Search results for: evolutionary operations

Authors: Ramandeep Kaur, Gurjit Singh Bhathal

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Cancer diagnosis is very difficult task. Magnetic resonance imaging (MRI) scan is used to produce image of any part of the body and provides an efficient way for diagnosis of cancer or tumor. In existing method, fuzzy clustering mean (FCM) is used for the diagnosis of the tumor. In the proposed method FCM is used to diagnose the cancer of the foot. FCM finds the centroids of the clusters of the foot cancer obtained from MRI images. FCM thresholding result shows the extract region of the cancer. Morphological operations are applied to get extracted region of cancer.

Keywords: magnetic resonance imaging (MRI), fuzzy C mean clustering, segmentation, morphological operations

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Authors: Abdallah A. Hlayel

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Operations research science (OR) deals with good success in developing and applying scientific methods for problem solving and decision-making. However, by using OR techniques, we can enhance the use of computer decision support systems to achieve optimal management for institutions. OR applies comprehensive analysis including all factors that affect on it and builds mathematical modeling to solve business or organizational problems. In addition, it improves decision-making and uses available resources efficiently. The adoption of OR by universities would definitely contributes to the development and enhancement of the performance of OR techniques. This paper provides an understanding of the structures, approaches and models of OR in problem solving and decision-making.

Keywords: best candidates' method, decision making, decision support system, operations research

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.

Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm

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Authors: Mahnaz Hosseinzadeh, Mohammad Reza Mehregan

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In this paper, we have developed a morphological field to assist multi- methodology (combining methodologies together in whole or part) in Operations Research (OR) for the problem contexts in Iranian organizations. So, we have attempted to identify some dimensions for problem context according to Iranian organizational problems. Then, a general morphological program is designed which helps the OR practitioner to determine the suitable OR methodology as output for any configuration of conditions in a problem context as input and to reveal the fields necessary to be improved in OR. Applying such a program would have interesting results for OR practitioners.

Keywords: hard, soft and emancipatory operations research, General Morphological Analysis (GMA), multi-methodology, problem context

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Authors: Emily Kambalame

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Water distribution networks necessitate many investments for construction, prompting researchers to seek cost reduction and efficient design solutions. Optimization techniques are employed in this regard to address these challenges. In this context, the penalty-free multi-objective evolutionary algorithm (PFMOEA) coupled with pressure-dependent analysis (PDA) was utilized to develop a multi-objective evolutionary search for the optimization of water distribution systems (WDSs). The aim of this research was to find out if the computational efficiency of the PFMOEA for WDS optimization could be enhanced. This was done by applying real coding representation and retaining different percentages of feasible and infeasible solutions close to the Pareto front in the elitism step of the optimization. Two benchmark network problems, namely the Two-looped and Hanoi networks, were utilized in the study. A comparative analysis was then conducted to assess the performance of the real-coded PFMOEA in relation to other approaches described in the literature. The algorithm demonstrated competitive performance for the two benchmark networks by implementing real coding. The real-coded PFMOEA achieved the novel best-known solutions ($419,000 and $6.081 million) and a zero-pressure deficit for the two networks, requiring fewer function evaluations than the binary-coded PFMOEA. In previous PFMOEA studies, elitism applied a default retention of 30% of the least cost-feasible solutions while excluding all infeasible solutions. It was found in this study that by replacing 10% and 15% of the feasible solutions with infeasible ones that are close to the Pareto front with minimal pressure deficit violations, the computational efficiency of the PFMOEA was significantly enhanced. The configuration of 15% feasible and 15% infeasible solutions outperformed other retention allocations by identifying the optimal solution with the fewest function evaluation

Keywords: design optimization, multi-objective evolutionary, penalty-free, water distribution systems

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Authors: Ahmed E. Hodaib, Mohamed A. Hashem

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In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design.

Keywords: mathematical optimization, multi-objective evolutionary algorithms "MOEA", computational fluid dynamics "CFD", aerodynamic shape optimization

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Authors: Emmanuela Ngozi Maduka

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In Nigeria, civil-military relations is at its lowest point as a result of the challenges emanating from incessant initiation of military aid to civil authorities (MACA) for internal security operations. This paper is concerned with the question whether it is appropriate for the military to handle internal security crisis with exception to terrorism and armed militia. It analyses the legal framework for MACA in internal security operations which appear to be in contradiction with military tactical and equipment training. The paper argues that the expectation that transitional re-training of the military for internal security operations will reconcile these inconsistencies specifically on the issue of use of force is not practicable and will always pose challenges for both the military and the citizens. Accordingly, this paper adopts a socio-legal methodology for better clarity on the interactions between the legal framework on MACA and military internal security operations. The paper also identifies the lack of effective and proficient paramilitary within the security design of Nigeria as the key issue which results in incessant initiation of MACA and advocates for the establishment of an effective and proficient paramilitary to effectively handle internal security crisis within Nigeria.

Keywords: civil-military relations, MACA, military training, operational challenges, paramilitary, use of force

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Authors: Hafiz Md. HasanBabu, Khandaker Mohammad Mohi Uddin, Md. IstiakJaman Ami, Rahat Hossain Faisal

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Computing in biomolecular programming performs through the different types of reactions. Proteins and nucleic acids are used to store the information generated by biomolecular programming. DNA (Deoxyribose Nucleic Acid) can be used to build a molecular computing system and operating system for its predictable molecular behavior property. The DNA device has clear advantages over conventional devices when applied to problems that can be divided into separate, non-sequential tasks. The reason is that DNA strands can hold so much data in memory and conduct multiple operations at once, thus solving decomposable problems much faster. Programmable Logic Array, abbreviated as PLA is a programmable device having programmable AND operations and OR operations. In this paper, a DNA PLA is designed by different molecular operations using DNA molecules with the proposed algorithms. The molecular PLA could take advantage of DNA's physical properties to store information and perform calculations. These include extremely dense information storage, enormous parallelism, and extraordinary energy efficiency.

Keywords: biological systems, DNA computing, parallel computing, programmable logic array, PLA, DNA

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Authors: Ron Cottam, Nils Langloh, Willy Ranson, Roger Vounckx

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We start by formally describing the requirements for environmental-reaction survival computation in a natural temporally-demanding medium, and develop this into a more general model of the evolutionary context as a computational machine. The effect of this development is to replace deterministic logic by a modified form which exhibits a continuous range of dimensional fractal diffuseness between the isolation of perfectly ordered localization and the extended communication associated with nonlocality as represented by pure causal chaos. We investigate the appearance of life and consciousness in the derived general model, and propose a representation of Nature within which all localizations have the character of quasi-quantal entities. We compare our conclusions with Heisenberg’s uncertainty principle and nonlocal teleportation, and maintain that computability is the principal influence on evolution in the model we propose.

Keywords: computability, evolution, life, localization, modeling, nonlocality

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Authors: S. Yaman, S. Rostami

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In this study, a black box modeling of the coupled-tank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutionary computation method, genetic algorithm. All tuned controllers are applied to the fuzzy model of the coupled-tank experimental setup and analyzed under the different reference input values. According to the results, it is seen that function tuner method demonstrates better robust control performance and guarantees the closed loop stability.

Keywords: function tuner method (FTM), fuzzy modeling, fuzzy PID controller, genetic algorithm (GA)

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Authors: Charalampos Saridakis, Stelios Tsafarakis

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Although environmental concern is on the rise across Europe, current market data indicate that adoption rates of environmentally friendly vehicles remain extremely low. Against this background, the aim of this paper is to a) assess preferences of European consumers for clean-fuel cars and their characteristics and b) design car lines that optimize the combination of fuel types among models in the line-up. In this direction, the authors introduce a new evolutionary mechanism and implement it to stated-preference data derived from a large-scale choice-based conjoint experiment that measures consumer preferences for various factors affecting clean-fuel vehicle (CFV) adoption. The proposed two-step methodology provides interesting insights into how new and existing fuel-types can be combined in a car line that maximizes customer satisfaction.

Keywords: clean-fuel vehicles, product line design, conjoint analysis, choice experiment, differential evolution

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, multi-objective optimization

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Authors: Maneesh Kumar, Roshan Kamal Topno, Manas Ranjan Dikhit, Vahab Ali, Ganesh Chandra Sahoo, Bhawana, Major Madhukar, Rishikesh Kumar, Krishna Pandey, Pradeep Das

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Chandipura vesiculovirus is an emerging (-) ssRNA viral entity belonging to the genus Vesiculovirus of the family Rhabdoviridae, associated with fatal encephalitis in tropical regions. The multi-functionally active viral RNA-dependent RNA polymerase (vRdRp) that has been incorporated with conserved amino acid residues in the pathogens, assigned to synthesize distinct viral polypeptides. The lack of proofreading ability of the vRdRp produces many mutated variants. Here, we have performed the evolutionary analysis of 20 viral protein sequences of vRdRp of different strains of Chandipura vesiculovirus along with other viral species from genus Vesiculovirus inferred in MEGA6.06, employing the Neighbour-Joining method. The p-distance algorithmic method has been used to calculate the optimum tree which showed the sum of branch length of about 1.436. The percentage of replicate trees in which the associated taxa are clustered together in the bootstrap test (1000 replicates), is shown next to the branches. No mutation was observed in the Indian strains of Chandipura vesiculovirus. In vRdRp, 1230(His) and 1231(Arg) are actively participated in catalysis and, are found conserved in different strains of Chandipura vesiculovirus. Both amino acid residues were also conserved in the other viral species from genus Vesiculovirus. Many isolates exhibited maximum number of mutations in catalytic regions in strains of Chandipura vesiculovirus at position 26(Ser→Ala), 47 (Ser→Ala), 90(Ser→Tyr), 172(Gly→Ile, Val), 172(Ser→Tyr), 387(Asn→Ser), 1301(Thr→Ala), 1330(Ala→Glu), 2015(Phe→Ser) and 2065(Thr→Val) which make them variants under different tropical conditions from where they evolved. The result clarifies the actual concept of RNA evolution using vRdRp to develop as an evolutionary marker. Although, a limited number of vRdRp protein sequence similarities for Chandipura vesiculovirus and other species. This might endow with possibilities to identify the virulence level during viral multiplication in a host.

Keywords: Chandipura, (-) ssRNA, viral RNA-dependent RNA polymerase, neighbour-joining method, p-distance algorithmic, evolutionary marker

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Authors: Walid Moudani

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The Crew Reserve Assignment Problem (CRAP) considers the assignment of the crew members to a set of reserve activities covering all the scheduled flights in order to ensure a continuous plan so that operations costs are minimized while its solution must meet hard constraints resulting from the safety regulations of Civil Aviation as well as from the airlines internal agreements. The problem considered in this study is of highest interest for airlines and may have important consequences on the service quality and on the economic return of the operations. In this communication, a new mathematical formulation for the CRAP is proposed which takes into account the regulations and the internal agreements. While current solutions make use of Artificial Intelligence techniques run on main frame computers, a low cost approach is proposed to provide on-line efficient solutions to face perturbed operating conditions. The proposed solution method uses a dynamic programming approach for the duties scheduling problem and when applied to the case of a medium airline while providing efficient solutions, shows good potential acceptability by the operations staff. This optimization scheme can then be considered as the core of an on-line Decision Support System for crew reserve assignment operations management.

Keywords: airlines operations management, combinatorial optimization, dynamic programming, crew scheduling

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Authors: John Hardy

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The expansion of special operations forces (SOF) in the twenty-first century is often discussed in terms of the size and disposition of SOF units. Research regarding the number SOF personnel, the equipment SOF units procure, and the variety of roles and mission that SOF fulfill in contemporary conflicts paints a fascinating picture of changing expectations for the use of force. A strong indicator of the changing nature of SOF in contemporary conflicts is the fusion of strike and intelligence functions in the SOF in many countries. What were once more distinct roles on the kind of battlefield generally associated with the concept of conventional warfare have become intermingled in the era of persistent conflict which SOF face. This study presents a historical analysis of the co-evolution of the intelligence and direct action functions carried out by SOF in counterterrorism, counterinsurgency, and training and mentoring missions between 2004 and 2016. The study focuses primarily on innovation in the US military and the diffusion of key concepts to US allies first, and then more broadly afterward. The findings show that there were three key phases of evolution throughout the period of study, each coinciding with a process of innovation and doctrinal adaptation. The first phase was characterized by the fusion of intelligence at the tactical and operational levels. The second phase was characterized by the industrial counterterrorism campaigns used by US SOF against irregular enemies in Iraq and Afghanistan. The third phase was characterized by increasing forward collection of actionable intelligence by SOF force elements in the course of direct action raids. The evolution of strike and intelligence functions in SOF operations between 2004 and 2016 was significantly influenced by reciprocity. Intelligence fusion led to more effective targeting, which then increased intelligence collection. Strike and intelligence functions were then enhanced by greater emphasis on intelligence exploitation during operations, which further increased the effectiveness of both strike and intelligence operations.

Keywords: counterinsurgency, counterterrorism, intelligence, irregular warfare, military operations, special operations forces

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Authors: Bruna G. M. Araújo, Pedro M. M. Q. Cruz

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In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands.

Keywords: Paul trap, ion-laser interaction, Gaussian operations

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Eugene Y. C. Wong, Henry Y. K. Lau, Mardjuki Raman

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CO2 emissions from maritime transport operations represent a substantial part of the total greenhouse gas emission. Vessels are designed with better energy efficiency. Minimizing CO2 emission in maritime operations plays an important role in supply chain decarbonisation. This paper reviews the initiatives on slow steaming operations towards the reduction of carbon emission. It investigates the relationship and impact among slow steaming cost reduction, carbon emission reduction, and shipment delay. A scenario-based cost-driven decision support model is developed to facilitate the selection of the optimal slow steaming options, considering the cost on bunker fuel consumption, available speed, carbon emission, and shipment delay. The incorporation of the social cost of cargo is reviewed and suggested. Additional measures on the effect of vessels sizes, routing, and type of fuels towards decarbonisation are discussed.

Keywords: slow steaming, carbon emission, maritime logistics, sustainability, green supply chain

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Authors: Dhruba Das

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In this paper, a method has been developed to construct the membership surfaces of row and column vectors and arithmetic operations of imprecise matrix. A matrix with imprecise elements would be called an imprecise matrix. The membership surface of imprecise vector has been already shown based on Randomness-Impreciseness Consistency Principle. The Randomness- Impreciseness Consistency Principle leads to defining a normal law of impreciseness using two different laws of randomness. In this paper, the author has shown row and column membership surfaces and arithmetic operations of imprecise matrix and demonstrated with the help of numerical example.

Keywords: imprecise number, imprecise vector, membership surface, imprecise matrix

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Authors: Awad Khireldin, Colin Law

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Runways are the major infrastructure of airports around the world. Efficient operations of runways are key to ensure that airports are running smoothly with minimal delays. There are many factors that affect the efficiency of runway operations, such as the aircraft wake separation, runways system configuration, the fleet mix, and the runways separation distance. This paper aims to address how to maximize runway operations using a Discrete Event Simulation model. A case study of Cairo International Airport (CIA) is developed to maximize the utilizing of three parallel runways using a simulation model. Different scenarios have been designed where every runway could be assigned for arrival, departure, or mixed operations. A benchmarking study was also included to compare the actual to the proposed results to spot the potential improvements. The simulation model shows that there is a significant difference in utilization and delays between the actual and the proposed ones, there are several recommendations that can be provided to airport management, in the short and long term, to increase the efficiency and to reduce the delays. By including the recommendation with different operations scenarios, such as upgrading the airport slot Coordination from Level 1 to Level 2 in the short term. In the long run, discuss the possibilities to increase the International Air Transport association (IATA) slot coordination to Level 3 as more flights are expected to be handled by the airport. Technological advancements such as radar in the approach full airside simulation model could improve the airport performance where the airport is recommended to review the standard operations procedures with the appropriate authorities. Also, the airport can adopt a future operational plan to accommodate the forecasted additional traffic density in case of adding a fourth terminal building to increase the airport capacity.

Keywords: airport performance, runway, discrete event simulation, capacity, airside

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Authors: Jiawei Li

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How cooperation emerges and persists in a population of selfish agents is a fundamental question in evolutionary game theory. Our research shows that Collective Strategies with Master-Slave Mechanism (CSMSM) defeat Tit-for-Tat and other well-known strategies in spatial iterated prisoner’s dilemma. A CSMSM identifies kin members by means of a handshaking mechanism. If the opponent is identified as non-kin, a CSMSM will always defect. Once two CSMSMs meet, they play master and slave roles. A mater defects and a slave cooperates in order to maximize the master’s payoff. CSMSM outperforms non-collective strategies in spatial IPD even if there is only a small cluster of CSMSMs in the population. The existence and performance of CSMSM in spatial iterated prisoner’s dilemma suggests that cooperation first appears and persists in a group of collective agents.

Keywords: Evolutionary game theory, spatial prisoners dilemma, collective strategy, master-slave mechanism

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Authors: Khodamorad Nabaki, Jianhu Shen, Xiaodong Huang

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This paper develops a method for considering the critical fatigue stress as a constraint in the Bi-directional Evolutionary Structural Optimization (BESO) method. Our aim is to reach an optimal design in which high cycle fatigue failure does not occur for a specific life time. The critical fatigue stress is calculated based on modified Goodman criteria and used as a stress constraint in our topology optimization problem. Since fatigue generally does not occur for compressive stresses, we use the p-norm approach of the stress measurement that considers the highest tensile principal stress in each point as stress measure to calculate the sensitivity numbers. The BESO method has been extended to minimize volume an object subjected to the critical fatigue stress constraint. The optimization results are compared with the results from the compliance minimization problem which shows clearly the merits of our newly developed approach.

Keywords: topology optimization, BESO method, p-norm, fatigue constraint

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Authors: Zhanar Imanova

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Masses of real celestial bodies change anisotropically and reactive forces appear, and they need to be taken into account in the study of these bodies' dynamics. We studied the two-planet problem of three bodies with variable masses in the presence of reactive forces and obtained the equations of perturbed motion in Newton’s form equations. The motion equations in the orbital coordinate system, unlike the Lagrange equation, are convenient for taking into account the reactive forces. The perturbing force is expanded in terms of osculating elements. The expansion of perturbing functions is a time-consuming analytical calculation and results in very cumber some analytical expressions. In the considered problem, we obtained expansions of perturbing functions by small parameters up to and including the second degree. In the non resonant case, we obtained evolution equations in the Newton equation form. All symbolic calculations were done in Wolfram Mathematica.

Keywords: two-planet, three-body problem, variable mass, evolutionary equations

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Authors: Christian Böning, Henrik Prinzhorn, Eric C. Hund, Malte Stonis

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In this article, the flexible job-shop scheduling problem is extended by consideration of energy costs which arise owing to the power peak, and further decision variables such as work in process and throughput time are incorporated into the objective function. This enables a production plan to be simultaneously optimized in respect of the real arising energy and logistics costs. The energy-costs-aware flexible job-shop scheduling problem (EFJSP) which arises is described mathematically, and a memetic algorithm (MA) is presented as a solution. In the MA, the evolutionary process is supplemented with a local search. Furthermore, repair procedures are used in order to rectify any infeasible solutions that have arisen in the evolutionary process. The potential for lowering the real arising costs of a production plan through consideration of energy consumption levels is highlighted.

Keywords: energy costs, flexible job-shop scheduling, memetic algorithm, power peak

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Authors: Sahithi Yarlagadda

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The design of antenna is constrained by mathematical and geometrical parameters. Though there are diverse antenna structures with wide range of feeds yet, there are many geometries to be tried, which cannot be customized into predefined computational methods. The antenna design and optimization qualify to apply evolutionary algorithmic approach since the antenna parameters weights dependent on geometric characteristics directly. The evolutionary algorithm can be explained simply for a given quality function to be maximized. We can randomly create a set of candidate solutions, elements of the function's domain, and apply the quality function as an abstract fitness measure. Based on this fitness, some of the better candidates are chosen to seed the next generation by applying recombination and permutation to them. In conventional approach, the quality function is unaltered for any iteration. But the antenna parameters and geometries are wide to fit into single function. So, the weight coefficients are obtained for all possible antenna electrical parameters and geometries; the variation is learnt by mining the data obtained for an optimized algorithm. The weight and covariant coefficients of corresponding parameters are logged for learning and future use as datasets. This paper drafts an approach to obtain the requirements to study and methodize the evolutionary approach to automated antenna design for our past work on Vivaldi antenna as test candidate. The antenna parameters like gain, directivity, etc. are directly caged by geometries, materials, and dimensions. The design equations are to be noted here and valuated for all possible conditions to get maxima and minima for given frequency band. The boundary conditions are thus obtained prior to implementation, easing the optimization. The implementation mainly aimed to study the practical computational, processing, and design complexities that incur while simulations. HFSS is chosen for simulations and results. MATLAB is used to generate the computations, combinations, and data logging. MATLAB is also used to apply machine learning algorithms and plotting the data to design the algorithm. The number of combinations is to be tested manually, so HFSS API is used to call HFSS functions from MATLAB itself. MATLAB parallel processing tool box is used to run multiple simulations in parallel. The aim is to develop an add-in to antenna design software like HFSS, CSTor, a standalone application to optimize pre-identified common parameters of wide range of antennas available. In this paper, we have used MATLAB to calculate Vivaldi antenna parameters like slot line characteristic impedance, impedance of stripline, slot line width, flare aperture size, dielectric and K means, and Hamming window are applied to obtain the best test parameters. HFSS API is used to calculate the radiation, bandwidth, directivity, and efficiency, and data is logged for applying the Evolutionary genetic algorithm in MATLAB. The paper demonstrates the computational weights and Machine Learning approach for automated antenna optimizing for Vivaldi antenna.

Keywords: machine learning, Vivaldi, evolutionary algorithm, genetic algorithm

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Authors: Maressa Aires de Proenca

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The inclusion of a gender perspective in peace operations is based on a series of conventions, treaties, and resolutions designed to protect and include women addressing gender mainstreaming. The UN Security Council recognizes that women's participation and gender equality within peace operations are indispensable for achieving sustainable development and peace. However, the participation of women in the field of peace and security is still embryonic. There are gaps when we think about female participation in conflict resolution and peace promotion spaces, and it does not seem clear how women are present in these spaces. This absence may correspond to silence about representation and the guarantee of the female perspective within the context of peace promotion. Thus, the present research aimed to describe the panorama of the participation of women who are currently active in the 14 active UN peace operations, which are: 1) MINUJUSTH, Haiti, 2) MINURSO, Western Sahara, 3) MINUSCA, Central African Republic, 4) MINUSMA, Mali, 5) MONUSCO, the Democratic Republic of the Congo, 6) UNAMID, Darfur, 7) UNDOF, Golan, 8) UNFICYP, Cyprus, 9) UNIFIL, Lebanon, 10) UNISFA, Abyei, 11) UNMIK, Kosovo, 12) UNMISS, South Sudan, 13) UNMOGIP, India, and Pakistan, and 14) UNTSO, Middle East. A database was constructed that reported: (1) position held by the woman in the peace operation, (2) her profession, (3) educational level, (4) marital status, (5) religion, (6) nationality, (8) number of years working with peace operations, (9) whether the operation in which it operates has provided training on gender issues. For the construction of this database, official reports and statistics accessed through the UN Peacekeeping Resource Hub were used; The United Nations Statistical Commission, Peacekeeping Master Open Datasets, The Armed Conflict Database (ACD), The International Institute for Strategic Studies (IISS) database; Armed Conflict Location & Event Data Project (ACLED) database; from the Evidence and Data for Gender Equality (EDGE) database. In addition to access to databases, peacekeeping operations will be contacted directly, and data requested individually. The database showed that the presence of women in these peace operations is still incipient, but growing. There are few women in command positions, and most of them occupy administrative or human-care positions.

Keywords: women, peace and security, peacekeeping operations, peace studies

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Authors: Hyun-Ho Lee, Kee-Won Kim

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The arithmetic operations over GF(2m) have been extensively used in error correcting codes and public-key cryptography schemes. Finite field arithmetic includes addition, multiplication, division and inversion operations. Addition is very simple and can be implemented with an extremely simple circuit. The other operations are much more complex. The multiplication is the most important for cryptosystems, such as the elliptic curve cryptosystem, since computing exponentiation, division, and computing multiplicative inverse can be performed by computing multiplication iteratively. In this paper, we present a parallel computation algorithm that operates Montgomery multiplication over finite field using redundant basis. Also, based on the multiplication algorithm, we present an efficient semi-systolic multiplier over finite field. The multiplier has less space and time complexities compared to related multipliers. As compared to the corresponding existing structures, the multiplier saves at least 5% area, 50% time, and 53% area-time (AT) complexity. Accordingly, it is well suited for VLSI implementation and can be easily applied as a basic component for computing complex operations over finite field, such as inversion and division operation.

Keywords: finite field, Montgomery multiplication, systolic array, cryptography

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Authors: Aslı Şalcıoğlu, Grigorous Krey, Raşit Bilgin

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In this study, the effect of the Turkish Straits System (TSS), comprising a biogeographical boundary that forms the connection between the Mediterranean and the Black Sea, on the evolutionary history, phylogeography and intraspecific gene flow of the whiting (Merlangius merlangus) a demersal fish species, was investigated. For these purposes, the mitochondrial DNA (CO1, cyt-b) genes were used. In addition, genetic comparisons samples from other regions (Greece, France, Atlantic) obtained from GenBank and Barcode of Life Database were made to better understand the phylogeographic history of the species at a larger geographic scale. Within this study, high level of genetic differentiation was observed along the Turkish coastal waters based on cyt-b gene, suggesting that TSS is a barrier to dispersal. Two different sub-species were also observed based on mitochondrial DNA, one found in Turkish coastal waters and Greece (M.m euxinus) and other (M.m. merlangus) in Atlantic, France.

Keywords: genetic, phylogeography, TSS, whiting

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Authors: George Hadjinicola, Andreas Soteriou

Abstract:

This paper examines the relationship between the number of price promotions that a firm should conduct per year and the level of safety stocks that the firm should maintain. Price promotions result in temporary sales increases, which affect the operations function through (1) an increase in the quantities demanded and (2) an increase in safety stocks required to maintain the desired service level. We propose a modeling framework where both price promotions and improved service levels, operationalized through higher safety stocks, can affect sales. We treat the annual number of promotions as a decision variable. We identify market conditions where the operations function, through improved safety stocks, can complement price promotions or even play the leading role in sales increases.

Keywords: price promotions, safety stocks, marketing/operations interface, mathematical model

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Authors: Abdel-Aziz M. Mohamed

Abstract:

This paper presents a state-of-the-art survey of the operations research models developed for internal audit planning. Two alternative approaches have been followed in the literature for audit planning: (1) identifying the optimal audit frequency; and (2) determining the optimal audit resource allocation. The first approach identifies the elapsed time between two successive audits, which can be presented as the optimal number of audits in a given planning horizon, or the optimal number of transactions after which an audit should be performed. It also includes the optimal audit schedule. The second approach determines the optimal allocation of audit frequency among all auditable units in the firm. In our review, we discuss both the deterministic and probabilistic models developed for audit planning. In addition, game theory models are reviewed to find the optimal auditing strategy based on the interactions between the auditors and the clients.

Keywords: operations research applications, audit frequency, audit-staff scheduling, audit planning

Procedia PDF Downloads 788