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Abstract: A wealth of evidence shows that real-world networks are endowed with the small-world property, i.e., that the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. In addition, most social networks are organized so that no individual is more than six connections apart from any other, an empirical regularity known as the six degrees of separation. Why social networks have this ultrasmall-world organization, whereby the graph’s diameter is independent of the network size over several orders of magnitude, is still unknown. We show that the “six degrees of separation” is the property featured by the equilibrium state of any network where individuals weigh between their aspiration to improve their centrality and the costs incurred in forming and maintaining connections. We show, moreover, that the emergence of such a regularity is compatible with all other features, such as clustering and scale-freeness, that normally characterize the structure of social networks. Thus, our results show how simple evolutionary rules of the kind traditionally associated with human cooperation and altruism can also account for the emergence of one of the most intriguing attributes of social networks.
Keywords: degree distribution, network evolution, complex network, small-world network, social physics
Published in DKUM: 16.07.2024; Views: 111; Downloads: 10
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Abstract: Recently, an increasing number of electromagnetic devices have been using smart fluids. These include ferrofluids, electrorheological fluids, and magnetorheological (MR) fluids. In the paper, magnetorheological fluids are considered for use in a spherical actuator for haptic applications. An approach is presented to the design and optimization of such a device, using finite element method modelling linked with differential evolution (DE). Much consideration was given to the construction of the objective function to be minimized. A novel approach to objective function assembly was used, using reference values based on the model design and created with parameters set to the midpoint values of the selected range. It was found to be a useful strategy when the reference values are unknown. There were four parameters to be optimized. Three of them gravitated towards the boundary value, and the fourth (actuator radius) was somewhere in between. The value of the objective function reached a minimum in the range of actuator radius between 42.9880 mm and 45.0831 mm, which is about a 5% difference in regard to the actuator radius. Three passes of optimization were performed with similar results, proving the robustness of the algorithm.
Keywords: magnetorheological fluid, finite element method, FEM, optimization, differntial evolution, DE, actuator
Published in DKUM: 22.05.2024; Views: 173; Downloads: 16
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Abstract: The determination is presented of seven parameters of a DC motor’s drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters’ determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive’s acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters’ calculations were less than 2%.
Keywords: differential evolution, artificial bee colony, teaching-learning based optimization, DC motors, electric drive
Published in DKUM: 26.01.2024; Views: 230; Downloads: 23
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Abstract: Real parameter optimization problems are often very complex and computationally expensive. We can find such problems in engineering and scientific applications. In this paper, a new algorithm is proposed to tackle the 100-Digit Challenge. There are 10 functions representing 10 optimization problems, and the goal is to compute each function’s minimum value to 10 digits of accuracy. There is no limit on either time or the maximum number of function evaluations. The proposed algorithm is based on the self-adaptive differential evolution algorithm jDE. Our algorithm uses two populations and some other mechanisms when tackling the challenge. We provide the score for each function as required by the organizers of this challenge competition.
Keywords: differential evolution, optimization, global optimum, accuracy
Published in DKUM: 23.01.2023; Views: 543; Downloads: 24
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Abstract: Evolution by natural selection is the fundamental backbone of the life sciences. Therefore, it is important for teacher education programs to ensure graduates possess a strong knowledge of evolution in order to teach at all levels of biology education. The main aim was to investigate the impacts of the Slovene pre-service preschool and primary school teachers' religiosity and educational background on their evolutionary content knowledge. In the present study, understanding of five evolutionary topics, religiosity and educational background of 269 students was studied. Results show that students have a very poor understanding of evolution. They very often use teleological reasoning. Although many pieces of research have shown that religiosity can be in conflict with evolutionary theory, our findings show that religiosity does not significantly correlate with evolutionary knowledge nor to the educational background of students. However, for students' understanding of evolution, it is important how many years of biology lessons they had in secondary school. This should be better taken into account by educational policymakers because evolutionary principles are becoming increasingly relevant in medicine, agriculture and other socio-scientific topics.
Keywords: vzgoja in izobraževanje, bodoči učitelji, religija, Slovenija, educational background, evolution knowledge, pre-service teachers, religion, Slovenia
Published in DKUM: 02.09.2022; Views: 700; Downloads: 24
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