SK Group (Japan)

Remote sensing technologies provide effective means for monitoring and analyzing the environmental impacts of natural hazards. Among Earth observation approaches, optical remote sensing has remained a fundamental data source for decades. In recent years, unmanned aerial vehicles (UAVs), also referred to as drones, have emerged as a flexible and cost-effective platform for acquiring high-resolution geospatial data, including optical imagery, thermal data, and LiDAR point clouds. Owing to their high operational flexibility, UAVs are particularly suitable for collecting first-hand spatial data shortly after disaster events, supporting rapid damage assessment. This study employs UAV-based optical imagery acquired after the Noto earthquake, which occurred on 1 January 2024 in Japan, to support post-disaster geovisualization and spatial analysis. Structure-from-motion (SfM) and multi-view stereo (MVS) techniques are applied to reconstruct three-dimensional (3D) geoinformation from the UAV images. Two key products—a textured triangulated irregular network (TIN) model and an orthophoto—are generated to visualize affected areas and support geospatial analysis. The study focuses on interpreting earthquake-induced damage by integrating 3D models and texture information, with particular emphasis on road damage assessment. Texture features are extracted from orthophotos and represented using indicators derived from the grey-level co-occurrence matrix (GLCM). These texture descriptors, combined with geospatial attributes, are used as inputs to an extreme gradient boosting (XGBoost) model for semi-automatic road damage prediction. The predicted damage results are subsequently correlated with the 3D TIN models to identify locations where road damage is likely to occur. By integrating texture-based analysis with 3D geovisualization, this workflow improves the interpretation of earthquake-related damage across virtual and real-world contexts. The results indicate that UAV-derived optical imagery, combined with machine learning and 3D reconstruction techniques, can support efficient post-disaster damage assessment. This approach enables advanced simulation of decision-making processes and rescue operations, reducing unnecessary costs while improving the effectiveness and timeliness of hazard response.

Current testing of Li-ion pouch cell batteries in manufacturing settings is performed through batch testing and often by destructive means. This process is costly and is insensitive to some defects. Detecting defects earlier in the production process than the end-of-line is imperative for financial and material efficiency and safety objectives to be met. Internal defects, including an improper fill-level of electrolyte, commonly result in explosive thermal runaway and fire, which pose a significant danger to human life. Therefore, the development of methods to rapidly and robustly detect internal defects within cells assists in the initial refinement of production and allows for the identification of defects to prevent these batteries from entering the consumer market. Innovative analysis of signals from air-coupled ultrasound scans for batteries with different electrolyte fill-levels, uses a combination of signal metrics and wavelet transform analysis. Results of Support Vector Machine classification models are used to demonstrate the potential for predicting anomalous fill-levels from air-coupled ultrasound signals, conducive to rapid in-line inspection in gigafactories.

Atmospheric pressure low-temperature plasma treatment has been shown to enhance seed germination in various plant species. However, whether plasma treatment modulates seed dormancy status or affects the seed germination process remains unclear. Additionally, most studies have primarily focused on the positive effects of plasma on germination and growth, without addressing dose-dependent responses or underlying molecular mechanisms. To elucidate the effects of plasma treatment on seed germination at a molecular level, we analyzed the germination phenotype of fully ripened Arabidopsis thaliana seeds under germination-inhibitory conditions following plasma treatment. We observed that plasma treatment enhanced germination potential up to a critical threshold, beyond which prolonged treatment diminished the enhanced effect. Chemical staining assays identified that plasma treatment induced the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) at different time points. Machine-learning aided modeling revealed that ROS, rather than RNS, plays a key role in plasma-mediated germination induction. Furthermore, transcriptome analyses suggested candidate genes likely modulated by plasma treatment during seed germination, including glutathione and L-phenylalanine metabolism, abscisic acid signaling, and the tricarboxylic acid cycle. Our study provides the first molecular-level insights into how atmospheric pressure low-temperature plasma modulates seed germination.

Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challenging domains. In this paper, we derive a new generalization bound for robust risk on the target domain using a novel divergence measure specifically designed for robust domain adaptation. Building upon this, we propose a new algorithm named TAROT, which is designed to enhance both domain adaptability and robustness. Through extensive experiments, TAROT not only surpasses state-of-the-art methods in accuracy and robustness but also significantly enhances domain generalization and scalability by effectively learning domain-invariant features. In particular, TAROT achieves superior performance on the challenging DomainNet dataset, demonstrating its ability to learn domain-invariant representations that generalize well across different domains, including unseen ones. These results highlight the broader applicability of our approach in real-world domain adaptation scenarios.

Background: The 35 kDa low-molecular-weight hyaluronic acid (HA35) injection has shown efficacy in alleviating various types of pain, reducing inflammation and edema, and exhibiting potential in lipolysis and cosmetic applications. Compared to injectable forms, oral administration offers greater convenience and improved patient compliance. However, the absorption mechanism of orally administered low-molecular-weight hyaluronic acid (LMWHA, <100 kDa) remains poorly understood. This study investigates the absorption pathways of two LMWHA—HA35 and HA70—which differ in their cellular binding affinity but exhibit similar tissue permeability, with a particular focus on the role of the mesenteric lymphatic system. Methods: Rats were administered high oral doses of HA35 and HA70. Blood and tissue samples were collected at predefined time points following administration. The concentrations of hyaluronic acid (HA) in serum, mesenteric lymph nodes, liver, and spleen were quantified using enzyme-linked immunosorbent assay (ELISA) to monitor absorption kinetics and tissue distribution. Results: Both HA35 and HA70 were rapidly absorbed into the systemic circulation, predominantly via the mesenteric lymphatic pathway, bypassing the conventional intestinal absorption route through the portal vein and liver. HA levels in the mesenteric lymph nodes increased significantly within 30 minutes post-administration and remained elevated over time. No significant increases in free HA were observed in the liver or spleen, reinforcing lymphatic uptake as the principal route of absorption. Conclusion: In contrast to high-molecular-weight hyaluronic acid (HMWHA, >1000 kDa), which requires degradation prior to absorption, this study demonstrates for the first time that LMWHA molecules (HA35 and HA70, <100 kDa) can be directly absorbed through the intestinal lymphatic system. This alternative absorption route is significant, as it enables compounds to bypass hepatic first-pass metabolism, potentially enhancing bioavailability and prolonging systemic exposure. These findings provide new insights into the absorption mechanisms of LMWHA and suggest promising opportunities for developing oral therapeutics that leverage lymphatic transport pathways for the improved delivery of bioactive macromolecules.

Since the electrical double layer (EDL) is where heterogeneous electron transfer occurs, understanding the effects of the electrode-electrolyte interface structure on electrocatalysis is important. The strong electric field in the EDL offers unique environment for electrochemical reactions by altering the solvation structure and mass transport of redox species. Yet, it is challenging to evaluate the physicochemical properties of the matter within EDL, especially for the inner Helmholtz plane (IHP), where most electrochemical reactions occur. This is largely because of the lack of an appropriate model system for experiments. In this talk, the microscopic environment in the IHP of ionic liquids (ILs) is investigated through the reorganization energy reflecting the solvation structure. Exceptionally fast mass transport of Br2 in N-methyl-N-ethyl-pyrrolidinium polybromide allows to obtain bromine reduction voltammograms in which the current plateau is governed by electron-transfer kinetic and to evaluate the reorganization energy based on the Marcus-Hush-Chidsey model. This enables investigation of the microscopic environment in the IHP of the IL affecting electrocatalytic reactions through reorganization energy. As a demonstration, titanium oxide-modified Pt was employed to show pH-dependent reorganization energy, which suggests the switch of major ions at the IHP as a function of surface charges of electrodes. On the other hand, regulating the potential of zero charge (PZC) of electrodes by surface modification and preparation of alloys with various elemental compositions, the surface charge sensitive-ionic structure in ILs is introduced. References Kim, M.; Park, S.; Chung, T. D., Heterogeneous electron transfer reorganization energy at the inner Helmholtz plane in a polybromide redox-active ionic liquid. Chem. Sci. 2022, 13(30), 8821-8828. Kim, M.; Tetteh, E. B.; Savan, A.; Xiao, B.; Ludwig, A.; Schuhmann, W.; Chung, T. D., Reorganization energy in a polybromide ionic liquid measured by scanning electrochemical cell microscopy. J. Chem. Phys. 2023, 158(13), 134707. Kim, M.; Tetteh, E. B.; Krysiak, O. A.; Savan, A.; Xiao, B.; Piotrowiak, T. H.; Andronescu, C.; Ludwig, A.; Chung, T. D.; Schuhmann, W., Acidic Hydrogen Evolution Electrocatalysis at High-Entropy Alloys Correlates with its Composition-Dependent Potential of Zero Charge. Angew. Chem. Int. Ed. 2023, 62(39), e2023100.

PurposeWe determined the cough counts and their variability in subjects with persistent cough for 30 days.MethodsThe Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.ResultsThe mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day’s 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).ConclusionThere is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.

As the amount of data usage increases with the development of technologies such as artificial intelligence, Internet of Things (IoT), and big data, the need for development of high-performance memory devices and improvement of density is increasing. Phase change memory is a next-generation memory device that stores and erases information by the resistance difference generated by reversibly and rapidly changing the crystal structure of the memory cell. Recently, research on the material engineering aspect to increase thermal efficiency by using various electrode materials and the structural aspect to increase the degree of integration by making a 3D structure is attracting attention. As an electrode material to increase the thermal efficiency of PRAM devices, carbon, which has various resistances (conductor (graphite, sp2 bond) – insulator (diamond, sp3 bond)) depending on the state of atomic bonding is an attractive material. Therefore, research using carbon as an electrode by using PVD and CVD processes has been conducted, but there is a problem that it is difficult to use it for devices with a complex 3D structure with the existing process. Therefore, in this study, an a-C ALD process that can be uniformly deposited in a 3D structure was developed using the CBr4 precursor. And by analyzing the bonding state and physical properties of the deposited thin film, the mechanism of how the a-C thin film grows according to the thin film deposition conditions is studied.

No abstract available.