Rail freight transport in Japan operates on a nationwide scale, and its disruption by natural disasters has widespread impacts on the logistics system. To restore transportation capacity at an early stage, rail freight operators collaborate with freight forwarders to implement alternative transport measures, where trucks play a crucial role. In this paper, we propose a planning method for alternative truck transport that supports the initial response to such disruptions. Specifically, we address the problem of determining truck departure times at freight stations and assigning containers to trucks under capacity and priority considerations. We formulate this problem as a mathematical programming model and develop a solution method that maximizes the number of containers transported while reflecting container priorities. Finally, through numerical experiments on several test instances, the effectiveness of the proposed method was confirmed, demonstrating its potential to support the development of alternative truck transport plans in the event of rail service disruptions.

The Cooperative Patent Classification (CPC) provides a standardised and reliable framework for organising patent information. As a structured taxonomy, CPC offers not only consistency for retrieval but also a basis for conducting technology trend analysis. This study develops a method for detecting and visualising technological shifts by leveraging the structural features of CPC. Compound words are extracted from patent claims and evaluated through two unsupervised measures: reconstruction errors calculated with AutoEncoder and anomaly scores estimated by an Isolation Forest. Based on these complementary measures, a four-quadrant model is developed to classify patents according to the degree and nature of technological change. Furthermore, a time-series analysis is conducted for CPC categories positioned in the quadrant with both high reconstruction errors and high anomaly scores, which represent areas of pronounced technological change. Many of the top-ranking words in 2020 show continuous growth since 2016, confirming that these terms are expanding within heterogeneous CPC areas identified through the model. The proposed method provides a systematic framework for identifying emerging technologies and heterogeneous technical elements, offering a robust basis for understanding technology evolution. It also demonstrates practical potential for supporting corporate R&D strategy, technology management, and forecasting of future technological priorities.

The aim of this study is to develop a device that can easily measure tongue movements simply by inserting it into an infant's oral cavity, along with an application that can assess sucking ability. We developed a device by placing force sensors on a silicone rubber sheet (DuraQ^®, manufactured by Sumitomo Bakelite Co., Ltd.) and attaching it to the little finger of a midwife to measure tongue movements in 20 infants. Additionally, using an application that displays the measurement results obtained from the device, midwives provided breastfeeding guidance and support services to mothers. As a result, mothers' confidence significantly increased after using the service. Furthermore, this increased confidence persisted for several weeks, suggesting that the device contributes to safe and secure breastfeeding.

In this paper, a synthesis method is proposed for locating the zeros of the closed-loop transfer function G_dd^~(s) from disturbance to its estimation error using a disturbance observer based on a minimal-order state observer. The basic idea involves distinguishing assignable and unassignable zeros by transforming the system matrix of G_dd^~(s) into a block lower-triangular form. The observer gain matrix is then designed to enable the placement of desired zeros and poles of G_dd^~(s). By applying the synthesis procedure for the observer gain matrix, G_dd^~(s) is made to satisfy the following design conditions: 1) The direct current (DC) gainG_dd^~(0) can be selected as a real value within the range [0, 1). 2) All poles of G_dd^~(s) can be located in the left half of the complex plane. The significance of the proposed approach is illustrated through its application to an angle control system of a DC motor.

This study presents a novel surgical training system utilizing the Apple Vision Pro, developed to address both the growing shortage of surgeons and the limitations of conventional surgical education. Traditional methods, relying primarily on textbooks and 2D videos, often fail to convey the three-dimensional anatomical transformations that occur during real procedures. To overcome this limitation, our system overlays text, images, 2D videos, 3D models, and stereoscopic 3D video footage captured in the operating room onto the user's field of view, aligned with each surgical step. This integration allows for a more intuitive and spatially accurate understanding of surgical workflows. The system was evaluated with both medical professionals and non-medical participants. Medical participants reported greater learning effectiveness compared to conventional methods, while non-medical users gave positive feedback on usability and visibility. These results suggest that the proposed AR-based system is a promising educational tool that could enhance surgical training and contribute to mitigating the decline in the surgical workforce.

This study proposes a method for determining optimal production conditions in manufacturing plants. The approach employs a simulator that estimates product quality from production parameters, thereby enabling the search for settings that meet specified quality targets. Reliability is enhanced by utilizing a Structured Neural Network as the surrogate model, which offers interpretable explanations for its predictions. In addition, penalties are applied when production settings deviate from normal operational ranges, guiding the optimization toward conditions that are more realistic for actual operations. Separate simulations carried out with synthetic data and with real production data both demonstrate the method's effectiveness, achieving the desired quality while producing conditions that closely align with real-world operation.

This paper presents a GPU-based acceleration method for tube diameter measurement in gastrointestinal medical images. An exhaustive image measurement algorithm is mapped onto the GPU thread architecture to enable massive parallel processing. Experimental results show that the proposed method reduces analysis time by approximately 99% compared with a CPU-based implementation.