performs sufficiently better when it creates a very promising approximation of functions. Moreover, the degree of precision is slightly decreased by the focus on the speed of the optimization process. [ABSTRACT FROM AUTHOR]

Zadeh's extension principle is one of the elementary tools in fuzzy set theory, and among other things, it provides a natural extension of a real-valued continuous self-map to a self-map having fuzzy sets as its arguments. The purpose of this paper is to introduce a new algorithm that can be used for simulations of fuzzy dynamical systems induced by interval maps. The core of the proposed algorithm is based on calculations including piecewise linear maps, and consequently, an implementation of an optimization algorithm (in our case, particle swarm optimization) was prepared to obtain necessary piecewise linear approximations. For all parts of this algorithm, we provide detailed testing and numerous examples. [ABSTRACT FROM AUTHOR]