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In response to the increasing demand for unrestricted access to diverse services regardless of location, cost-effective and easily deployable Wireless Mesh Network (WMN) solutions have once again captured attention. This paper primarily addresses the implementation challenges of Autonomous Load-balancing Field-based Anycast routing+ (ALFA+) for three-dimensional (3D) WMNs. Subsequently, we evaluate the performance of ALFA+ in an 802.11-based 3D WMN testbed established within a university campus using commercial devices, thus validating the practical viability of ALFA+. While most prior research has relied on performance evaluation through virtual environment simulations, this study distinguishes itself by performance evaluations in a real-world testbed using commercial devices and providing detailed implementation-related information necessary for such evaluations. This approach holds considerable significance in assessing the actual applicability of ALFA+.

The development of artificial intelligence technology changes the social structure and educational environment, and the importance of artificial intelligence capabilities continues to increase. This study was conducted with the purpose of developing a checklist of questions to measure AI capabilities of elementary school students. To achieve the purpose of the study, a Delphi survey was used to analyze literature and develop questions. For literature analysis, two domestic studies, five international studies, and the Ministry of Education's curriculum report were collected through a search. The collected data was analyzed to construct core competency measurement elements. The core competency measurement elements consisted of understanding artificial intelligence (6 elements), artificial intelligence thinking (4 elements), artificial intelligence ethics (4 elements), and artificial intelligence social-emotion (3 elements). Considering the knowledge, skills, and attitudes of the constructed measurement elements, 19 questions were developed. The developed questions were verified through the first Delphi survey, and 7 questions were revised according to the revision opinions. The validity of 19 questions was verified through the second Delphi survey. The checklist items developed in this study are measured by teacher evaluation based on performance and behavioral observations rather than a self-report questionnaire. This has the implication that the measurement results of competency are raised to a reliable level.

Recently, reliability has become more important as flash-based storage devices are actively used in cloud servers and data centers. Flash memory chips have limitations in reading/writing, so if writing is concentrated in one location, the chip can no longer be used. To solve this problem and improve reliability, it is necessary to equalize the wear of flash memory chips. However, in order to equalize the wear of flash memory with increasing capacity, the workload increases proportionally. In particular, when searching for a block with the maximum/minimum number of deletions for all blocks of a flash memory chip, the cost increases depending on the capacity of the storage device. In this paper, a random selection method of blocks was applied to solve the previous problem. When k is the randomly selected block, actual experimental results confirmed that searching all blocks with an k value of 4 or more yields similar results.