This study aims to verify the effectiveness of a parallel learning approach for Python and R programming languages in data science education and to develop a practical learning environment using the Windows Subsystem for Linux (WSL) and Jupyter Notebook in a Bring Your Own Device (BYOD) setting. Additionally, it measures and analyzes the educational impact of this method on specialized courses in business and healthcare. The research methodology includes implementing a curriculum for simultaneous learning of both languages in the first-year "DS Programming Introduction" course, with effectiveness measured through language comprehension tests, problem-solving ability tests, and student language preference surveys. Furthermore, the study evaluates the efficacy of the BYOD environment using the WSL and Jupyter Notebook and optimizes programming education methods for specific fields. The findings of this study are expected to contribute to the development of highly skilled data science professionals by establishing new best practices in data science education.

In this study, with the aim of effectively providing education in mathematics, data science, and AI to university and technical college students and working adults, we analyzed the understanding of the basic concepts of “descriptive statistics” necessary for such education and the points where students have difficulties, using a basic skills test administered to liberal arts university students.
The results of the analysis showed that the students had a good level of understanding of questions that required them to find the mean, median and mode. However, the correct response rate for questions about variance and quartiles was low, and there were some students whose definitions were unclear.
Therefore, I think that education on descriptive statistics should include guidance on the meaning of variance and quartiles.

In a tourist destination, the government which expects the increase of tourists might need to develop abilities to accommodate them. Considering local economy where upper-midscale hotels whose capacities are small relative to a market compete in prices for acquiring customers in an oligopolistic market, we investigate tourism policy such that the government would attract a new hotel. The government would like to make a “feasible” policy in the sense that the attraction never leave the customers or the industry worse off. Focusing on the market where hotels including an entrant are symmetric, we show that there exists a legitimate range of hotels' capacities within which the attraction is feasible. We also explicitly find the range of hotels' capacities. Our result suggests that the government does not necessarily attract a high-class hotel such as a five star hotel (an upscale or luxury hotel) for a positive impact of the attraction. Instead, the government might still enjoy the positive impact by introducing an entrant which is similar to incumbents in terms of capacity size and brand equity without incurring vast amount costs of the attraction of a larger hotel or a high-class hotel.

Aim: This study explores mothers' perceptions of grief support groups following child loss and provide implications for future support. Methods: Mothers attending grief support meetings since 2017 completed online surveys after each meeting from 2020. Survey completion indicated consent. Content analysis identified from the 6th to 13th meetings to identify codes and categories from open-ended comments. Results: Thirty-seven (64.9%) out of 58 participants responded. Eight categories emerged from 106 codes: "space to talk about the deceased child," "listening and being listened to," "empathy and shared experiences," "expressing suppressed emotions," "realizing they are not alone," "emotional relief," "a place to remember the deceased child," and "facing grief." Conclusions: Mothers reported benefits from support groups, while persistent nature of grief emphasizes the need for ongoing, individualized support.

All programs that run our world are written by humans in high-level programming languages such as Python, Java, and C, which are then compiled into low-level code and executed. Much of the technology for compiling for modern programming languages, i.e., compilers, is due to the contributions of Alfred V. Aho and Jeffrey D. Ullman, which earned them the Turing Prize, the pinnacle of computer science, in FY2020 [1]. Techniques and algorithms for lexical analysis, parsing, and code generation are important for compilers. This article describes the structure of compilers and the most important and most difficult part of compilers, parsing, especially LR parsing [7] , by clarifying the relationship between parsing methods and adding new effective examples not found in other books. This will deepen your understanding of computer software that supports present and future society. More importantly, the theory, techniques, and implementation of parsing are the principles of many other software, and will be of great benefit in the development of future software. This article is based on Dragon Book [2] and excellent textbooks [3,4,5,6].

Rough set methods are often used to reduce decision rules. Specific techniques using rough sets are used as a method for extracting decision rules. However, when dealing with many decision rules, the computational load becomes an issue. The problem of calculating all minimum-length decision rules is a NP-hard problem with combinatorial explosion. To address this computational challenge, this article describes a method to introduce biocomputing technology. This method applies DNA molecular technology to the reduction of decision rules, and can effectively reduce the computational complexity of the problem. Since L.M. Adleman pioneered the concept of the biological computing paradigm in 1998, this technology has provided the ability to develop new problem-solving algorithms by utilizing and implementing them in existing algorithms. However, algorithms using interdisciplinary DNA molecular technology for industrial engineering decision-making problems are still limited to areas where DNA is used in a limited way, and it cannot be said that they are widely used as a computational technology. This article describes the mechanisms and techniques of molecular engineering that manipulate DNA molecular structures and properties, and introduces the use of general molecular algorithms. In particular, we describe an algorithm we developed to minimize decision rules for minimum rule searches of rough sets.

South Korea ranks seventh globally in container cargo handling, whereas Japan does not hold a comparable position among leading countries. This study aims to analyze the status of global maritime container cargo, examine the relationship between maritime container ports in Japan and South Korea, and provide insights into Japan's current situation. Although the global logistics metric is primarily based on container cargo volume (TEU), Japan’s unique industrial characteristics result in significant imports of coal, oil, iron ore, and grain—commodities unsuited to container transport—and exports of passenger vehicles. Consequently, Japan's contribution to global competitiveness in this area is limited. Meanwhile, global port development trends focus on accommodating larger vessels and enhancing container cargo transport technologies. However, Japanese ports face limitations, as the world’s largest container ships cannot dock due to depth constraints, making it difficult to attract primary-route vessels. Furthermore, Japan’s stagnant economy necessitates prioritizing feeder services.