22,104 research outputs found

    Measuring the effectiveness of computer-based scientific visualisations for conceptual development in Australian chemistry classrooms

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    Visual modes of representation have always been very important in science and science education. Interactive computer-based animations and simulations offer new visual resources for chemistry education. Many studies have shown that students enjoy learning with visualisations but few have explored how learning outcomes compare when teaching with or without visualisations. This study employs a quasi-experimental crossover research design and quantitative methods to measure the educational effectiveness - defined as level of conceptual development on the part of students - of using computer-based scientific visualisations versus teaching without visualisations in teaching chemistry. In addition to finding that teaching with visualisations offered outcomes that were not significantly different from teaching without visualisations, the study also explored differences in outcomes for male and female students, students with different learning styles (visual, aural, kinesthetic) and students of differing levels of academic ability

    Computing Replenishment Cycle Policy under Non-stationary Stochastic Lead Time

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    Effects of precompetition state anxiety interventions on performance time and accuracy among amateur soccer players: Revisiting the matching hypothesis

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    In this study, we tested the matching ypothesis, which contends that administration of a cognitive or somatic anxiety intervention should be matched to a participant's dominant anxiety response. Sixty-one male soccer players (mean age 31.6 years, s=6.3) were assigned to one of four groups based on their responses to the Competitive State Anxiety Inventory-2, which was modified to include a directional scale. Interventions were randomly administered in a counterbalanced order 10 min before each performance trial on a soccer skill test. The dominantly cognitive anxious group (n=17), the dominantly somatic anxious group (n=17), and the non-anxious control intervention group (n=14) completed a baseline performance trial. The second and third trials were completed with random administration of brief cognitive and somatic interventions. The non-anxious control group (n=13) completed three trials with no intervention. A mixed-model, GroupTreatment multivariate analysis of variance indicated significant (P0.05), or performance time or accuracy (P>0.05). The present findings do not provide support for the matching hypothesis for state anxiety intensity and direction, or for performance

    Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

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    Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

    An ESPC algorithm based approach to solve inventory deployment problem

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    Global competitiveness has enforced the hefty industries to become more customized. To compete in the market they are targeting the customers who want exotic products, and faster and reliable deliveries. Industries are exploring the option of satisfying a portion of their demand by converting strategically placed products, this helps in increasing the variability of product produced by them in short lead time. In this paper, authors have proposed a new hybrid evolutionary algorithm named Endosymbiotic-Psychoclonal (ESPC) algorithm to determine the amount and type of product to stock as a semi product in inventory. In the proposed work the ability of previously proposed Psychoclonal algorithm to exploit the search space has been increased by making antibodies and antigen more cooperative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results obtained, are compared with other evolutionary algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained, and convergence time required to reach the optimal /near optimal value of the solution

    Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

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    Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

    Effects of intervention upon precompetition state anxiety in elite junior tennis players: The relevance of the matching hypothesis

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    Reproduced with permission of publisher from: Terry, P., Coakley, L., & Karageorghis, C. Effects of intervention upon precompetition state anxiety in elite junior tennis players: the relevance of the matching hypothesis. Perceptual and Motor Skills, 1995, 81, 287-296. © Perceptual and Motor Skills 1995The matching hypothesis proposes that interventions for anxiety should be matched to the modality in which anxiety is experienced. This study investigated the relevance of the matching hypothesis for anxiety interventions in tennis. Elite junior tennis players (N = 100; Age: M = 13.9 yr., SD = 1.8 yr.) completed the Competitive State Anxiety Inventory-2 before and after one of four randomly assigned intervention strategies approximately one hour prior to competition at a National Junior Championship. A two-factor multivariate analysis of variance (group x time) with repeated measures on the time factor gave no significant main effect by group but indicated significant reductions in somatic anxiety and cognitive anxiety and a significant increase in self-confidence following intervention. A significant group by time interaction emerged for self-confidence. The results question the need to match intervention strategy to the mode of anxiety experienced
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