In spinal cord injuries,external forces from various directions occur at various velocities.Therefore,it is important to physically evaluate whether the spinal cord is susceptible to damage and an increase in internal...In spinal cord injuries,external forces from various directions occur at various velocities.Therefore,it is important to physically evaluate whether the spinal cord is susceptible to damage and an increase in internal stress for external forces.We hypothesized that the spinal cord has mechanical features that vary under stress depending on the direction and velocity of injury.However,it is difficult to perform experiment because the spinal cord is very soft.There are no reports on the effects of multiple external forces.In this study,we used bovine spinal cord white matter to test and analyze the anisotropy and velocity dependence of the spinal cord.Tensile-vertical,tensile-parallel,shear-vertical,and shear-parallel tests were performed on the white matter in the fibrous direction(cranial to caudal).Strain rate in the experiment was 0.1,1,10,and 100/s.We calculated the Young’s modulus of the spinal cord.Results of the tensile and shear tests revealed that stress tended to increase when external forces were applied parallel to the direction of axon fibers,such as in tensile-vertical and shear-vertical tests.However,external forces those tear against the fibrous direction and vertically,such as in tensile-parallel and shear-parallel tests,were less likely to increase stress even with increased velocity.We found that the spinal cord was prone to external forces,especially in the direction of the fibers,and to be under increased stress levels when the velocity of external forces increased.From these results,we confirmed that the spinal cord has velocity dependence and anisotropy.The Institutional Animal Care and Use Committee of Yamaguchi University waived the requirement for ethical approval.展开更多
Hydrated Cement Treated Crushed Rock Base （HCTCRB） is widely used as a base course in Western Australian pavements. HCTCRB has been designed and used as a basis for empirical approaches and in empirical practices. T...Hydrated Cement Treated Crushed Rock Base （HCTCRB） is widely used as a base course in Western Australian pavements. HCTCRB has been designed and used as a basis for empirical approaches and in empirical practices. These methods are not all-encompassing enough to adequately explain the behaviour of HCTCRB in the field. Recent developments in mechanistic approaches have proven more reliable in the design and analysis of pavement, making it possible to more effectively document the characteristics of HCTCRB. The aim of this study was to carry out laboratory testing to assess the mechanical characteristics of HCTCRB. Conventional triaxial tests and repeated load triaxial tests （RLT tests） were performed. Factors affecting the performance of HCTCRB, namely hydration periods and the amount of added water were also investigated. It was found that the shear strength parameters of HCTCRB were 177 kPa for cohesion （c） and 42~ for the internal friction angle （~）. The hydration period, and the water added in this investigation affected the performance of HCTCRB. However, the related trends associated with such factors could not be assessed. All HCTCRB samples showed stress-dependency behaviour. Based on the stress stages of this experiment, the resilient modulus values of HCTCRB ranged from 300 MPa to 1100 MPa. CIRCLY, a computer program based on the multi-layer elastic theory was used in the mechanistic approach to pavement design and analysis, to determine the performance of a typical pavement model using HCTCRB as a base course layer. The mechanistic pavement design parameters for HCTCRB as a base course material were then introduced. The analysis suggests that the suitable depth for HCTCRB as a base layer for WA roads is at least 185 mm for the design equivalent standard axle （ESA） of 10 million.展开更多
Amine-based CO_(2) capture represents the most mature approach for large-scale carbon reduction,with systems implemented across multiple industrial demonstration projects globally.However,vast chemical spaces encompas...Amine-based CO_(2) capture represents the most mature approach for large-scale carbon reduction,with systems implemented across multiple industrial demonstration projects globally.However,vast chemical spaces encompassing millions of potential formulations and complex multiscale coupling effects pose unprecedented challenges for traditional experimental methods.Machine learning applications have achieved revolutionary advances through differentiated strategies.In liquid amine systems,ensemble learning algorithms delivered breakthrough precision improvements from traditional 4-5%to below 0.93%,while interpretable models revealed that nitrogen atom charge distribution contributes 56%to reaction barriers,enabling rational biphasic solvent design(DETA/DEEA system)that achieved 34%regeneration energy reduction compared to benchmark MEA.For solid amine systems,differential descriptor methods overcame severe overfitting challenges,improving test set performance from R2=0.5102 to 0.79.Virtual screening of 1.6 million binding sites from the GDB-17 database identified 11%of candidates with stronger CO_(2) binding than the industrial benchmark BPEI(−0.04 eV).Among these high-performance candidates,2642 molecules simultaneously satisfied synthesizability criteria(SAscore<3.4,GDBscore>0.64),demonstrating both favorable binding energetics and high experimental feasibility.Critically,mechanistic analysis revealed that support physical properties dominate adsorption performance over amine chemical characteristics,fundamentally transforming material design concepts.Industrial applications demon-strated 35.76%cost reductions through intelligent solvent selection and 15-25%profit improvements through dynamic capture-level optimization combined with market-responsive bidding strategies.Despite these break-throughs,systematic limitations,including model generalization difficulties,cross-scale integration challenges,and data standardization,persist,requiring physics-constrained algorithms and unified modeling frameworks for laboratory-to-industrial translation.These developments establish machine learning as the core driving force transitioning amine-based CO_(2) capture from empirical development toward intelligent design paradigms.展开更多
基金This work was supported by the Japan Society for the Promotion of Science(KARENHI grant number JP 15K20002)by the Yamaguchi University Hospital(a translational promotion grant).
文摘In spinal cord injuries,external forces from various directions occur at various velocities.Therefore,it is important to physically evaluate whether the spinal cord is susceptible to damage and an increase in internal stress for external forces.We hypothesized that the spinal cord has mechanical features that vary under stress depending on the direction and velocity of injury.However,it is difficult to perform experiment because the spinal cord is very soft.There are no reports on the effects of multiple external forces.In this study,we used bovine spinal cord white matter to test and analyze the anisotropy and velocity dependence of the spinal cord.Tensile-vertical,tensile-parallel,shear-vertical,and shear-parallel tests were performed on the white matter in the fibrous direction(cranial to caudal).Strain rate in the experiment was 0.1,1,10,and 100/s.We calculated the Young’s modulus of the spinal cord.Results of the tensile and shear tests revealed that stress tended to increase when external forces were applied parallel to the direction of axon fibers,such as in tensile-vertical and shear-vertical tests.However,external forces those tear against the fibrous direction and vertically,such as in tensile-parallel and shear-parallel tests,were less likely to increase stress even with increased velocity.We found that the spinal cord was prone to external forces,especially in the direction of the fibers,and to be under increased stress levels when the velocity of external forces increased.From these results,we confirmed that the spinal cord has velocity dependence and anisotropy.The Institutional Animal Care and Use Committee of Yamaguchi University waived the requirement for ethical approval.
文摘Hydrated Cement Treated Crushed Rock Base （HCTCRB） is widely used as a base course in Western Australian pavements. HCTCRB has been designed and used as a basis for empirical approaches and in empirical practices. These methods are not all-encompassing enough to adequately explain the behaviour of HCTCRB in the field. Recent developments in mechanistic approaches have proven more reliable in the design and analysis of pavement, making it possible to more effectively document the characteristics of HCTCRB. The aim of this study was to carry out laboratory testing to assess the mechanical characteristics of HCTCRB. Conventional triaxial tests and repeated load triaxial tests （RLT tests） were performed. Factors affecting the performance of HCTCRB, namely hydration periods and the amount of added water were also investigated. It was found that the shear strength parameters of HCTCRB were 177 kPa for cohesion （c） and 42~ for the internal friction angle （~）. The hydration period, and the water added in this investigation affected the performance of HCTCRB. However, the related trends associated with such factors could not be assessed. All HCTCRB samples showed stress-dependency behaviour. Based on the stress stages of this experiment, the resilient modulus values of HCTCRB ranged from 300 MPa to 1100 MPa. CIRCLY, a computer program based on the multi-layer elastic theory was used in the mechanistic approach to pavement design and analysis, to determine the performance of a typical pavement model using HCTCRB as a base course layer. The mechanistic pavement design parameters for HCTCRB as a base course material were then introduced. The analysis suggests that the suitable depth for HCTCRB as a base layer for WA roads is at least 185 mm for the design equivalent standard axle （ESA） of 10 million.
基金supported by grants from the National Natural Science Foundation of China(52225003,52570117).
文摘Amine-based CO_(2) capture represents the most mature approach for large-scale carbon reduction,with systems implemented across multiple industrial demonstration projects globally.However,vast chemical spaces encompassing millions of potential formulations and complex multiscale coupling effects pose unprecedented challenges for traditional experimental methods.Machine learning applications have achieved revolutionary advances through differentiated strategies.In liquid amine systems,ensemble learning algorithms delivered breakthrough precision improvements from traditional 4-5%to below 0.93%,while interpretable models revealed that nitrogen atom charge distribution contributes 56%to reaction barriers,enabling rational biphasic solvent design(DETA/DEEA system)that achieved 34%regeneration energy reduction compared to benchmark MEA.For solid amine systems,differential descriptor methods overcame severe overfitting challenges,improving test set performance from R2=0.5102 to 0.79.Virtual screening of 1.6 million binding sites from the GDB-17 database identified 11%of candidates with stronger CO_(2) binding than the industrial benchmark BPEI(−0.04 eV).Among these high-performance candidates,2642 molecules simultaneously satisfied synthesizability criteria(SAscore<3.4,GDBscore>0.64),demonstrating both favorable binding energetics and high experimental feasibility.Critically,mechanistic analysis revealed that support physical properties dominate adsorption performance over amine chemical characteristics,fundamentally transforming material design concepts.Industrial applications demon-strated 35.76%cost reductions through intelligent solvent selection and 15-25%profit improvements through dynamic capture-level optimization combined with market-responsive bidding strategies.Despite these break-throughs,systematic limitations,including model generalization difficulties,cross-scale integration challenges,and data standardization,persist,requiring physics-constrained algorithms and unified modeling frameworks for laboratory-to-industrial translation.These developments establish machine learning as the core driving force transitioning amine-based CO_(2) capture from empirical development toward intelligent design paradigms.
