Ashikaga University

This study aims to evaluate a driver's usual driving situation using driving footage, such as that from a dashcam. The dashcam provides a first-person image depicting the front view of the driver's seat. However, to evaluate your own driving, a thirdperson perspective taken from behind the vehicle can give you a more accurate picture of your car's movements. This requires the cooperation of the car driving behind, which is impossible with a car alone. To resolve this, pix2pix, a generative adversarial network (GAN) model, was used to generate third-person viewpoint images from first-person images captured using a dashcam. For the evaluation, we used third-person perspective images generated using the model and first-person and thirdperson perspective images created using a simulator. The experiments using footage captured in an actual dashcam was also conducted.

This paper proposes a gaze evaluation and training system for beginner drivers, implemented using Unity and You Only Look Once (YOLO) v8x for real-time object detection. Objects that could not be recognized by YOLOv8x were annotated manually. The system automatically identifies regions to be observed as must-see areas and regions that should be avoided as must-not-see areas. The must-see areas are marked with green bounding boxes, whereas the must-not-see areas are marked with red bounding boxes. Two experiments were conducted to assess YOLO accuracy and score comparison (manual counting) respectively. The manual score comparison was then updated to automatic scoring system which helps to calculate the automatic score when the subject looks at the system under the surveillance of an eye tracking sensor. Experiments with beginner and experienced drivers revealed significant differences in gaze patterns, with experienced drivers exhibiting wider gaze movements. In contrast, beginner drivers normally focusing straight on the road, showing insufficient hazard checks while driving. Finally, comparative score evaluation with two different approaches were performed with beginner and experienced drivers. The findings of this paper support the development of training mechanisms to improve driving skill in beginners and to enhance safe driving.

B is considered a valuable additive for TiAl alloys, because it is believed to improve their properties by refining their microstructures. However, the effects of B on the practical properties of TiAl alloys for jet engine blades and the optimal addition amount for achieving balanced properties remain unclear. Specifically, there have been very few studies to date in which the practical properties of alloys have been evaluated across a wide range of B addition levels. Therefore, we evaluated various reliability, cost, and performance properties of jet engine blade materials using cast Ti-45,47Al-2Nb-2Mn (the same as XD alloys), with varying B addition levels. The results showed that, in some cases, low B addition levels (0.1–0.2 at.%) could enhance the impact resistance and high-cycle fatigue performance. However, even low B addition levels negatively impacted the machinability, castability, and creep strength. Further, adding 0.4 B or more significantly reduced most practical properties. Compared to XD alloys, TiAl4822 exhibited a superior balance, which is attributed to the higher B content (1 at.%) in XD alloys and the greater effectiveness of Cr relative to Mn in improving the alloy’s high-temperature impact resistance.

This study examines the feasibility of biomass briquettes as a renewable energy source, utilizing Giant Miscanthus, sawdust, and spent coffee grounds. Recycled paper pulp serves as a binder, enhancing energy density and combustion efficiency. The briquetting process involves drying, grinding, mixing, and compressing the raw materials. Fuel characteristics were assessed through proximate and ultimate analyses, calorimetry, and thermogravimetric analysis (TG/DTA). Spent coffee grounds exhibited the highest heating value (21,370 kJ/kg), followed by sawdust (17,610 kJ/kg) and Giant Miscanthus (17,020 kJ/kg). Thermal decomposition confirmed efficient combustion, with Giant Miscanthus achieving complete combustion at 484°C and an exothermic peak at 452°C. Giant Miscanthus emerged as a promising feedstock due to its low ash content, high energy yield, and compatibility with existing infrastructure. Combining agricultural residues with non-arable crops enhances resource efficiency. This study highlights the potential of biomass briquettes to support decarbonization, energy security, and sustainable development goals by providing a viable low-carbon alternative to fossil fuels.

Abstract The hardening of K0.5Na0.5NbO3 (KNN) ceramics with the addition of Ag2O and CuO and the crystal-orientation behavior of KNN and CuO-added KNN powders under a high magnetic field were investigated. Ag2O reduced the degree of compositional deviation of KNN ceramics and increased their density. Additionally, the piezoelectric characteristics were improved by the addition of Ag2O. CuO functioned as a sintering aid for KNN, and the CuO-added KNN ceramics increased the grain size and mechanical quality factor by up to approximately 1200. The simultaneous addition of Ag2O (0.01 mol) and CuO (0.01 mol) to KNN resulted in hardening with a mechanical quality factor of 1400 owing to the synergistic contributions of each additive. Under a high magnetic field of 5 T, the KNN powder and CuO (0.01 mol)-added KNN powder exhibited b- and c-axes orientation parallel to the magnetic field, which is expected to enhance the piezoelectric properties through crystal orientation.

Although the rapid development of Artificial Intelligence (AI) in recent years has brought increasing academic attention to the intelligent transformation of physical education, the core knowledge structure of this field, such as its primary research topics, has yet to be systematically explored. The LDA (latent Dirichlet allocation) topic model can identify latent themes in large-scale textual data, helping researchers extract key research directions and development trends from extensive literature. This study is based on data from the Web of Science Core Collection and employs a systematic literature screening process, utilizing the LDA topic model for in-depth analysis of relevant literature to reveal the current status and trends of AI technology in physical education. The findings indicate that AI applications in this field primarily focus on three areas: “AI and data-driven optimization of physical education and training”, “computer vision and AI-based movement behavior recognition and training optimization”, and “AI and virtual technology-driven innovation and assessment in physical education”. An in-depth analysis of existing research shows that the intelligentization of physical education, particularly in school and athletic training contexts, not only promotes sustainable development in the field but also significantly enhances teaching quality and safety, allowing educators to utilize data more precisely to optimize teaching strategies. However, current research remains relatively broad and lacks more precise and robust data support. Therefore, this study critically examines the limitations of current research in the field and proposes key research directions for further advancing the intelligent transformation of physical education, providing a solid theoretical framework and guidance for future research.

We introduce (co)homology theory for multiple group racks and construct cocycle invariants of compact oriented surfaces in the 3-sphere using their 2-cocycles, where a multiple group rack is a rack consisting of a disjoint union of groups. We provide a method to find new rack cocycles and multiple group rack cocycles from existing rack cocycles and quandle cocycles. Evaluating cocycle invariants, we determine several symmetry types, caused by chirality and invertibility, of compact oriented surfaces in the 3-sphere.

Abstract In protoplanetary disks around young stars, magnetic fields play an important role for disk evolution and planet formation [1-3]. Polarized thermal emission from magnetically aligned grains is one of the reliable methods to trace magnetic fields [4,5]. However, it has been difficult to observe magnetic field by dust polarization in protoplanetary disks because other polarization mechanisms involving grown dust grains become efficient [6,7,8]. Here, we report muti-wavelengths (0.87 mm, 1.3 mm, 2.1 mm, and 2.7 mm) observations of polarized thermal emission in the protoplanetary disk around HD 142527 that shows the lopsided dust distribution [9,10,11,12]. We revealed that the smaller dust still exhibits magnetic alignment in the southern part of the disk. Furthermore, angular offsets between the observed magnetic field and the disk azimuthal direction were discovered, which can be used for a novel approach to measure the relative strengths of each component (radial (Br), azimuthal (B∅), and vertical (Bz)) of 3D magnetic field. Applying this method, we, for the first time, derived the magnetic field around a 200-au radius from the protostar as |Br|:|B∅ |:|Bz| ~ 0.26±0.01:1:0.23±0.03 and a strength of ~ 0.3 milli Gauss. Our observations provide some key parameters of magnetic activities including the plasma beta, which have been only assumed in theoretical studies. In addition, the radial and vertical angular momentum transfer show comparable, which poses a challenge to theoretical studies of protoplanetary disks.