Nippon Institute of Technology

We have developed a simple method for generation of densely trifluoromethylated alkyl radicals, including the nonafluoro-tert-butyl radical. Readily available fluorinated alkyl benzoates derived from fluorinated alkyl alcohols act as radical precursors under dual photoredox and acid catalysis, enabling selective fluoroalkylation of various aromatic alkenes and indoles.

In this work, an attempt was made to optimize the process of synthesizing diamond powders without metal catalysts at high pressures and temperatures and to determine the features of diamond crystallization in C–O–H fluid. For this purpose, a comparative study of diamond powders synthesized at a pressure of 7.5 GPa from a mixture of carbon black (55wt%) with pentaerythritol (45wt%) and the same mixture modified with detonation nanodiamonds (DNDs) (5wt%) was carried out. The addition of DND leads to the formation of aggregates with a size of ∼10 μm, consisting of high quality diamond crystals with a size of ∼0.5–3 µm, and a decrease in the synthesis temperature from ∼1600 to ∼1500 °C. The addition of DNDs into the reaction medium has no effect on the concentration of N0, N2 and NV– centers in the synthesized diamonds, while the content of N3VH0 increases.

Abstract Ferrous slag is the most abundant solid by-product generated from the steel manufacturing process, and a stable demand for ferrous slag is essential to ensure the stable production of steel. Therefore, various applications have been developed to maintain a stable demand for ferrous slag. Furthermore, continuous fundamental study and technological development are essential to find more value-added valorization and explore more advanced ways of use. It is inevitable to develop ideal slag valorization methods corresponding to new types of ferrous slag with a society-wide perspective to ensure the rapid decarbonization trend, which is an unprecedented experience in the steel industry. This article provides an overview of the progress and technological structure of ferrous slag valorization in Japan so far and also summarizes the research activities related to ferrous slag in recent years in the Iron and Steel Institute of Japan, which plays a central role in this field. Particularly, unique activities such as exploring environmentally friendly valorization methods using ferrous slag through collaboration between different industries such as agriculture and fisheries, and carbon fixation through vegetation promotion in coastal areas in Japan, harvesting blue carbon, are summarized. Based on the above, the future role of ferrous slag in establishing a sustainable society has been discussed. Graphical Abstract

Musical instrument classification in the symbolic domain is a challenging task due to the inherent differences between MIDI representations and their corresponding acoustic signals. In this study, we propose a contrastive learning-based framework, Contrastive Audio-MIDI Learning (CAMIL), to improve MIDI-domain instrument classification by leveraging both symbolic and audio information. In our approach, we use MIDI embeddings as anchors, pairing them with their corresponding audio embeddings as positive samples and audio embeddings from different instruments as negative samples. By optimizing a contrastive loss function, our model learns to align MIDI embeddings with their corresponding audio representations while pushing apart embeddings of different instruments. We evaluate our method on the Lakh MIDI dataset and demonstrate that it improves instrument classification performance in the symbolic domain. Our results highlight the potential of contrastive learning in bridging the gap between audio and MIDI representations for more robust musical instrument recognition.

In recent years, the development of convolutional neural networks (CNNs) has led to their widespread application in various fields such as the Internet of things (IoT) and image recognition. The learning performance of NNs is highly dependent on the optimizer of the loss function, so selecting an appropriate optimizer is crucial to improve the speed and accuracy of learning. Currently, the main optimizer is adaptive moment estimation (Adam), which is based on the gradient method. Adam is relatively computationally intensive because it requires gradient calculations. To solve this problem, this study examines the application of particle swarm optimization (PSO), one of the swarm intelligence optimization methods, as an optimizer for CNNs.

In wireless power transfer (WPT) for multiple energy receivers (ERs), fair scheduling is crucial as well as improving the power transfer efficiency. In time division (TD) scheduling, the energy transmitter (ET) periodically switches beamforming weight vectors of multiple antennas for receivers by assigning a time slot to each weight vector. In this study, we propose a battery-aware TD scheduling method based on the channel states of ERs. The proposed method aims to efficiently transmit radio signals to stabilize the battery levels of ERs. The ERs estimate the channel vectors and notify them to the ET with the remaining battery levels. From the channel vectors and the battery levels, the TD scheduling to stabilize the battery levels is formulated as a mixed-integer optimization problem. By solving the problem, the ERs are classified into active and inactive, and the optimized time slots are assigned to active ERs. In an indoor environment, we evaluate the performance of the proposed method with simulation experiments, and show that the proposed method can stabilize the battery levels of ERs with efficient signal transmission.

This paper proposes a cluster-based flight path construction method for automated drone-assisted pear pollination systems in orchard environments. The approach uses RGB-D (Red-Green-Blue-Depth) sensing through an observation drone equipped with RGB and depth cameras to detect blooming pear flowers. Flower detection is performed using a YOLO (You Only Look Once)-based object detection algorithm, and three-dimensional flower positions are estimated by integrating depth information with the drone’s positional and orientation data in the east-north-up coordinate system. To enhance pollination efficiency, the method applies the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm to group detected flowers based on spatial proximity that correspond to branch-level distributions. The cluster centroids then construct a collision-free flight path, with offset vectors ensuring safe navigation and appropriate nozzle orientation for effective pollen spraying. Field experiments conducted using RTK-GNSS-based flight control confirmed the accuracy and stability of generated flight trajectories. The drone hovered in front of each flower cluster and performed uniform spraying along the planned path. The method achieved a fruit set rate of 62.1%, exceeding natural pollination at 53.6% and compared to the 61.9% of manual pollination. These results demonstrate the effectiveness and practicability of the method for real-world deployment in pear orchards.

This study clarifies the relationship between subjective person, place, and spatial expression by examining the production and sales location, and the composition of sales space at “Pottery Market” in Mashiko Town. As a result, several patterns about localization degrees and the composition of sales space are found. Furthermore, by comparing these results, several characteristics are identified as the overall picture of the relationship between ceramic activities and the regional environment. These are the way of being, such as continuity of daily activities, selective division of locations, purposeful movement and sales, and compatibility of ceramic culture cooperation and individual spatial expression.

Nanotribology is a key technology for the advanced mechanical industry. The development of advanced micro- and nano-mechanical systems, bio-technology elements, automobile mechanics and so on have been realized in recent years. Nanotribology is a key technology for the advanced mechanical industry. The development of advanced micro- and nano-mechanical systems, bio-technology elements, automobile mechanics and so on have been realized in recent years. Tribological durability properties of extremely thin diamond-like carbon (DLC) films (target thickness 0.03–5 nm) deposited using filtered cathodic vacuum arc (FCVA) and plasma-chemical vapor deposition (P-CVD) methods were evaluated using load-increase-and-decrease, ball-on-disk, and low-load reciprocating friction tests. Friction durability notably increased at a certain film thickness for FCVA-DLC and P-CVD-DLC films. These thicknesses were nearly equal to surface roughness and corresponded to film thickness at which the nanowear profiles changed from protuberance to grooves and at which nanoscratch resistance increased. Excellent friction durability of FCVA-DLC films (thickness \(\ge\) 0.4 nm), evaluated by the rapid increase in the friction coefficient, was observed. In contrast, the friction durability of P-CVD-DLC films gradually increased when the film thickness was 0.6 nm or greater. When the thickness of the DLC films was 2.0 nm or greater, the films did not exhibit a rapid increase in their friction coefficient within the total number of testing cycles. The tribological properties of extremely thin DLC films depend on film thickness; extremely thin FCVA-DLC films exhibit excellent wear resistance. The film thickness at which FCVA-DLC films endured the total number of test cycles was approximately one-fifth the corresponding thickness of P-CVD-DLC films, evaluated by the three different friction tests. The friction durability notably increases at a certain film thickness. FCVA-DLC films (thickness \(\ge\) 0.4 nm) exhibited excellent friction durability. The ratio between the durable film thickness of FCVA-DLC and P-CVD-DLC films was about 1/5. Friction durability dependence on thickness corresponds to the nanowear properties.