The Boolean network (BN) is a simple and tractable dynamic system model with its logical state variables, taking their values in 0-1 (or equivalent true and false values), and state equation comprising logical operations. In this paper, we review modeling techniques based on the semi-tensor product (STP) of matrices in BNs and resulting state equations, which are friendly to researchers of system control. Furthermore, we illustrate optimization algorithms for solving optimal control and related Kullback-Leibler control problems.

In the field of study related to the analysis and control of spatial sound, or spatial audio processing, the sound field, i.e., the distribution of a physical quantity of sound such as sound pressure, is called sound field estimation. Although applications related to spatial audio processing range widely, many of them are based on sound field estimation and its accuracy plays an important role in the performance of subsequent processing stages. In a simple scenario, the sound field estimation problem can be formulated as a function interpolation in machine learning. However, applying general function interpolation techniques that solely depend on data does not yield high estimation accuracy. The physical properties of the sound field are useful prior information, and effectively incorporating them into the estimation is considered important. In this article, we explain the fundamentals of physics-informed machine learning (PIML) for sound field estimation or spatial audio processing and provide an overview of current PIML-based sound field estimation methods.

The scientific satellites and spacecraft exposed to harsh space environments cannot be maintained after they are launched. Therefore, the onboard electronic components must be of sufficient quality to be free of initial defects and reliable enough to withstand external conditions such as temperature extremes, radiation, and vacuum. Although, the use of space-qualified parts ensures high quality and reliability, advanced scientific instruments sometimes cannot meet their functional requirements using only such parts. For this reason, we appropriately combine space-qualified parts with industrial-grade parts that have undergone additional screening and evaluation. In this paper, we outline guidelines and criteria for part selection, explain the differences in quality assurance between space-qualified and industrial-grade parts, and discuss evaluation methods and countermeasures to address reliability requirements specific to space applications. Furthermore, trends and challenges in the utilization of parts for recently developed small satellites are also presented.

The rapid development of social media has created a strong interconnection between online user activities and real-world phenomena. Events such as online firestorms and viral trends represent typical cases in which the activity level of online user dynamics suddenly intensifies, potentially influencing corporate behavior, public perception, and even economic trends. In this study, we formulate the mutual influences arising among users in online spaces as a fundamental theoretical framework based on the principles of locality and causality, and demonstrate that the phenomena theoretically predicted using this framework can also be observed in real online data. Furthermore, as a social implementation of this framework, we present an application to the FinTech domain, in which early detection of corporate-related news is utilized to support investment decisions in stock markets. Specifically, investment simulations based on the proposed early-warning detection method reveal that predictive indicators derived from the theoretical framework enable high-performance investment decisions. In addition, this technology is expected to be applicable to various other domains, including trend forecasting, crime prevention, and national security, suggesting promising prospects for developing a social risk prediction technology grounded in online user dynamics.

Drones have been used for various purposes in recent years and are even useful for assessing disaster damage and ensuring communications during disasters. They are also expected to be used in smart cities, which will be realized in the future. However, when large-scale disasters occur, public communication networks may fail. Therefore, there is growing interest in the use of wireless ad hoc networks that are composed only of communication terminals mounted on drones and do not rely on existing infrastructure. In this paper, we describe the research and development of wireless communication systems used by drones and also describe an example of research and development we conducted on routing control protocols for applications that will contribute to safety and security in smart cities.

Double selectivity consists of frequency selectivity due to delay spread and time selectivity due to Doppler spread. For radiofrequency wireless communications, the spread factor, which is the product of delay spread and Doppler spread, is generally less than 0.1%. In this paper, we assume that severe double selectivity has a spread factor of more than 0.1%. In this paper, we discuss severe doubly-selective channels and wireless communications in the presence of severe double selectivity, e.g., underwater acoustic communications and point-to-multipoint narrow-band communications employing a single-frequency network.

As social media has become increasingly expansive, content moderation has become a key issue in balancing platform governance and freedom of expression. Taking Japanese law and guidelines issued by the Ministry of Internal Affairs and Communications as a starting point, in this paper, aimed at engineers, practical and normative issues of content moderation have through perspectives such as responses to unlawful content, the secrecy of communications, the quasi-public nature of large platforms, transparency, and tolerance thresholds. Then, an analytical framework that distinguishes between expressions that may be permitted and those that ought to be protected from restriction is introduced and illustrated with concrete cases. Treating machine-learning-based automated moderation as a delegation of decision-making authority, we further argue for the importance of governance arrangements that include explainability and meaningful human involvement.

The rapid development of information technology has brought great benefits to society, but it has also created challenges such as algorithmic bias and limited accessibility. While quantitative research is effective in identifying large-scale data patterns, it often fails to capture the complex experiences and voices of minorities. This paper introduces duoethnography as a new qualitative approach to address these challenges. By engaging researchers in reflective dialogue and treating differences as resources for new understanding, duoethnography renders visible the social assumptions and power structures embedded in technological systems, and invites audiences to engage with these issues in depth through approachable, dialogic language. Through this lens, the paper demonstrates that duoethnography can serve as an effective framework for developing information technologies that are socially responsible and ethically informed, thereby contributing to the creation of a more inclusive society.

In the Department of Science of Design at Musashino Art University, the author has conducted research for two decades on perceiving the world from innovative perspectives. This research begins with open-minded observation of human-created environments, including the objects and phenomena shaping the human world. Design education extends beyond techniques, focusing on engagement with the environment and understanding world structures and perception. This article presents selected outcomes from collaborative projects developed through student seminars and learning.