Search results for: VM allocation problem

Authors: Felippe Clemente, Viviani S. Lírio

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Brazilian tax evasion is very high. It causes many problems for economics as budget realization, income distribution and no allocation of productive resources. Therefore, the purpose of this article is to use the instrumental game theory to understand tax evasion agents and tax authority in Brazil (Federal Revenue and Federal Police). By means of Game Theory approaches, the main results from considering cases both with and without specialists show that, in a high dropout situation, penalizing taxpayers with either high fines or deprivations of liberty may not be very effective. The analysis also shows that audit and inspection costs play an important role in driving the equilibrium system. This would suggest that a policy of investing in tax inspectors would be a more effective tool in combating non-compliance with tax obligations than penalties or fines.

Keywords: tax evasion, Brazil, game theory, specialists

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Authors: Suleiman Aliyu

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Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies.

Keywords: pareto optimality, fog application deployment, resource allocation, internet of things

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: H. M. Soroush

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The problem of scheduling products and services for on-time deliveries is of paramount importance in today’s competitive environments. It arises in many manufacturing and service organizations where it is desirable to complete jobs (products or services) with different weights (penalties) on or before their due dates. In such environments, schedules should frequently decide whether to schedule a job based on its processing time, due-date, and the penalty for tardy delivery to improve the system performance. For example, it is common to measure the weighted number of late jobs or the percentage of on-time shipments to evaluate the performance of a semiconductor production facility or an automobile assembly line. In this paper, we address the problem of scheduling a set of jobs on a server where processing times or due-dates of jobs are random variables and fixed weights (penalties) are imposed on the jobs’ late deliveries. The goal is to find the schedule that minimizes the expected weighted number of tardy jobs. The problem is NP-hard to solve; however, we explore three scenarios of the problem wherein: (i) both processing times and due-dates are stochastic; (ii) processing times are stochastic and due-dates are deterministic; and (iii) processing times are deterministic and due-dates are stochastic. We prove that special cases of these scenarios are solvable optimally in polynomial time, and introduce efficient heuristic methods for the general cases. Our computational results show that the heuristics perform well in yielding either optimal or near optimal sequences. The results also demonstrate that the stochasticity of processing times or due-dates can affect scheduling decisions. Moreover, the proposed problem is general in the sense that its special cases reduce to some new and some classical stochastic single machine models.

Keywords: number of late jobs, scheduling, single server, stochastic

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Abdous Noureddine

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The problem of funding in Algeria emerged as a problem you need to study after many Algerians researchers pointed out that the faltering Algerian public economic institution due to the imbalance in the financial structures and lower steering and marketing efficiency, as well as a result of severe expansion of borrowing because of inadequate own resources, and the consequent inability This institution to repay loans and interest payments, in addition to increasing reliance on overdraft so used to finance fixed assets, no doubt that this deterioration requires research and study of the causes and aspects of treatment, which addresses the current study, aside from it.

Keywords: financial structure, financial capital, equity, debt, firm’s value, return, leverage

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Authors: Majid Khalili

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This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective variable neighborhood algorithm (MOVNS). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOVNS provides sound performance comparing with other algorithms.

Keywords: no-wait hybrid flowshop scheduling; multi-objective variable neighborhood algorithm; makespan; total weighted tardiness

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Authors: Vladimir S. Timofeev

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In this article the problem of distributional moments estimation is considered. The new approach of moments estimation based on usage of the characteristic function is proposed. By statistical simulation technique, author shows that new approach has some robust properties. For calculation of the derivatives of characteristic function there is used numerical differentiation. Obtained results confirmed that author’s idea has a certain working efficiency and it can be recommended for any statistical applications.

Keywords: characteristic function, distributional moments, robustness, outlier, statistical estimation problem, statistical simulation

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Authors: Eric Huang, Coleman DeLude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

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High performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from electromagnetic (EM) simulations is often times cumbersome leading to large storage requirement. This paper proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. This paper solves this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.

Keywords: RADAR, RCS, high performance computing, point scatterer model

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Authors: Nivedha Rajaram

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Hybrid Quantum solvers have been given prime focus in recent days by computation problem-solving domain industrial applications. D’Wave Quantum Computers are one such paragon of systems built using quantum annealing mechanism. Discrete Quadratic Models is a hybrid quantum computing model class supplied by D’Wave Ocean SDK - a real-time software platform for hybrid quantum solvers. These hybrid quantum computing modellers can be employed to solve classic problems. One such problem that we consider in this paper is finding a network connectivity knowledge hub in a huge network of systems. Using this quantum solver, we try to find out the prime system hub, which acts as a supreme connection point for the set of connected computers in a large network. This paper establishes an innovative problem approach to generate a connectivity system hub plot for a set of systems using DWave ocean SDK hybrid quantum solvers.

Keywords: quantum computing, hybrid quantum solver, DWave annealing, network knowledge graph

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Authors: Ons Sassi, Wahiba Ramdane Cherif-Khettaf, Ammar Oulamara

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In this paper, we consider a new real-life Heterogenous Electric Vehicle Routing Problem with Time Dependant Charging Costs and a Mixed Fleet (HEVRP-TDMF), in which a set of geographically scattered customers have to be served by a mixed fleet of vehicles composed of a heterogenous fleet of Electric Vehicles (EVs), having different battery capacities and operating costs, and Conventional Vehicles (CVs). We include the possibility of charging EVs in the available charging stations during the routes in order to serve all customers. Each charging station offers charging service with a known technology of chargers and time-dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are not necessarily compatible with all available charging technologies and a partial charging is allowed. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and then minimize the total travel and charging costs. In this study, we present a Mixed Integer Programming Model and develop a Charging Routing Heuristic and a Local Search Heuristic based on the Inject-Eject routine with three different insertion strategies. All heuristics are tested on real data instances.

Keywords: charging problem, electric vehicle, heuristics, local search, optimization, routing problem

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Authors: Devinder Singh, Rajneesh Kumar, Arvind Kumar

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The present investigation deals with the deformation of micropolar generalized thermoelastic solid subjected to thermo-mechanical loading due to a thermal laser pulse. Laplace transform and Fourier transform techniques are used to solve the problem. Thermo-mechanical laser interactions are taken as distributed sources to describe the application of the approach. The closed form expressions of normal stress, tangential stress, coupled stress and temperature are obtained in the domain. Numerical inversion technique of Laplace transform and Fourier transform has been implied to obtain the resulting quantities in the physical domain after developing a computer program. The normal stress, tangential stress, coupled stress and temperature are depicted graphically to show the effect of relaxation times. Some particular cases of interest are deduced from the present investigation.

Keywords: pulse laser, integral transform, thermoelastic, boundary value problem

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Authors: Samia Sadouki Chibani, Abdelkamel Tari

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Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.

Keywords: bio-inspired algorithms, elephant herding optimization, QoS optimization, web service composition

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Authors: Chenchen Liu, Jacques Audran

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Using tablet apps with a certain educational purpose to promote young children’s cognitive development, is quite common now. Developing an educational app on an Ipad like tablet, especially for a young child (age 3-5) requires an optimal level of challenge to continuously attract children’s attention and obtain an educational effect. Adaptive difficulty adjustment, which could dynamically set the difficulty in the challenge according to children’s performance, seems to be a good solution. Since space concept plays an important role in young children’s cognitive development, we made an experimental comparison in a French kindergarten between one group of 23 children using an educational app ‘Debout Ludo’ with adaptive difficulty settings and another group of 20 children using the previous version of ‘Debout Ludo’ with a classic incremental difficulty adjustment. The experiment results of spatial problem solving indicated that a significantly higher learning outcome was acquired by the young children who used the adaptive version of the app.

Keywords: adaptive difficulty, spatial problem solving, tactile tablet, young children

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Authors: A. H. Choudhury

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In this paper, a highly accurate numerical method for the solution of one-dimensional fourth-order wave equation is derived. This hyperbolic problem is solved by using semidiscrete approximations. The space direction is discretized by wavelet-Galerkin method, and the time variable is discretized by using Newmark schemes.

Keywords: hyperbolic problem, semidiscrete approximations, stability, Wavelet-Galerkin Method

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Authors: Saeedeh Ahmadi Basir, Mohammad Mahdavi Mazdeh, Mohammad Namakshenas

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Manufacturers often dispatch jobs in batches to reduce delivery costs. However, sending several jobs in batches can have a negative effect on other scheduling-related objective functions such as minimizing the number of tardy jobs which is often used to rate managers’ performance in many manufacturing environments. This paper aims to minimize the number of weighted tardy jobs and the sum of delivery costs of a two-stage assembly flow-shop problem in a batch delivery system. We present a mixed-integer linear programming (MILP) model to solve the problem. As this is an MILP model, the commercial solver (the CPLEX solver) is not guaranteed to find the optimal solution for large-size problems at a reasonable amount of time. We present several numerical examples to confirm the accuracy of the model.

Keywords: scheduling, two-stage assembly flow-shop, tardy jobs, batched delivery system

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: Ghada Badr, Manar Hosny, Nuha Bintayyash, Eman Albilali, Souad Larabi Marie-Sainte

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The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms.

Keywords: median problem, phylogenetic tree, permutation, genetic algorithm, beam search, genome rearrangement distance

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Authors: Apurva Patil, Maithilee Kulkarni, Ashay Aswale

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This paper proposes an algorithm to develop the kinematic model of a 5 DOF robot arm. The formulation of the problem is based on finding the D-H parameters of the arm. Brute Force iterative method is employed to solve the system of non linear equations. The focus of the paper is to obtain the accurate solutions by reducing the root mean square error. The result obtained will be implemented to grip the objects. The trajectories followed by the end effector for the required workspace coordinates are plotted. The methodology used here can be used in solving the problem for any other kinematic chain of up to six DOF.

Keywords: 5 DOF robot arm, D-H parameters, inverse kinematics, iterative method, trajectories

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Authors: Olusegun Adeleke Adenuga

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The study examined effect of fishbone creative thinking strategy on problem-solving skills of public teachers in Ogun state, Nigeria. A 2x2x2 factorial design was employed for the study which consisted of 80 participants made up of 40 male and 40 female public teachers randomly selected among public teaching personnel from the two local government area headquarters (Ijebu-ode and Ijebu-Igbo) within Ogun East Senatorial District. Each treatment group received 45minutes instructions and training per week for 8weeks. Data was collected from participants with the use of standardized instrument tagged ‘Problem Solving Inventory’ (PSI) developed by the researchers prior to the training to form a pre-test and immediately after eight weeks of training to form a post-test. One hypothesis was tested; the data obtained was analyzed using Analysis of Covariance (ANCOVA) tested at significance level of 0.05. The result of the data analysis shows that there was a significant effect of the fishbone creative thinking technique on the participants (F (2,99) = 12.410; p <.05). Based on the findings, it is therefore recommended that the report of this study be used to effect organizational change and development of teaching service in Nigeria through teachers’ retraining and capacity building.

Keywords: fishbone, creative thinking strategy, and problem-solving skills, public teachers

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a new hybrid particle swarm optimization algorithm is proposed for the multi-objective flexible job shop scheduling problem that is very important and hard combinatorial problem. The Pareto approach is used for solving the multi-objective problem. Several new local search heuristics are integrated into an algorithm based on the critical block concept to enhance the performance of the algorithm. The algorithm is compared with the recently published multi-objective algorithms based on benchmarks selected from the literature. Several metrics are used for quantifying performance and comparison of the achieved solutions. The algorithms are also compared based on the Weighting summation of objectives approach. The proposed algorithm can find the Pareto solutions more efficiently than the compared algorithms in less computational time.

Keywords: swarm-based optimization, local search, Pareto optimality, flexible job shop scheduling, multi-objective optimization

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Authors: Kiatkulchai Jitt-Aer, Graham Wall, Dylan Jones

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As a result of the Indian Ocean tsunami in 2004, logistics applied to disaster relief operations has received great attention in the humanitarian sector. As learned from such disaster, preparing and responding to the aspect of delivering essential items from distribution centres to affected locations are of the importance for relief operations as the nature of disasters is uncertain especially in suffering figures, which are normally proportional to quantity of supplies. Thus, this study proposes a spatial decision support system (SDSS) for humanitarian logistics by integrating Geographical Information Systems (GIS) and the capacitated vehicle routing problem (CVRP). The GIS is utilised for acquiring demands simulated from the tsunami flooding model of the affected area in the first stage, and visualising the simulation solutions in the last stage. While CVRP in this study encompasses designing the relief routes of a set of homogeneous vehicles from a relief centre to a set of geographically distributed evacuation points in which their demands are estimated by using both simulation and randomisation techniques. The CVRP is modeled as a multi-objective optimization problem where both total travelling distance and total transport resources used are minimized, while demand-cost efficiency of each route is maximized in order to determine route priority. As the model is a NP-hard combinatorial optimization problem, the Clarke and Wright Saving heuristics is proposed to solve the problem for the near-optimal solutions. The real-case instances in the coastal area of Phuket, Thailand are studied to perform the SDSS that allows a decision maker to visually analyse the simulation scenarios through different decision factors.

Keywords: demand simulation, humanitarian logistics, geographical information systems, relief operations, capacitated vehicle routing problem

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Authors: S. H. Borghei, E. Teymourian, M. Mobin, G. M. Komaki, S. Sheikh

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Imperialist competitive algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population based algorithm which has achieved a great performance in comparison to other meta-heuristics. This study is about developing enhanced ICA approach to solve the cell formation problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems.

Keywords: cell formation problem, group technology, imperialist competitive algorithm, sequence data

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Authors: Mahboobeh Sadeghi, Zahra Izadi Khah, Mansour Hakim Javadi, Masoud Gholamali Lavasani

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Background: Since there is a relation between psychological and physiological factors, the aim of this study was to examine the effect of Emotion Regulation training on cognitive emotion regulation problem in patients with Multiple Sclerosis(MS) Method: In a randomized clinical trial thirty patients diagnosed with Multiple Sclerosis referred to state welfare organization were selected. The sample group was randomized into either an experimental group or a nonintervention control group. The subjects participated in 75-minute treatment sessions held three times a week for 4weeks (12 sessions). All 30 individuals were administered with Cognitive Emotion Regulation questionnaire (CERQ). Participants completed the questionnaire in pretest and post-test. Data obtained from the questionnaire was analyzed using Mancova. Results: Emotion Regulation significantly decreased the Cognitive Emotion Regulation problems patients with Multiple sclerosis (p < 0.001). Conclusions: Emotion Regulation can be used for the treatment of cognitive-emotion regulation problem in Multiple sclerosis.

Keywords: Multiple Sclerosis, cognitive-emotion regulation, emotion regulation, MS

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Authors: Olayemi Bakre

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The majoritant of the South African rural populace are directly or indirectly engaged in agricultural practices for a livelihood. However, impediments such as the climate change and inadequacy of governmental support has undermined the once thriving subsistence farming communities of South Africa. Furthermore, the poor leadership in hydrology, coupled with lack of depths in skills to facilitate the understanding and acceptance of groundwater from national level to local governance has made it near impossible for subsistence farmers to optimally benefit from the groundwater beneath their feet. The 2012 drought experienced in South Africa paralysed the farming activities across several subsistence farming communities across the KwaZulu-Natal Province. To revamp subsistence farming, a variety of interventions and strategies such as the Resource Poor Farmers (RPF) and Water Allocation Reforms (WAR) have been launched by the Department of Water and Sanitation (DWS) as an agendum to galvanising the defunct subsistence farming communities of KwaZulu-Natal as well as other subsistence farming communities across South Africa. Despite the enormous resources expended on the subsistence farming communities whom often fall under the Historically Disadvantaged Individuals (HDI); indicators such as the unsustainable farming practices, poor crop yield, pitiable living condition as well as the poor standard of living, are evidential to the claim that these afore cited interventions and a host of other similar strategies indicates that these initiatives have not yield the desired result. Thus, this paper seeks to suggest practicable interventions aimed at salvaging the vulnerability of subsistence farmers within the province understudy. The study pursued a qualitative approach as the view of experts on ground water and similarly related fields from the DWS were solicited as an agendum to obtaining in-depth perspective into the current study. Some of the core challenges undermining the sustainability and growth of subsistence farming in the area of study were - inadequacy of experts (engineers, scientist, researchers) in ground water; water shortages; lack of political will as well as lack of coordination among stakeholders. As an agendum to optimising the ground water usage for subsistence farming, this paper advocates the strengthening of geohydrological skills, development of technical training capacity, interactive participation among stakeholders as well as the initiation of Participatory Action Research as an agenda to optimising the available ground water in KwaZulu-Natal which is intended to orchestrate a sustainable and viable subsistence farming practice within the province.

Keywords: subsistence farming, ground water optimisation, resource poor farmers, and water allocation reforms, hydrology

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Authors: Emily J. Morey, Kevin Galvin, Thomas Riley, R. Eddie Wilson

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The key challenges faced by integrating autonomous rail operations into the existing mainline railway environment have been identified through the understanding and framing of the problem space and stakeholder analysis. This was achieved through the completion of the first four steps of Soft Systems Methodology, where the problem space has been expressed via conceptual models. Having identified these challenges, we investigated one of them, namely capacity, via the use of models and simulation. This paper examines the approach used to move from the conceptual models to a simulation which can determine whether the integration of autonomous trains can plausibly increase capacity. Within this approach, we developed an architecture and converted logical models into physical resource models and associated design features which were used to build a simulator. From this simulator, we are able to analyse mixtures of legacy-autonomous operations and produce fundamental diagrams and trajectory plots to describe the dynamic behaviour of mixed mainline railway operations.

Keywords: autonomy, executable architecture, modelling and simulation, railway capacity

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Authors: Wenjun Hou, Marek Perkowski

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The Traveling Salesman problem is famous in computing and graph theory. In short, it asks for the Hamiltonian cycle of the least total weight in a given graph with N nodes. All variations on this problem, such as those with K-bounded-degree nodes, are classified as NP-complete in classical computing. Although several papers propose theoretical high-level designs of quantum algorithms for the Traveling Salesman Problem, no quantum circuit implementation of these algorithms has been created up to our best knowledge. In contrast to previous papers, the goal of this paper is not to optimize some abstract complexity measures based on the number of oracle iterations, but to be able to evaluate the real circuit and time costs of the quantum computer. Using the emerging quantum programming language Q# developed by Microsoft, which runs quantum circuits in a quantum computer simulation, an implementation of the bounded-degree problem and its respective quantum circuit were created. To apply Grover’s algorithm to this problem, a quantum oracle was designed, evaluating the cost of a particular set of edges in the graph as well as its validity as a Hamiltonian cycle. Repeating the Grover algorithm with an oracle that finds successively lower cost each time allows to transform the decision problem to an optimization problem, finding the minimum cost of Hamiltonian cycles. N log₂ K qubits are put into an equiprobablistic superposition by applying the Hadamard gate on each qubit. Within these N log₂ K qubits, the method uses an encoding in which every node is mapped to a set of its encoded edges. The oracle consists of several blocks of circuits: a custom-written edge weight adder, node index calculator, uniqueness checker, and comparator, which were all created using only quantum Toffoli gates, including its special forms, which are Feynman and Pauli X. The oracle begins by using the edge encodings specified by the qubits to calculate each node that this path visits and adding up the edge weights along the way. Next, the oracle uses the calculated nodes from the previous step and check that all the nodes are unique. Finally, the oracle checks that the calculated cost is less than the previously-calculated cost. By performing the oracle an optimal number of times, a correct answer can be generated with very high probability. The oracle of the Grover Algorithm is modified using the recalculated minimum cost value, and this procedure is repeated until the cost cannot be further reduced. This algorithm and circuit design have been verified, using several datasets, to generate correct outputs.

Keywords: quantum computing, quantum circuit optimization, quantum algorithms, hybrid quantum algorithms, quantum programming, Grover’s algorithm, traveling salesman problem, bounded-degree TSP, minimal cost, Q# language

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Authors: Ilgoun Kim, Jongpil Jeong

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Recently, a connected industrial parks (CIPs) architecture using new technologies such as RFID, cloud computing, CPS, Big Data, 5G 5G, IIOT, VR-AR, and ventral AI algorithms based on IoT has been proposed. This researcher noted the vehicle junction problem (VJP) as a more specific detail of the CIPs architectural models. The VJP noted by this researcher includes 'efficient AI physical connection challenges for vehicles' through ventilation, 'financial and financial issues with complex vehicle physical connections,' and 'welfare and working conditions of the performing personnel involved in complex vehicle physical connections.' In this paper, we propose a public solution architecture for the 'electronic financial problem of complex vehicle physical connections' as a detailed task during the vehicle junction problem (VJP). The researcher sought solutions to businesses, consumers, and Korean social problems through technological advancement. We studied how the beneficiaries of technological development can benefit from technological development with many consumers in Korean society and many small and small Korean company managers, not some specific companies. In order to more specifically implement the connected industrial parks (CIPs) architecture using the new technology, we noted the vehicle junction problem (VJP) within the smart factory industrial complex and noted the process of achieving the vehicle junction problem performance among several electronic processes. This researcher proposes a more detailed, integrated public finance enterprise architecture among the overall CIPs architectures. The main details of the public integrated financial enterprise architecture were largely organized into four main categories: 'business', 'data', 'technique', and 'finance'.

Keywords: enterprise architecture, IT Finance, smart logistics, CIPs

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Authors: Maryam Davodi-Far

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Although sustainability offers a vision to preserve the earth’s resources while sustaining life on earth, there tends to be injustice and disparity in how resources are allocated across the globe. As such, the question that arises is whom will sustainability benefit? Will the rich grow richer and the poor become worse off? Is there a way to find balance between sustainability and still implement and achieve success with distributive justice theories? One of the facets of justice is distributive justice; the idea of balancing benefits and costs associated with the way in which we disseminate and consume goods. Social justice relies on how the cost and burdens of our resource allocation can be done reasonably and equitably and spread across a number of societies, and within each society spread across diverse groups and communities. In the end, the question is how to interact with the environment and diverse communities of today and of those communities of the future.

Keywords: consumerism, sustainability, sustainable development, social justice, social equity, distributive justice

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Authors: Thomas Slunecko Karat

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The years since 9/11 2001 have given us a plethora of research papers with the word ‘terrorism’ in the title. Yet only a small subset of these papers has produced new data, which explains why more than 20 years of research since 9/11 have done little to increase our understanding of the mechanisms that lead to terrorism. Specifically, terrorism scholars are divided by political, temporal, geographical and financial demarcation lines which prevent a clear definition of terrorism. As a consequence, the true root causes of terrorism remain unexamined. Instead, the psychopathological conditions of the individual have been emphasized despite ample empirical evidence pointing in a different direction. This paper examines the underlying reasons and motives that prevent open discourse about the root causes of terrorism and proposes that terrorism is linked to the current international system of resource allocation and systematic violations of human rights.

Keywords: terrorism, root causes of terrorism, prevention of terrorism, racism, human rights violations

Procedia PDF Downloads 87