<p>The genetic algorithm (GA) is an adaptive metaheuristic search method based on the process of evolution and natural selection theory. It is an efficient algorithm used for solving the combinatorial optimization problems, e.g., travel salesman problem (TSP), linear ordering problem (LOP), and job-shop scheduling problem (JSP). The simple GA applied takes a long time to reach the optimal solution, the configuration of the GA parameters is vital for a successful GA search and convergence to optimal solutions, it includes population size, crossover operator, and mutation operator rates. Also, very recently, many research papers involved the GA in coding theory, In particular, in the decoding linear block codes case, which has heavily contributed to reducing the complexity, and guaranting the convergence of searching in fewer iterations. In this paper, an efficient method based on the genetic algorithm is proposed, and it is used for computing the Automorphisms groups of low density parity check (LDPC) codes, the results of the aforementioned method show a significant efficiency in finding an important set of Automorphisms set of LDPC codes.</p>
The nursing student walks through a complex study system in a field full of challenges. He is called upon to embark on a three-year training program that should foster a better link between theory and practice and be part of a professionalization approach, thus ensuring the development of his autonomy. In such a context, taking a step back and questioning the practice on the part of each student remains an area rarely explored. Following the implementation of the competency-based approach, the development of reflective practice (RP) in students and the need to stimulate critical thinking skills in them are a priority. From this perspective, this study aims to study the development of RP among 200 students from 21 ISPITS (Higher Institute of Nursing Professions and Health Technologies) from different corners of the Kingdom of Morocco. It is done using a questionnaire, the internal validity of which has been approved, sent through the Google Forms platform. As such, this quantitative study will focus on two aspects on the first is descriptive exploratory to study the level of development of RP in ISPITS. The second aims to study the relationship between RP and the development of autonomy among nursing students and to verify the existence of a possible impact on their academic success. The results show a positive impact on the importance given to RP in ISPITS training. Also, the statistical tests demonstrated a positive impact of the RP on the development of autonomy and on the students' success.
Many research papers in coding theory have recently focused on designing high-rate codes or improving codes that exist through a better understanding and then improving the coding and decoding algorithm. As a result, this paper aims to investigate the computation of the Automorphisms groups of some optimal codes (e.g., some linear circulant codes where their distance meets the lower bound and nonlinear Nordstrom-Robinson (24, 28, 6) code). These Automorphisms groups provide information about the structure of the code, which aids in both the design and enhancement and improvement of decoding algorithms. A new genetic algorithm-based method is proposed, with a detailed description of its components, the fitness function, selection, crossover, and mutation, and is used to find an important collection of Automorphisms; the results obtained have shown that the proposed method is effective in finding stabilizers set for some types of codes.
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