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Potential of non-flat solar sail for higher characteristic acceleration
Full text linkSolar sails offer a promising means of propelling spacecraft by harnessing Solar Radiation Pressure. Although non-flat solar sails have traditionally been considered less efficient in generating thrust, altering the sails’ design to a non-flat configuration can enhance structural stability, allowing for thinner sail films and reducing overall mass. Consequently, non-flat sails could enable the use of larger sails for future space exploration without excessive mass. This study comprehensively explores non-flat solar sails using three folding techniques: Miura-Ori, Iso-area flasher by Palmer-Shafer, and Parachute-folded. The main goal is to optimize the characteristic acceleration while maintaining its structural integrity. Using Finite Element Analysis (FEA), we analyze deformation patterns and thrust generation to identify the most effective folding technique for a non-flat solar sail. The Parachute-folded sail with five support points demonstrated significantly improved the characteristic acceleration while maintaining a lower deformation than the other two folded configurations and the flat sail counterpart. Further, an enhancement was achieved by strategically integrating a polyimide reinforcement layer along its outer borders, resulting in a sail model with a higher structural resistance to deformation and decreased moment reaction compared to the uniform thickness Parachute-folded sail. Due to computational limitations, the thickness evaluated for this analysis was made in the interval of 30 μm to 400 μm. Investigating new solar sail designs can make large sails more feasible for future missions that are capable of shorter transfer times.journal articl
Function and mechanism of hydrophilic drug-penetration enhancement using ultra-fine water particles on treated skin
Full text linkThe stratum corneum (SC), the outermost layer of the skin, serves as a significant barrier to transdermal drug delivery; therefore, effective penetration enhancers are critical for therapeutic applications. Ultra-fine water (UFW), a humidifier capable of generating nanometer-sized fine water particles, can improve skin barrier function, increase facial skin hydration, and enhance skin softness. However, its mechanism of action on the skin is not clear. Therefore, we aimed to clarify the effects of UFW on skin hydration and its penetration-enhancing properties for hydrophilic drug, particularly L-ascorbic acid 2-glucoside (AAG), and to investigate its mechanisms of action. In vitro studies demonstrated that UFW was rapidly absorbed into the SC, which maintained a higher water content than vapor-treated skin. AAG penetration studies revealed a mountainous flux profile with a peak at 15 min of UFW treatment, which significantly enhanced flux compared to that of untreated controls. A bilayer skin model was employed to calculate penetration parameters, diffusion coefficient and partition coefficient, in each layer, showing that UFW increased solubility in the SC and diffusivity in the viable skin (VS). Furthermore, extraction experiments confirmed that UFW facilitated the retention of AAG in the SC during the early stages of penetration and sustained its release over time. These findings suggest that UFW effectively enhances skin hydration and penetration of hydrophilic drugs, offering a safe and multifunctional tool for cosmetic and medical applications. Its unique properties make it a promising candidate for addressing challenges in transdermal drug delivery and skin treatment.journal articl
RNA修飾部位を予測するための深層学習と機械学習戦略に関する研究
Full text link九州工業大学博士（情報工学）1 Introduction| 2 Methods and Materials| 3 Stack-DHUpred| 4 Meta-2OM| 5 GAPred-ac4C| 6 Conclusion| 7 Future Directions| 8 Acknowledgements| 9 List of PublicationsRNA modifications play critical roles in regulating RNA stability, structure, and function, influencing various biological processes. Among nearly 300 known chemical modifications, dihydrouridine (DHU or D) is commonly found in tRNA, mRNA, and snoRNA, which are closely associated with disease pathogenesis and various biological processes in eukaryotes. 2¢-O-methylation (2-OM or Nm) is another widespread RNA modification observed in various RNA types like tRNA, mRNA, rRNA, miRNA, piRNA, and snRNA, which occurs on the ribose sugar of RNA and contributes to stability and translational control. N4-acetylcytidine (ac4C) is another novel and highly conserved chemical modification observed in both eukaryotic and prokaryotic tRNA, rRNA, and mRNA, that is involved in maintaining translational fidelity and enhancing mRNA stability. Understanding these modifications is essential for elucidating post-transcriptional gene regulation and its potential implications in human health and disease. To comprehend its modification mechanisms and potential epigenetic regulation, it is necessary to accurately identify the modification (DHU, 2-OM, ac4C) sites.
Traditional experimental methods for detecting RNA modifications, including DHU, 2-OM, and ac4C, have significantly advanced our understanding of RNA biology. However, these methods are often labor-intensive, technically demanding, time-consuming, and costly, particularly when aiming for single-nucleotide resolution across large transcriptomes. Given these challenges, there is a growing demand for computational prediction models to complement and accelerate the discovery of RNA modification sites. Despite progress, existing computational approaches still face several limitations in prediction performance. For instance, DHU site predictors often suffer from data redundancy due to duplicate samples, limited generalizability to independent datasets, and model overfitting. In the case of 2-OM, many predictors are developed using a single type of RNA (e.g., mRNA or rRNA) or target only specific nucleotide modifications (Am, Gm, Cm, or Um), and are often trained on relatively small datasets. For ac4C, current predictors show low accuracy on independent test datasets. Moreover, several machine learning methods and feature encoding strategies remain unexplored, limiting the full potential of prediction models for these RNA modifications.
To address these challenges, in this study, we proposed three cutting-edge predictors named Stack-DHUpred, Meta-2OM, and GAPred-ac4C, which can accurately identify DHU, 2-OM, and ac4C, respectively. In Stack-DHUpred, we systematically evaluated six classifiers across 11 RNA sequence features, resulting in the development of 66 baseline (single-feature) models. These baseline models were then combined using logistic regression in a stacked ensemble framework. The optimal subset of baseline models was selected to construct the final stacked model, named Stack-DHUpred. This model achieved an accuracy greater than 0.77 and an AUC above 0.87 on the independent dataset, outperforming existing tools on both training and independent datasets. Meta-2OM utilized a meta-learning approach that considered eight conventional machine learning algorithms and eighteen different feature encoding algorithms that cover physicochemical, compositional, position-specific, and natural language processing information. The predicted probabilities of 2-OM sites from the 144 baseline models are then combined and trained using logistic regression to generate the optimal prediction. On the independent test set, Meta-2OM achieved an overall accuracy above 0.87 and AUC above 0.93, demonstrating superior performance compared to the existing predictors. GAPred-ac4C was built by combining probability scores from 120 ML and DL-based single-feature models. Using forward feature selection, a genetic algorithm-based meta-model leveraging six top-performing features from CNNBiGRU, CNNBiLSTM, CNNAtt, and LGBM achieved optimal results. GAPred-ac4C presents the AUC values of 0.893 and 0.902 for training and independent datasets, respectively, which outperformed all existing state-of-the-art methods and demonstrated the superiority of the model. Therefore, the proposed approaches significantly improved the prediction performance, and we believe that these can be extended to other sequence-based function prediction problems, including enhancer prediction, peptide therapeutic function prediction, and post-translational modification sites prediction.
To facilitate its use, two user-friendly web servers and standalone programs have been developed and are freely available at http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred, http://kurata35.bio.kyutech.ac.jp/Meta-2OM/, https://github.com/kuratahiroyuki/Stack- DHUpred, and https://github.com/kuratahiroyuki/Meta-2OM.九州⼯業⼤学博⼠学位論⽂ 学位記番号：情工博甲第412号 学位授与年⽉⽇: 令和7年9⽉25⽇令和7年度doctoral thesi
Automatic Classification of Respiratory Sounds by Improving the Loss Function of ResNet
Full text linkRespiratory diseases cause 8 million deaths annually, and this number is expected to increase. Breath auscultation, a primary diagnostic method, is noninvasive, repeatable, and immediate, but faces challenges such as reliance on skilled practitioners, difficulty in quantitative assessment, and limited accessibility in developing regions or disaster sites. To address these issues, we developed a deep learning-based breath sound classification system using the ICBHI 2017 dataset. Our method classifies breath sounds into four categories: Normal, Crackle, Wheeze, and Crackle and Wheeze. We use ResNet-34 as the base model, which is enhanced with CBAM for better spatial and channel feature extraction. To deal with class imbalances, we incorporate Focal Loss. The system achieves Accuracy of 0.732, SE of 0.607, SP of 0.843, and ICBHI Score of 0.725.conference pape
Charge-to-spin conversion at argon ion milled SrTiO3/NiFe hetero-interfaces
Full text linkTwo-dimensional electron gases (2DEGs) at perovskite oxide interfaces, such as strontium titanate (STO), have garnered significant attention due to their induced ferromagnetic (FM), spin–orbit coupling, and superconducting properties. The 2DEG, formed at the interface between STO and either insulating oxides or reactive metals, exhibits efficient charge-to-spin interconversion in STO/NM(non-magnetic) /FM structures. The insulating oxide layer at the STO interface attenuates the spin currents injected into the ferromagnet. In contrast, the metallic layers facilitate efficient spin current injection but suffer from spin current diffusion. Here, we present an approach to overcome these challenges by directly creating a 2DEG at the STO surface through Ar+ ion bombardment. This method enables efficient spin-to-charge conversion without an intermediate NM layer. Our experimental and simulation results demonstrate the generation of unconventional spin currents at the STO(Ar+)/NiFe(Permalloy) interface. Our findings may enable applications of complex oxide and ferromagnet interfaces for efficient charge-to-spin conversion, paving the way for low-power, room-temperature oxide-based spintronic devices.journal articl
Analyzing Eye-Tracking Data to Detect Joint Attention in Hexgame Experiments
Full text linkThis study aims to explore the mechanisms of joint attention in a strategic game experiment by analyzing Tobii eyetracking data. In this experiment, two participants play Hexgame, during which the gaze direction is tracked and projected onto the game's board plane through perspective geometry, in order to track attention sharing correlates at particular stages of the game from behavioral data. The primary focus of this study is the analysis of eye-tracking data to identify attention coordination between players during the progression of the game. Future work will expand this framework to assess win probabilities and predict subsequent moves, providing deeper insights into strategic decisionmaking.conference pape
Objective Evaluation of Out-of-Competition Volume of Action in Wheelchair Basketball Classification
Full text linkIn wheelchair basketball, classes are based on competition observations. Since 2021, out-of-competition testing has been implemented; however, research remains limited. This study aimed to determine whether the quantified volume of action (VOA) can be an indicator for classification and examined the influence of a competitive wheelchair on VOA evaluation. This cross-sectional study included 47 wheelchair basketball players (21 able-bodied, 26 with physical impairments: class 1, n = 8; class 2, n = 5; class 3, n = 4; class 4, n = 9). Tests were performed in a wheelchair (wheelchair condition) and on a trainer bed (bed condition). Participants held a ball and rotated their trunks in various planes. Movements were recorded using four cameras, and position coordinates were extracted using the three-dimensional DLT method. Classes and sitting conditions were compared across five groups: classes 1, 2, 3, 4, and able-bodied. Comparisons between classes revealed significant differences in all planes, including wheelchair and bed conditions (p < 0.05). The VOA expanded in the wheelchair condition compared to the bed condition across multiple classes and planes (p < 0.05). Measuring the VOA outside the competition while sitting on a bed may effectively classify players by eliminating equipment influence.journal articl
大学における情報基盤及び情報ネットワークのセキュリティ強化と運用に関する研究
Full text link九州工業大学九州工業大学博士学位論文（要旨）学位記番号: 情工博甲第406号 学位授与年月日:令和7年3月25日thesi
実社会の課題解決のためのデータ収集・分析・管理およびシステム化に関する研究
Full text link九州工業大学九州工業大学博士学位論文（要旨）学位記番号: 情工博乙第61号 学位授与年月日:令和7年3月25日thesi
Zn-Based Three-Dimensional Metal-Organic Framework for Selective Fluorescence Detection in Zwitterionic Ions
Full text linkZinc-based MOFs exhibit significant advantages in ion detection due to their unique structure and chemical properties. They can efficiently and selectively recognize and detect specific ions, making them powerful analytical tools for applications in environmental monitoring, biomedical fields, and more. In this work, we used a simple ligand to improve the coordination environment of Zn2+ ions and successfully synthesized a 3D coordination compound Zn(all-bdc)(Py) MOF through a straightforward hydrothermal method at low temperature. Additionally, we explored the potential of this MOF as a bifunctional ion fluorescence probe for both cationic and anionic recognition. The results showed that this 3D porous MOF exhibited excellent recognition ability for trivalent iron ions (Fe3+) and potassium permanganate (KMnO4−) ions due to its highly porous structures and efficient ion recognition. When iron ions were added to 500 μL and potassium permanganate ions were added to 100 μL, the fluorescence of the compound was effectively quenched, and the detection limits for these two ions were 0.95 μM and 0.13 μM, respectively. The mixed-ion experiments also demonstrated that even in the presence of similar ions, this 3D MOF still maintained good selective recognition ability, specifically identifying Fe3+ and KMnO4− ions. This work provides a novel synthetic strategy for the design of MOFs capable of mixed-ion recognition and detection, expanding their application potential in ion sensing and analysis.journal articl