A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.

Kriopreservasi Tunas in Vitro Pepaya ‘Sukma’ Dengan Perlakuan Prakultur, Loading, Dan Dehidrasi Dengan Pvs2 Dan Modifikasinya

Papaya has high genetic variability because it is an open-pollinated plant and has genotype and phenotypeare that are easily changed due to environment changes. Cryopreservation is a storing method of germplasm in liquid nitrogen (-196 oC) which can maintain the genotype and phenotype of germplasm. The experiment aimed to obtain the best preculture, loading, and dehydration for cryopreservation of papaya ‘Sukma’ in vitro shoots. For preculture, we planted shoots on MS media with 0.3 M and 0.4 M sucrose for 1, 2, and 3 days. In the loading treatment, we immersed shoots in loading solution (liquid MS+1.2M glycerol+0.4M sucrose) for 0, 10, 20, and 30 minutes. For dehydration, we immersed shoots in cryoprotectant (PVS2 and its modification) for 5, 10, and 15 minutes. Then, shoots were immersed in liquid nitrogen. The results showed thatshoots had the best survival rate while they had been precultured on MS medium with 0.3 M sucrose for 3 days. The best loading treatment time was 20–30 minutes. The best dehydration treatment was obtained by modification of PVS2 for 10 minutes. The shoots have not been able to recovery after cryopreservation, so it can be concluded that cryopreservation of in vitro papaya ‘Sukma’ shoots has not been successful.

Digital Videos in Accounting Education: A Study on Perceived Use and Satisfaction in the Light of Connectivism

In the light of the connectivism theory, the objective was to analyze the perception of use and satisfaction of accounting students about Edpuzzle, a digital video application. This tool was employed in four accounting courses using the cell phones of 92 students. A questionnaire was administered on-site and was structured in two parts. The first contained questions about the respondent's profile, and the second about the use and satisfaction with Edpuzzle. The results indicated that students liked to use Edpuzzle and felt satisfied, especially those under 21 years old. We also noticed significant correlations among the questions. For example, the positive association between interactivity and appropriateness of use of the Edpuzzle stands out. It is concluded, therefore, that the results corroborate the connectivism theory, which sustains that knowledge must suffer updates as the environment changes and that sharing is crucial for the creation of networks that feed each other. Edpuzzle helps in this regard.

Map-less long-term localization in complex industrial environments

Purpose This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes. Design/methodology/approach The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose. Findings Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach. Practical implications This paper provides comprehensive experiments in real industrial environments. Originality/value The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.