Purpose:The purpose of this research is to provide evidence for decision-makers to realize the potentials of collaborations between countries/regions via the scientometric analysis of co-authoring in academic publicat...Purpose:The purpose of this research is to provide evidence for decision-makers to realize the potentials of collaborations between countries/regions via the scientometric analysis of co-authoring in academic publications.Design/methodology/approach:The approach is that Osaka University,which has set a strategy to become a global campus,is positioned to have a leading role to enhance such collaborations.This research measures co-authoring relations between Osaka University and other countries/regions to identify networks for fostering strong research collaborations.Findings:Five countries are identified as candidates for the future global campuses of Osaka University based on three factors,co-authoring relations,GDP growth,and population growth.Research limitations:The main limitation of this study is not being able to use the relations by the former positions of authors in Osaka University,because the data retrieved is limited by the query of the organization name at the first step.Practical implications:The significance of this work is to provide evidence for the university strategy to expand abroad based on the quantity and visualization of trends.Originality/value:With wider practical implementations,the approach of this research is useful in making a strategic roadmap for scientific organizations that intend to collaborate internationally.展开更多
Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral te...Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover(LULC)mapping.A basic Raman spectroscopy model demonstrates that Savitzky-Golay(SG)filtering,Wavelet denoising,and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULCclassification.Savitzky-Golay filtering yielded the most efficient results,increasing classification accuracy from 0.71(noisy)to 1.00(denoised),resulting in perfect classification with zero errors and enhancing the Precision-Recall curve,as Area Under the Precision-Recall Curve(AUC-PR)transformed from 0.84 to 1.00.The study examined wavelet denoising in conjunction with a 1D Convolutional Autoencoder,assessing the noise reduction capability through visual evaluation.Based on Raman-based spectral analysis,a traditional method complemented with machine learning denoising provides promising fields for feature identification in remote sensing images,thereby improving the quality of LULC-related mapping outcomes.展开更多
Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,...Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.展开更多
Prologue In On Death and Dying(1969),Kübler-Ross introduced the five stages of"dying"-denial,anger,bargaining,depression,and acceptance.Originally designed to outline the emotional journey of individual...Prologue In On Death and Dying(1969),Kübler-Ross introduced the five stages of"dying"-denial,anger,bargaining,depression,and acceptance.Originally designed to outline the emotional journey of individuals confronting mortality,this framework can also be extended to various types of loss and transition,such as the"death of the old teacher"in the AI era.展开更多
In this study, we investigate the properties of black holes within the framework of multi-fractional theories of gravity, focusing on the effects of q-derivatives and weighted derivatives. These modifications, which i...In this study, we investigate the properties of black holes within the framework of multi-fractional theories of gravity, focusing on the effects of q-derivatives and weighted derivatives. These modifications, which introduce scale-dependent spacetime geometries, alter black hole solutions in intriguing ways. Within these frameworks,we analyze two key observable phenomena-black hole shadows and particle deflection angle in the weak field limit-using both analytical techniques and observational data from the Event Horizon Telescope(EHT) for M87^(*) and Sgr A^(*). The study using the q-derivative formalism reveals that the multi-scale length l_(*)influences the size of the black hole shadow in two ways and modifies the weak deflection angle. Constraints on l_(*)are derived from the EHT observations, showing significant deviations from standard Schwarzschild black hole predictions, which range from 10^(9) to 10^(10)orders of magnitude. Additionally, the weak deflection angle is computed using the non-asymptotic generalization of the Gauss-Bonnet theorem(GBT) to reveal the effects of finite-distance and multi-scale parameters.Using the Sun in the Solar System test, we observe that the constraints for l_(*)range from 10^(8) to 10^(9) orders of magnitude. Results from the weighted derivative formalism generate a dS/AdS-like behavior, where smaller deviations are found in the strong field regime than in the weak field regime. The results suggest that, while these effects are subtle, they provide a potential observational signature of quantum gravity effects. The findings presented here contribute to the broader effort of testing alternative theories of gravity through black hole observations, offering a new perspective on the quantum structure of spacetime at cosmological and astrophysical scales.展开更多
文摘Purpose:The purpose of this research is to provide evidence for decision-makers to realize the potentials of collaborations between countries/regions via the scientometric analysis of co-authoring in academic publications.Design/methodology/approach:The approach is that Osaka University,which has set a strategy to become a global campus,is positioned to have a leading role to enhance such collaborations.This research measures co-authoring relations between Osaka University and other countries/regions to identify networks for fostering strong research collaborations.Findings:Five countries are identified as candidates for the future global campuses of Osaka University based on three factors,co-authoring relations,GDP growth,and population growth.Research limitations:The main limitation of this study is not being able to use the relations by the former positions of authors in Osaka University,because the data retrieved is limited by the query of the organization name at the first step.Practical implications:The significance of this work is to provide evidence for the university strategy to expand abroad based on the quantity and visualization of trends.Originality/value:With wider practical implementations,the approach of this research is useful in making a strategic roadmap for scientific organizations that intend to collaborate internationally.
文摘Noise present in remote sensing data creates obstacles to proper land use and land cover(LULC)classification methods.Thepaper evaluates machine learning(ML)denoisingmethods that adapt Raman spectroscopy’s spectral techniques to optimise remote sensing spectra for land-use/land-cover(LULC)mapping.A basic Raman spectroscopy model demonstrates that Savitzky-Golay(SG)filtering,Wavelet denoising,and basic 1D Convolutional Autoencoder have different effects on synthetic spectral features relevant to LULCclassification.Savitzky-Golay filtering yielded the most efficient results,increasing classification accuracy from 0.71(noisy)to 1.00(denoised),resulting in perfect classification with zero errors and enhancing the Precision-Recall curve,as Area Under the Precision-Recall Curve(AUC-PR)transformed from 0.84 to 1.00.The study examined wavelet denoising in conjunction with a 1D Convolutional Autoencoder,assessing the noise reduction capability through visual evaluation.Based on Raman-based spectral analysis,a traditional method complemented with machine learning denoising provides promising fields for feature identification in remote sensing images,thereby improving the quality of LULC-related mapping outcomes.
基金supported by The Social Science Foundation of Fujian Province(Grant no.FJ2021B080)The 2023 Fujian Provincial Foreign Cooperation Science and Technology Plan Project(2023I0047)+3 种基金The 2022 Longyan Industry-University-Research Joint Innovation Project(2022LYF18001)The 2023 Fujian Natural Resources Science and Tech-nology Innovation Project(KY-060000-04-2023-2002)Open Project Fund of Hunan Provincial Key Laboratory for Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area(Project No:DTH Key Lab.2023-04)The Construction Science and Technology Research and Development Project of Fujian Province,China(Grant no.2022-K-85).
文摘Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.
文摘Prologue In On Death and Dying(1969),Kübler-Ross introduced the five stages of"dying"-denial,anger,bargaining,depression,and acceptance.Originally designed to outline the emotional journey of individuals confronting mortality,this framework can also be extended to various types of loss and transition,such as the"death of the old teacher"in the AI era.
文摘In this study, we investigate the properties of black holes within the framework of multi-fractional theories of gravity, focusing on the effects of q-derivatives and weighted derivatives. These modifications, which introduce scale-dependent spacetime geometries, alter black hole solutions in intriguing ways. Within these frameworks,we analyze two key observable phenomena-black hole shadows and particle deflection angle in the weak field limit-using both analytical techniques and observational data from the Event Horizon Telescope(EHT) for M87^(*) and Sgr A^(*). The study using the q-derivative formalism reveals that the multi-scale length l_(*)influences the size of the black hole shadow in two ways and modifies the weak deflection angle. Constraints on l_(*)are derived from the EHT observations, showing significant deviations from standard Schwarzschild black hole predictions, which range from 10^(9) to 10^(10)orders of magnitude. Additionally, the weak deflection angle is computed using the non-asymptotic generalization of the Gauss-Bonnet theorem(GBT) to reveal the effects of finite-distance and multi-scale parameters.Using the Sun in the Solar System test, we observe that the constraints for l_(*)range from 10^(8) to 10^(9) orders of magnitude. Results from the weighted derivative formalism generate a dS/AdS-like behavior, where smaller deviations are found in the strong field regime than in the weak field regime. The results suggest that, while these effects are subtle, they provide a potential observational signature of quantum gravity effects. The findings presented here contribute to the broader effort of testing alternative theories of gravity through black hole observations, offering a new perspective on the quantum structure of spacetime at cosmological and astrophysical scales.
