Popular Problem Research Articles

Virtual Screening, Oriented-synthesis and Evaluation of Lipase Inhibitory Activity of Benzyl Amino Chalcone Derivatives

Nowadays, obesity has been becoming one of the most popular problems to the global health. Molecular design with the aid of computing method is an efficient and cost-saving solution in the initial research of new potential drugs for the treatment of obesity. This study focused on benzyl amino chalcone derivatives as they have a benzyl group that can mimic the hydrophobic effect of the long chain carbon of Orlistat, a drug used to treat obesity. Initially, 102 molecular structures were prepared and docked into the protein by using AutoDock Vina version 1.5.6. Fourteen structures having good docking scores were selected to synthesize using a Claisen-Schmidt reaction. Afterward, these synthesized chalcones were tested biological activity against pancreatic lipase by spectrophotometric determination at a wavelength of 405 nm, using p-nitro phenyl palmitate as the substrate. The co-crystallized ligand of pancreatic lipase enzyme was redocked into the enzyme and the RMSD was 1.4976 A which showed the ligand and the protein preparation could regenerate the practical experiment. As the docking results, the binding affinities of top ten compounds varied from -8.6 and -10.2 kcal/mol. Biological testing resulted in 4 derivatives with IC50 >120 μM, 8 derivatives with 60 μM < IC50 < 120 μM and 2 derivatives with IC50 < 60 jliM. In addition, the docking results also confirmed the key role of amino acid Seri 52 in interacting with the ligands. The benzyl amino chalcone derivatives are required for further investigation to become a lead compound for anti-obesity drug discovery.

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

This paper focuses on the flowshop scheduling problem with sequence dependent setup times (FSSP-SDST), which has been an investigated object for decades as one of the most popular scheduling problems in manufacturing systems. A novel hybrid local search algorithm called HLS is presented to solve the flowshop scheduling problem with sequence dependent setup times with the criterion of minimizing the makespan. Firstly, the population is initialized by the Nawaz-Enscore-Hoam based problem-specific method (NEHBPS) to generate high quality individuals of the current population. Then, a global search embedded with a light perturbation is designed to produce a new population. After that, to improve the quality of individuals in the current population, a single insertion-based local search is applied. Meanwhile, a further local search strategy based on the insertion-based local search is used to find better solutions for the individuals which are non-improved. Finally, the heavy perturbation is used to explore potential solutions in the neighbor region. To validate the performance of HLS, we compare our proposed algorithm with other competitive algorithms on Taillard benchmark problems. From the experimental results, it can be concluded that the proposed algorithm outperforms the benchmark algorithms.

Optimized Charging Scheduling with Single Mobile Charger for Wireless Rechargeable Sensor Networks

Due to the rapid development of wireless charging technology, the recharging issue in wireless rechargeable sensor network (WRSN) has been a popular research problem in the past few years. The weakness of previous work is that charging route planning is not reasonable. In this work, a dynamic optimal scheduling scheme aiming to maximize the vacation time ratio of a single mobile changer for WRSN is proposed. In the proposed scheme, the wireless sensor network is divided into several sub-networks according to the initial topology of deployed sensor networks. After comprehensive analysis of energy states, working state and constraints for different sensor nodes in WRSN, we transform the optimized charging path problem of the whole network into the local optimization problem of the sub networks. The optimized charging path with respect to dynamic network topology in each sub-network is obtained by solving an optimization problem, and the lifetime of the deployed wireless sensor network can be prolonged. Simulation results show that the proposed scheme has good and reliable performance for a small wireless rechargeable sensor network.

Сокращение продолжительности проекта в условиях неопределенности и ограниченных ресурсов

Shortening project expected makespan in the case of resource constraints is the most popular problem in project scheduling. But in practice activity durations is not determined. So the problem is to find a policy which could define which activity should be started at decision points to minimize expected project makespan. This problem usually called SRCPSP. In this paper we define key research tasks of such a problem and propose extended activity-based policy XABP. Computational results show us the high efficiency of this policy.

Generation of discrete random variables in scalable frameworks

In this paper, we face the problem of simulating discrete random variables with general and varying distributions in a scalable framework, where fully parallelizable operations should be preferred. The new paradigm is inspired by the context of discrete choice models. Compared to classical algorithms, we add parallelized randomness, and we leave the final simulation of the random variable to a single associative operation. We characterize the set of algorithms that work in this way, and those algorithms that may have an additive or multiplicative local noise. As a consequence, we could define a natural way to solve some popular simulation problems.

Solving the deterministic and stochastic uncapacitated facility location problem: from a heuristic to a simheuristic

The uncapacitated facility location problem (UFLP) is a popular combinatorial optimization problem with practical applications in different areas, from logistics to telecommunication networks. While most of the existing work in the literature focuses on minimizing total cost for the deterministic version of the problem, some degree of uncertainty (e.g., in the customers’ demands or in the service costs) should be expected in real-life applications. Accordingly, this paper proposes a simheuristic algorithm for solving the stochastic UFLP (SUFLP), where optimization goals other than the minimum expected cost can be considered. The development of this simheuristic is structured in three stages: (i) first, an extremely fast savings-based heuristic is introduced; (ii) next, the heuristic is integrated into a metaheuristic framework, and the resulting algorithm is tested against the optimal values for the UFLP; and (iii) finally, the algorithm is extended by integrating it with simulation techniques, and the resulting simheuristic is employed to solve the SUFLP. Some numerical experiments contribute to illustrate the potential uses of each of these solving methods, depending on the version of the problem (deterministic or stochastic) as well as on whether or not a real-time solution is required.

A triangular shell element for geometrically nonlinear analysis

In this paper, a geometrically nonlinear formulation for a six-node triangular shell element is proposed. Total Lagrangian formulation is utilized to consider large displacements and rotations in the shell analysis. To avoid shear and membrane locking, a proper interpolation function for the strain field is implemented. Both algorithm and flowchart of the nonlinear solution, which are utilized in the author’s computer program, are presented. To validate the suggested formulation, several popular benchmark problems are solved. Moreover, the obtained results are compared with those of the other well-known elements. Findings demonstrate the ability of the suggested shell element.

An efficient DCA based algorithm for power control in large scale wireless networks

In recent years, power control and resource allocation techniques for cellular communication systems are very active research areas. Power control is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. One of the most popular power control problems is based on maximizing the weighted sum of data rates under the peak power constraints for all users. It is a difficult nonconvex optimization problem for which standard approach Geometric Programming is not applicable in large scale setting. In this paper, we propose an efficient method based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), an innovative approach in nonconvex programming framework for solving this problem. The purpose is to develop fast and scalable algorithms able to handle large scale systems. The two main challenges in DC programming and DCA that are the effect of DC decomposition and the efficiency of solution methods to convex subproblems are carefully studied. The computational results on several datasets show the robustness as well as the efficiency of the proposed method in terms of both quality and rapidity, and their superiority compared with the standard approach Geometric Programming.

P$^2$S: A Primary and Passer-By Scheduling Algorithm for On-Demand Charging Architecture in Wireless Rechargeable Sensor Networks

As the interdiscipline of wireless communication and control engineering, the cooperative charging issue in wireless rechargeable sensor networks (WRSNs) is a popular research problem. With the help of wireless power transfer technology, electrical energy can be transferred from wireless charging vehicles to sensors, providing a new paradigm to prolong the network lifetime. However, existing techniques on cooperative charging usually take the periodical and deterministic approach but neglect the influences of the nondeterministic factors such as topological changes and node failures, making them unsuitable for largescale WRSNs. In this paper, we develop a primary and passer-by scheduling (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S) algorithm for on-demand charging architecture for large-scale WRSNs. In P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S, task interdependence is utilized to enhance charging efficiency. We exploit a local searching algorithm, in which nearby nodes on the way to primary nodes, which are the targets of wireless charging vehicle's current movement, will be charged as passer-by nodes. Such a strategy not only makes full use of the available remaining time of a charging deadline but solves the complex scheduling problem with spatial and temporal task interdependence as well. Analysis and simulations are conducted to show the superiority of our scheme, revealing that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> S has a higher survival rate and throughput, as well as other performance metrics.

Popular edges and dominant matchings

Given a bipartite graph $$G = (A \cup B,E)$$ with strict preference lists and given an edge $$e^* \in E$$ , we ask if there exists a popular matching in G that contains $$e^*$$ . We call this the popular edge problem. A matching M is popular if there is no matching $$M'$$ such that the vertices that prefer $$M'$$ to M outnumber those that prefer M to $$M'$$ . It is known that every stable matching is popular; however G may have no stable matching with the edge $$e^*$$ . In this paper we identify another natural subclass of popular matchings called “dominant matchings” and show that if there is a popular matching that contains the edge $$e^*$$ , then there is either a stable matching that contains $$e^*$$ or a dominant matching that contains $$e^*$$ . This allows us to design a linear time algorithm for identifying the set of popular edges. When preference lists are complete, we show an $$O(n^3)$$ algorithm to find a popular matching containing a given set of edges or report that none exists, where $$n = |A| + |B|$$ .

Finding strongly popular b-matchings in bipartite graphs

The computational complexity of the bipartite popular matching problem depends on how indifference appears in the preference lists. If one side has strict preferences while nodes on the other side are all indifferent (but prefer to be matched), then a popular matching can be found in polynomial time [Cseh, Huang, Kavitha, 2015]. However, as the same paper points out, the problem becomes NP-complete if nodes with strict preferences are allowed on both sides and indifferent nodes are allowed on one side. We show that the problem of finding a strongly popular matching is polynomial-time solvable even in the latter case. Our result also extends to the many-to-many version, i.e. the strongly popular b-matching problem.

A certificateless signature scheme and a certificateless public auditing scheme with authority trust level 3+

Many certificateless cryptosystems have been proposed for cloud security applications. These applications have to face the inherent issues of dealing with low authority trust levels, especially when the cloud server takes charge of doing the role of the key generation center (KGC), that is the authority to trust. This paper focuses on popular authority trust problems in certificateless signatures and proposes a public cloud auditing scheme with high trust level. In current cloud security applications based on certificateless cryptography, the level of trust can at most detect and prove that the authority is guilty but can never punish it for its malicious behavior; precisely, in those settings where malicious servers have to be punished, an external arbitrator becomes necessary. We develop a novel notion of enhanced authority trust level, that we call 3+, where even if KGCs can impersonate any entity, still there is no way to avoid an immediate penalty for its malicious behavior. First, we construct a certificateless signature scheme with authority trust level 3+, then we prove its security in the random oracle model, illustrating some benefits in applications made for clouds. In particular, we propose a certificateless homomorphic authenticable signature scheme and a cloud public auditing scheme. Our proposed trust level 3+ sensibly boosts the trustworthiness and acceptability of such cloud computation environments by its ordinary customers.

Extracting Knowledge from the Geometric Shape of Social Network Data Using Topological Data Analysis

Topological data analysis is a noble approach to extract meaningful information from high-dimensional data and is robust to noise. It is based on topology, which aims to study the geometric shape of data. In order to apply topological data analysis, an algorithm called mapper is adopted. The output from mapper is a simplicial complex that represents a set of connected clusters of data points. In this paper, we explore the feasibility of topological data analysis for mining social network data by addressing the problem of image popularity. We randomly crawl images from Instagram and analyze the effects of social context and image content on an image’s popularity using mapper. Mapper clusters the images using each feature, and the ratio of popularity in each cluster is computed to determine the clusters with a high or low possibility of popularity. Then, the popularity of images are predicted to evaluate the accuracy of topological data analysis. This approach is further compared with traditional clustering algorithms, including k-means and hierarchical clustering, in terms of accuracy, and the results show that topological data analysis outperforms the others. Moreover, topological data analysis provides meaningful information based on the connectivity between the clusters.

Numerical modeling of structural frames with infills subjected to thermal exposure

PurposeReinforced concrete structural frames with masonry infills (infill-frames) are commonly used for construction worldwide. While the behavior of such frames has been studied extensively in the context of earthquake loading, studies related to their fire performance are limited. Therefore, this study aims to characterize the behavior of infill-frames under fire exposure by presenting a state-of-the-art literature review of the same.Design/methodology/approachBoth experimental and computational studies have been included with a special emphasis on numerical modeling (simplified as well as advanced). The cold behavior of the infill-frame and its design requirements in case of fire exposure are first reviewed to set the context. Subsequently, the applicability of numerical modeling strategies developed for modeling cold infill-frames to simulate their behavior under fire is critically examined.FindingsThe major hurdles in developing generic numerical models for analyzing thermo-mechanical behavior of infill-frames are identified as: lack of temperature-dependent material properties, scarcity of experimental studies for validation and idealizations in coupling between thermal and structural analysis.Originality valueThis study presents one of the most popular research problems connected with practical and reliable utilization of numerical models, as a good alternative to expensive traditional furnace testing, in assessing fire resistance of infill-frames. It highlights major challenges in thermo-mechanical modeling of infill-frames and critically reviews the available approaches for modeling infill-frames subjected to fire.

Multi‐Type, Multi‐Zone Facility Location

The placement of facilities according to spatial and/or geographic requirements is a popular problem within the domain of location science. Objectives that are typically considered in this class of problems include dispersion, median, center, and covering objectives—and are generally defined in terms of distance or service‐related criteria. With few exceptions, the existing models in the literature for these problems only accommodate one type of facility. Furthermore, the literature on these problems does not allow for the possibility of multiple placement zones within which facilities may be placed. Due to the unique placement requirements of different facility types—such as suitable terrain that may be considered for placement and specific placement objectives for each facility type—it is expected that different suitable placement zones for each facility type, or groups of facility types, may differ. In this article, we introduce a novel mathematical treatment for multi‐type, multi‐zone facility location problems. We derive multi‐type, multi‐zone extensions to the classical integer‐linear programming formulations involving dispersion, centering and maximal covering. The complexity of these formulations leads us to follow a heuristic solution approach, for which a novel multi‐type, multi‐zone variation of the non‐dominated sorting genetic algorithm‐II algorithm is proposed and employed to solve practical examples of multi‐type, multi‐zone facility location problems.

A simple algorithm for calculating the area of an arbitrary polygon

Computing the area of an arbitrary polygon is a popular problem in pure mathematics. The two methods used are Shoelace Method (SM) and Orthogonal Trapezoids Method (OTM). In OTM, the polygon is partitioned into trapezoids by drawing either horizontal or vertical lines through its vertices. The area of each trapezoid is computed and the resultant areas are added up. In SM, a formula which is a generalization of Green’s Theorem for the discrete case is used. The most of the available systems is based on SM. Since an algorithm for OTM is not available in literature, this paper proposes an algorithm for OTM along with efficient implementation. Conversion of a pure mathematical method into an efficient computer program is not straightforward. In order to reduce the run time, minimal computation needs to be achieved. Handling of indeterminate forms and special cases separately can support this. On the other hand, precision error should also be avoided. Salient feature of the proposed algorithm is that it successfully handles these situations achieving minimum run time. Experimental results of the proposed method are compared against that of the existing algorithm. However, the proposed algorithm suggests a way to partition a polygon into orthogonal trapezoids which is not an easy task. Additionally, the proposed algorithm uses only basic mathematical concepts while the Green’s theorem uses complicated mathematical concepts. The proposed algorithm can be used when the simplicity is important than the speed. Keywords. Computational geometry, computer graphics programming, coordinate geometry, euclidian geometry, computer programming.

Пам’ятки гутництва у музеях: проблеми збереження та популяризаціїі

Пам’ятки гутництва у музеях: проблеми збереження та популяризаціїі

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization

The no idle permutation flow shop scheduling problem (NIPFSP) is a popular NP-hard combinatorial optimization problem, which exists in several real world production processes. This study proposes a novel hybrid estimation of the distribution algorithm and cuckoo search (CS) algorithm (HEDA_CS) to solve the NIPFSP with the total tardiness criterion minimization. The problem model is built on the basis of the starting and ending time point of each job. A discrete solution representation method is applied in HEDA_CS to increase the operation efficiency. A novel probability matrix build method is also designed within the knowledge of the processing time matrix. The partially-mapped crossover operation works effectively during the CS phase. A suitable knowledge-based local search is also designed in the HEDA_CS to balance the exploitation and exploration. Finally, many simulations based on the new hard Ruiz benchmarks are conducted. Computational results demonstrate the effectiveness of the proposed HEDA_CS.

A comparative evaluation of swarm intelligence techniques for solving combinatorial optimization problems

This article presents a critical evaluation of swarm intelligence techniques for solving combinatorial optimization problems. Since, unarguably, the traveling salesman’s problem is the most developed, studied, and popular combinatorial problem, this study uses it as a benchmark. After a number of experimental investigations involving 24 popular but complex benchmark symmetric traveling salesman’s problem instances and 15 asymmetric traveling salesman’s problem of the 19 instances available in TSPLIB95, the African buffalo optimization proved to be the best algorithm in terms of efficiency and effectiveness in solving the problems under investigation.