review article

　Expanding the physical scope of regional tourist destinations is a cornerstone of economic revitalization; however, enhancing "visitor mobility" frequently precipitates friction with local residents. This Academic Project undertakes a theoretical analysis by organizing existing literature, centering on the case of the Kanmon Region (Shimonoseki and Kitakyushu). 　First, it traces the conceptual evolution of "visitor mobility," highlighting how commercialism has been uncritically integrated into public policy. Second, utilizing Social Exchange Theory and Doxey’s Irridex model, this paper analyzes the structural transformation of resident attitudes toward "antagonism" driven by area expansion. Third, it discusses the current reality where social media neutralizes traditional tourist flow management, thereby accelerating the encroachment of tourism into the private livelihood spheres of residents. 　Building upon these findings, the paper critically reconstructs the consumption promotion model to propose a "Livelihood-Coexistence Visitor Mobility Model," which incorporates resident life satisfaction as a primary prerequisite. Furthermore, it operationalizes a "friction-predicated consensus-building process" that redefines interest conflicts as strategic resources for reaching an agreement. This paper establishes the theoretical foundation for the subsequent empirical research in the Kanmon Region to be presented in a forthcoming paper.

　This paper is a Japanese tutorial article that summarizes our replication of the Forward-Forward (FF) algorithm on MNIST in the supervised label-in-input setting. We verify the correspondence between the implementation and the mathematical formulation and organize our hyperparameter exploration in a hypothesisintervention-result-interpretation-next-step format, providing tables and figures aggregated across runs from the experiment logs. In our replication, the best test error under label-search inference was 2.12% with jitter augmentation (± 2 pixels) after 500 epochs.

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.

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].