长崎大学

Controlling the structural properties and dissolution of bioactive glasses (BGs) through compositional design is critical for enhancing and tailoring their performance in tissue engineering and bone healing. This study aimed to systematically investigate the effects of MgO substitution on the structure and dissolution behavior of SrOcontaining silicate-based BGs using molecular dynamics (MD) simulations and experimental analysis. The simulation was conducted using LAMMPS for a series of Sr-Mg co-substituted BGs with varying MgO content. MD simulations were combined with experimental analysis to validate the structural predictions and identify the optimal BG composition. The simulation confirmed that the majority of the network is based on Si-O-Si bonds rather than P-O-P bonds. Meanwhile, the MgO substitution slightly decreased the BG density while bridging and non-bridging oxygens (BO and NBO) distribution stayed the same. Clustering analysis revealed that BG with 5 % MgO exhibited the most optimal R-factor, correlating with the most homogeneous atomic distribution. Moreover, inductively coupled plasma atomic emission spectroscopy (ICP-AES) and 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) results demonstrated that the BG with 5 % MgO showed favorable ion release followed by statistically significant cell viability compared to the control sample at 1 (*p<0.05), 3 (**p<0.01), and 7 days (**p<0.01). Taken together, according to the structural properties derived from the MD simulations, ion release behavior profile, and MTT assay of the co-substituted SMBGs, S5M5 was considered the most optimal sample to be utilized and further studied for biomedical and therapeutic applications. These findings provide new insights into how MgO/SrO co-substitution alters the BG dissolution behavior, offering a novel strategy for designing BGs with tunable therapeutic performance.

This paper considers critical points of the length-penalized elastic bending energy among planar curves whose endpoints are fixed. We classify all critical points with an explicit parametrization. The classification strongly depends on a special penalization parameter lambda 0.70107. Stability of all the critical points is also investigated, and again the threshold lambda plays a decisive role. In addition, our explicit parametrization is applied to compare the energy of critical points, leading to uniqueness of minimal nontrivial critical points. As an application we obtain eventual embeddedness of elastic flows. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This study establishes a pressure-relief model of roof pre-blasting aimed at preventing rock bursts. The stress and energy evolution law of roadway surrounding rock under the action of higher and lower roof pre-blasting was studied to determine the pressure and release energy mechanism of roof pre-blasting. First, the results show that the factors that influence the degree of energy release of the surrounding rock after roof blasting include the charge length, charge height, borehole spacing and borehole elevation angle. The charge length and charge height mainly affect the energy release range of the surrounding rock, whereas the borehole spacing and borehole elevation angle primarily affects the energy release position and value. Second, the roof and coal seam both releases energy under the action of low-position roof pre-blasting. The energy release range and value of the coal seam increase with an increase in the charge length or decrease in the borehole elevation angle. The energy release only occurs in the pre-blasting area under the action of high-position roof pre-blasting. Third, the pressure and energy release mechanism of roof pre-blasting can be summarized as follows: the pre-blasting of low-position roof promotes the reduction of stress concentration and energy accumulation in the roof and coal seams, weakens the static and dynamic load strength of the surrounding rock, and is beneficial to the prevention and control of coal mass instability-type rock bursts. Furthermore, pre-blasting of high-position roof promotes roof fracture, weakens the energy storage capacity of the high-position roof, weakens the dynamic load strength of the surrounding rock, and is conducive to preventing and controlling hard-roof-type rock bursts. According to the on-site application results, from the perspective of energy accumulation and release in the roof strata and coal seam, using a combination of higher and lower roof blasting is more conducive to preventing rock burst.

A collection of expert opinions critically evaluates the role of seaweed in blue carbon strategies for climate change mitigation. While the concept of fast-growing seaweed to capture atmospheric carbon is appealing, the experts largely agree that its potential for direct, long-term carbon sequestration is currently overstated and faces significant challenges. One primary limitation is that most farmed seaweed is used for food or other products that quickly decompose, releasing the captured carbon back into the atmosphere. Additionally, only a small fraction of seaweed biomass is sequestered in long-term storage sinks like deep-sea sediments. Furthermore, the process of monitoring, reporting, and verification for seaweed-based carbon dioxide removal is complex and currently lacks accurate tools. More importantly, quantifying the net climate benefit is complicated by life cycle emissions from farming and processing, which can offset carbon gains. Some experts suggest a more viable climate benefit lies in using seaweed to reduce emissions by substituting for products with higher carbon footprints. Socio-economic initiatives like the blue carbon crediting scheme in Japan show a path forward, where credits are purchased to support local communities and conservation, suggesting value beyond pure carbon offsetting. The consensus is that while seaweed farming offers substantial benefits for food security and coastal ecosystems, its most realistic contribution to climate action is through indirect emission reduction, not large-scale carbon removal. A rigorous, science-based approach is essential to avoid hype and ensure sustainable development.

Assessment of slope stability under rainfall at the regional scale remains a major challenge in disaster prevention. Traditional geographic information system (GIS)-based methods are efficient but oversimplify slope geometry, while precise numerical simulations are too computationally intensive for large-scale applications. This study applies machine learning (ML) to predict rainfall-induced slope stability at the regional scale. Support vector regression (SVR), extreme gradient boosting (XGBoost), a baseline random forest (RF) model, and an optimized version with recursive feature elimination (RFE-RF) were adopted to capture complex nonlinear relationships between slope geometry and the factor of safety (FoS). Geometric features, including maximum and average slope gradients, surface undulation, and the frequency of undulations exceeding 10 m along the longitudinal direction of the slope, were used as input variables. The proposed framework, integrating numerical simulation and ML, was tested on a highway slope in Nagasaki, Japan. The results indicate that the RFE-RF model achieves the highest predictive accuracy and computational efficiency. With a computation time of only 0.68 s per prediction, this model demonstrates strong potential for real-time and large-scale slope stability evaluations. This approach also enables rapid identification of potentially unstable slope regions for early warning and risk management.