Year: 2025
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Green Conversion of Palm Oil Wastes into Bio-Compressed Natural Gas (Bio-CNG) for Renewable Energy Diversification in Malaysia
This collaborative research aims to enhance the production and upgrading of bio-compressed natural gas (Bio-CNG) by effectively utilizing two major byproducts generated from Malaysia’s palm oil industry: palm oil mill effluent (POME) and empty fruit bunches (EFB). Specifically, the Japanese team will develop and support the operation of anaerobic digestion processes, conduct microbial community analyses, and apply machine-learning-based data-driven modelling to improve process stability and enable rapid and precise optimization of CO2 adsorbent performance. The Malaysian team will optimize pretreatment and fermentation conditions for EFB and lead the development and evaluation of gas separation technologies using EFB-derived biochar. Through collaborative research between the two countries, the project is expected to contribute to establishing a renewable energy technology platform that supports Malaysia’s transition toward a decarbonized energy society
Mamoru Oshiki, and Adeline Seak May CHUA
Networked Exchange, United Strength for Stronger Partnerships between Japan and ASEAN (NEXUS) 2026.1〜2028.12
Design of micro-electro-mechanical systems-driven environmental sensor for steel corrosion detection in concrete using sacrificial anode metal sheets
This study develops a MEMS (Micro-Electro-Mechanical Systems)-based monitoring system incorporating MEH (Micro-Energy Harvester) technology to identify chloride-induced corrosion-prone areas in RC structures. The proposed system integrates sacrificial anode metal sheets (SAMS) into MEMS–MEH devices, enabling simultaneous corrosion detection and energy harvesting. In tracking corrosion progress through frequency shifts while generating power, the system operates without batteries or external power. The results help define target operating frequencies for reliable MEMS–MEH performance in predicting corrosion conditions in RC structures.
Nicharin Nithimethaporn, Katsufumi Hashimoto, Hiroyuki Mitsuya, Hisayuki Ashizawa, Takuma Ishiguro, and Wanchai Yodsudjai
Application of unsupervised AI-assisted acoustic wave sound analysis for non-destructive detection of steel corrosion induced deterioration
Reinforced concrete structures require reliable monitoring to ensure safety and efficient maintenance. Non-destructive testing methods such as tapping sound inspection are widely applied. However, the diagnosis results often depend on technical expert skill and experience. This study proposes an easy-to-use, AI-based evaluation method for tapping sounds using unsupervised deep learning. Laboratory tests were carried out on reinforced concrete beams with simulated steel bar corrosion. The method proposes an anomaly index that reflects corrosion progress and surface cracking. The results demonstrate that acoustic inspection with AI can support early damage detection and improve condition assessment of concrete structures.
Nopphanan Phannakham, Katsufumi Hashimoto, Yasuhiko Sato, and Naoshi Ueda
A multi-module deep learning framework with graph-based network and crack attention for tunnel lining crack segmentation from LiDAR point cloud
Cracks in railway tunnels threaten safety and require efficient monitoring. This study introduces PointCrackNet, a deep learning method that detects tunnel lining cracks directly from LiDAR point clouds. By combining graph-based convolution, attention mechanisms, and a crack-enhancement module, the model accurately captures fine crack details while maintaining global structural context. A tailored loss function addresses class imbalance and improves crack continuity. Tested on a large-scale LiDAR dataset from a real railway tunnel, PointCrackNet outperformed existing methods. The approach enables automated tunnel inspection and supports smart, data-driven infrastructure maintenance.
Shanpeng Liu, Koichi Isobe, Junling Si, Diyuan Li, and Daoju Ren
Construction and Building Materials Volume 494, 2025, 143383
Effects of Combined Deterioration of Steel Corrosion and Freeze-thaw Cycles on the Pull-out Behavior of Deformed Bars in Concrete
This study is the first to systematically investigate how loading sequence and cracking history affect the bond performance of deformed bars under the combined deterioration caused by steel corrosion and freeze–thaw action, a serious problem in concrete structures in cold regions. Through controlled experiments simulating realistic deterioration paths, the study clarified the influence of damage sequence on structural performance. In particular, it demonstrated that pre-existing cracks significantly accelerate deterioration by promoting moisture ingress and freeze–thaw damage. These findings highlight that considering damage history is essential for reliable durability assessment and long-term maintenance planning of aging reinforced concrete infrastructure.
Muhmudul Hasan Mizan, Ryuhei Hayakawa, and Koji Matsumoto
Superfluid dripping: a new analog for continuous time crystals
The dripping behavior of superfluid helium-4 has been found to be consistently discretized, even when flow rates vary. This unexpected phenomenon suggests that the superfluid dripping system exhibits time crystallinity by spontaneously breaking continuous time translation symmetry. The condition for the emergence of this continuous time crystal is that the edges of the pendant droplets, which hang from the underside of the cup, can move freely—a characteristic specific to superfluid dripping. This free motion leads to volume-independent oscillation periods for the droplets, effectively eliminating the influence of fluctuations in the flow rates.
Shota Takamatsu, Ryota Yamane, Tomoyuki Tani, Yuri Ishimoto, Keito Miyake, Yuki Aoki, and Ryuji Nomura
Mesoscale modeling of anisotropic compressive behavior and pull-out performance of 3D printed concrete with steel bars using 3D RBSM
This study uses a 3D Rigid Body Spring Model (RBSM) to analyze the anisotropic behavior of 3D-printed concrete (3DPC) with steel reinforcement. Validated by experiments, the research highlights how the mesoscale structure—specifically porous interlayer interfaces—affects performance. Results indicate that specimens loaded parallel to the printing direction exhibit superior compressive strength and bond performance. Conversely, loading perpendicular to the layers leads to stress concentrations and weaker bonds due to interfacial zones. Overall, this research provides a predictive framework for optimizing the structural integrity of 3DPC through mesoscale modeling.
Jiaxu Yao, Jie Luo, Minghong Qiu, and Kohei Nagai
Substituent Effects on Electrocyclic Reactions: Ultrafast Ring-Opening of α-Phellandrene Stimulated by Impulsively Excited Molecular Vibrations
Zhiyi Zhou, Kenichiro Saita, Yusuke Minegishi, Tetsuya Taketsugu, and Taro Sekikawa*
Broadband Phase Retardation with Palladium Coated Mirrors for M-edge XMCD in the 40–70 eV Range
Furkan Aksay, and Taro Sekikawa
Self-organized luminous pattern formation observed above the anode surface of a DC glow discharge in pure He
To elucidate the mechanism of self-organization phenomena in luminescence observed on the anode surface during atmospheric pressure direct current glow discharge generation, we investigated luminescence patterns while varying pressure in a pure He environment. The results revealed that luminescence patterns appear when the product of pressure p and electrode distance d (pd) is high. Moving forward, we will connect this to reaction-diffusion systems, a method used in mathematics to obtain self-organization phenomena.
Toshiaki Miyazaki, Jan Kuhfeld, Koichi Sasaki, and Naoki Shirai