Recently, data centers have faced a major challenge due to the rapid growth of data volume. In particular, high-speed signal processing is essential in network switches, underscoring the critical need to develop substrate materials suitable for next-generation high-frequency applications. Although it has long been recognized that glass cloth within substrates can introduce additional transmission losses, a further issue has recently been identified: a localized transmission loss (dip) and its higher-order dips. In this paper, we demonstrate that this phenomenon also occurs in the latest high-frequency substrates. Through theoretical calculations, electromagnetic field simulations, and experimental measurements, we clarify the mechanism responsible for dip formation, propose methods for its suppression, and suggest strategies to prevent the phenomenon entirely.

To improve the quality of life for elderly people living in care facilities, care facility managers conduct various activities, including interactive seminars led by instructors. This study presents a system for evaluating the satisfaction level of elderly seminar participants using a full 3D and multi-input multi-output (MIMO) fast chirp modulation-based frequency-modulated continuous wave (FCM-FMCW) radar. Using this system, the radar can detect the range, 3D positioning, velocity, angle, and size of objects. Experiments were conducted in two scenarios: raising both hands and singing a song, with three levels of satisfaction: satisfied, half-satisfied, and dissatisfied. The method used to evaluate the satisfaction level was based on the spreading of point clouds and time series point clouds. The experimental results showed that this method can be used to evaluate the satisfaction level of elderly seminar participants.

This paper proposes an energy efficient radio frequency identification (RFID) reader with sensing capabilities. The RFID reader comprises an IMPINJ RFID reader and a vibration sensor. This vibration sensor can detect physical vibrations and activate the IMPINJ RFID reader. Before vibration occurs, the RFID reader remains in sleep mode; there is no radio wave emission or reading function. Once the vibration sensor detects a shock/vibration, the RFID reader switches to active mode and begins reading operation. The general-purpose input/output ports (GPIOs) of the RFID reader enable these functions. The falling edge signal from the vibration sensor, transmitted via the GPIOs, triggers the reading function of the RFID reader. The active mode of the vibration-sensitive reader is initiated only when vibration occurs. The experimental results show that the proposed RFID reader can monitor vibrations caused by earthquakes and human activities. Traditional RFID sensing systems require the reader to emit continuous electromagnetic waves to obtain information from RFID tags. These continuous reading operations generate a large amount of useless data, which places a heavy burden on sensor data analysis and storage. In contrast, the proposed system can effectively reduce the reading of useless data, thereby decreasing the burden on data storage and analysis.

The hot strip mill is a typical large-scale plant consisting of numerous units, where equipment failures may lead to significant downtime and product quality deterioration. To support stable operation, monitoring and diagnostic technologies with high interpretability are increasingly required. This paper proposes a monitoring and diagnostic approach that emphasizes explainability by introducing model-based monitoring. The proposed method focuses on specific abnormal phenomena that are difficult to interpret using conventional statistical approaches. For rolls and motors, frequency-domain analysis using Fast Fourier Transform (FFT) monitors harmonics based on the fundamental rotational frequency to detect abnormal vibration patterns. For actuators such as the hydraulic cylinder, an Auto-Regressive with eXogenous input (ARX) model is employed to identify time constants and damping ratios, enabling precise monitoring of response deterioration. Application results from an actual hot strip mill demonstrate that the proposed approach identifies abnormal phenomena and localizes their occurrence. The results confirm the effectiveness of model-based monitoring in improving the interpretability of diagnostic results and supporting practical operation in large-scale plants.

In this paper, we propose a wire-driven 7-DOF haptic device for gripping operation using palm. The aim is to realize a total of 7-DOF force display by using a total of 8 wires, not only for 6-DOF force display of translation and rotation of the end effector, but also for gripping operation using the palm. The proposed haptic device was developed. As an evaluation experiment of the proposed system, rigid body operations by using palm were measured in VR world. As a result, it was confirmed that the device generates torque for the palm and the manipulating force simultaneously. It is possible to display high tracking and force ability to the VR object.

This study investigates the media characteristics and effectiveness of virtual reality (VR)-based regional promotion using a metaverse environment. A virtual world utilizing VR technology was developed to promote tangible cultural properties in the Hatsukaichi area of Hiroshima Prefecture, Japan, as part of the Hatsukaichi Regional Issue-Solving DX Demonstration Experiment. The metaverse world digitally reconstructed the main building of the Yasui Residence and incorporated elements from the Miyajima Craft Museum. To examine the communicative potential of VR as a video medium and as an immersive experience, two experiments were conducted: (1) a before-and-after evaluation of participants' interest after watching a VR video, and (2) a comparative evaluation of change scores (post - pre interest ratings) between VR video viewing and HMD-based exploration. The results showed that the VR video significantly increased young participants' interest in regional revitalization, traditional crafts, and architectural heritage, and that HMD-based experiences further strengthened this interest in a stepwise manner. These findings indicate that VR can effectively function both as a viewable and an experiential medium, complementing conventional video-based promotion while enhancing engagement and immersion in regional resource communication.

Morale issues are considered a major factor hindering organizational change. Employee morale during such change is known to depend upon trust in management. Our previous study proposed a procedure for estimating employees' trust in management and developed a multi-agent simulation model to demonstrate changes in employee states. This study modifies the model to estimate trust using fewer independent variables while improving prediction accuracy, thereby simplifying the multi-agent simulation model. Simulation experiments are presented to analyze the relationship between employees' trust and changes in morale.

In manufacturing plants, Industrial Engineering (IE) methods have been applied to reduce workers' burden and improve productivity by addressing overburden, waste, and unevenness. Representative approaches include the OWAS method, which evaluates physical load by observing workers' postures, Sabrick analysis, which records each task using symbols and diagrams, and the stopwatch method, which measures task duration. Although video recording devices allow observation without being on site, the identification of overburden, waste, and unevenness still requires detailed analysis of recorded data, demanding considerable effort. To simplify this process, various motion analysis techniques using specialized equipment have been studied; however, their high-cost limits practical adoption. To address this issue, this study proposes a low-cost and user-friendly motion analysis method using mocopi^®, a mobile motion capture system. We developed a system that captures workers' movements, visualizes them in Unity, and outputs coordinate data. Furthermore, by applying for a Support Vector Machine (SVM), we constructed a posture classification model that successfully distinguished four actions: standing, squatting, hand movements, and forward bending. Future work will focus on enabling real-time classification.

This study aims to develop an e-learning system for summary essay tasks. In summary problems, a wide variety of answers can be regarded as correct; therefore, the scoring function must be capable of multi-perspective evaluation. The proposed system seeks to realize an automatic scoring function that enables multi-perspective evaluation by utilizing generative AI. To achieve multi-perspective evaluation, we conducted experiments using multiple generative AI models and found that their evaluation policies differed depending on the model. In the learning process, it is assumed that learners will repeatedly receive scoring results and feedback comments; however, inconsistent evaluation policies may cause confusion among learners. To address this issue, we propose a method in which multiple generative AI models first generate evaluation criteria, and then the scoring is performed according to these criteria. By selecting the evaluation criteria that align most closely with their own perspectives, the system enables multi-perspective evaluation while maintaining consistency in feedback for learning improvement.

To address pressing environmental challenges such as global warming and the increasing consumption of natural resources, a transition toward a circular economy is imperative. Scenario-based simulations of transition pathways to a circular economy offer valuable insights that can inform societal decision-making and guide future actions. However, the development of such simulation models often relies heavily on individual expertise, raising concerns about subjectivity and reproducibility. This paper proposes a methodological framework aimed at enhancing both the efficiency and quality of simulation model development for circular economy transitions.

This paper addresses the challenge of efficiently operating unstructured data stored in object storage using table formats. Table formats enable data analysts to query objects within object storage buckets like a database, which facilitates efficient data retrieval in data science workloads. Conventional table formats require converting unstructured data into structured ones such as Parquet or ORC formats and storing them in separate buckets. Conventional data conversion requires extra time and effort, and separate bucket storage leads to duplicating data in data management manner. To overcome this problem, we propose a new table format that introduces virtual schema structure to unstructured data without structured transformation by leveraging object metadata. Our method allows a bucket containing unstructured data to be directly treated as a table and makes it possible to eliminate conversion overhead and simplify data management. We evaluate query execution time using TPCx-BB benchmark data. The results show that the proposed table format achieves query performance equal to or better than that of conventional ones.

Recent regulatory reforms in Japan have mandated the inclusion of sustainability-related information in annual reports, offering institutional investors standardized access to its information. However, the absence of evaluation criteria makes it difficult to assess the usefulness of such information for ESG investment decision-making. This study proposes a method that combines generative AI and factor model to identify and evaluate valuable sustainability disclosures. The approach involves extracting distinctive sustainability-related information from companies with relatively strong stock performance using natural language processing techniques. These contents are then scored by generative AI based on specificity, strategic relevance, and measurability. The scores are incorporated into a factor model to examine their relationship with stock returns. Empirical results show that disclosures related to employee well-being have a statistically significant positive association with stock returns.

As more and more foreign tourists visit Japan, it is increasingly important to communicate with them in a disaster. However, language barriers are a problem in communication in it. Therefore, we developed a non-linguistic evacuation support system without language barriers. This system is characterized by its ability to indicate evacuation routes using only non-linguistic information such as pictograms, Arabic numerals and images. Furthermore, we conducted field tests using this prototype system, there is a possibility that this prototype system can provide proper evacuation routes.