The Sustainable Development Goals(SDGs)are crucial in tackling the sustainability challenges and emerging issues faced by humanity,with government attention being a significant factor in promoting their successful ach...The Sustainable Development Goals(SDGs)are crucial in tackling the sustainability challenges and emerging issues faced by humanity,with government attention being a significant factor in promoting their successful achievement.However,there is limited quantitative research systematically examining the impacts of government attention on SDGs progress.This study employs text analysis and a panel regression model to analyze the impacts of government attention intensity,text similarity,and tone on the achievement of SDGs,utilizing data extracted from China’s Government Work Reports spanning the decade from 2010 to 2020.The findings reveal that the Chinese government attention to the SDGs has generally increased over time.The heightened focus has notably bolstered the achievement of the SDGs,with the most significant impact observed post-2015.Government attention intensity was identified as the most impactful factor.Moreover,government attention intensity,text similarity,and tone have positively influenced the coupling coordination relationship between 17 SDGs,as measured by the coupling coordination degree,leading to a more harmonious and balanced achievement of socioeconomic and environmental goals in China.Financial investment served as a moderating factor,enhancing the positive impacts of attention intensity,text similarity and tone on the promotion of SDGs attainment.The effects of government attention on SDGs progress were notably positive in the eastern region,exhibiting greater significance in areas with stronger governance capacity compared to those with weaker governance capacity.This study provides insightful information for enhancing the modernization and efficiency of China’s national governance system,promoting SDGs at local and global scales,and fostering sustainable transformation.展开更多
Simulating ambient light adaptability and polarization sensitivity of biological vision is paramount for developing intelligent optoelectronic devices with multi-dimensional perception capabilities.However,achieving b...Simulating ambient light adaptability and polarization sensitivity of biological vision is paramount for developing intelligent optoelectronic devices with multi-dimensional perception capabilities.However,achieving both functionalities in semiconductor devices has historically necessitated complex architectures and high-voltage operation,posing significant challenges for bionic vision systems.Here,we present a light-adaptable and polarization-sensitive bionic vision utilizing a simple yet effective strategy of semiconductor-metal contact engineering in PdSe_(2)transistors.By exploiting the differential coupling strengths at diverse metal-semiconductor interfaces to modulate the dynamics of photogenerated carriers,the device achieves energy-efficient visual adaptive perception across a broad range of lighting conditions,from dim to bright,without the need for additional gate voltage.Furthermore,this transistor enables multi-dimensional perception of visual information through dynamic polarization angle changes and light intensity(dim/bright)detection,providing rich input features for intelligent recognition in complex scenarios.Capitalizing on the intrinsic anisotropy of PdSe_(2)and contact engineering,we have constructed a bionic light-adaptive visual neural network capable of perceiving and recognizing images in complex lighting environments.When enhanced by a residual-generating adversarial network,the system achieves remarkable recognition accuracies of 98%and 97%under dim and bright adaptation conditions,respectively.This research offers a streamlined,versatile,and scalable approach for developing energy-efficient,highly integrated,and multi-dimensional imaging recognition capabilities in light-adaptive and polarization-sensitive bionic vision devices.展开更多
Materials are the building blocks of various functional applications.With Moore’s Law approaching Si’s physical limits,traditional semiconductor-based monolithic three-dimensional(M3D)integrated circuits always suff...Materials are the building blocks of various functional applications.With Moore’s Law approaching Si’s physical limits,traditional semiconductor-based monolithic three-dimensional(M3D)integrated circuits always suffer from the issues,including electrical performance(carrier scattering),chip-overheating(low heat conductivity),electromagnetic interference.Recently,two-dimensional transition metal dichalcogenides(2D TMDs)inherit the atomically-thin thickness of 2D materials and exhibit outstanding natures,such as smooth flatness(excellent compatibility),electronic property(thickness below 1 nm),absence of dangling bonds(decreasing carrier scattering),making them highly promising for next-generation functional devices in comparison with traditional bulk materials.Up to now,2D TMD-based transistors have already exhibited the feasibility of replacing conventional one in terms of performances.Furthermore,the technology of large-area 2D TMDs films has been greatly successful,which lays the foundation for the fabrication of scalable 2D TMD-based devices.Besides,the scalable devices based on 2D TMDs also show the prospects of realizing ultra-high-density M3D integrated circuits owing to the presence of outstanding compatibility.Herein,we focus some thriving research areas and provide a systematic review of recent advances in the field of scalable electronic and optoelectronic devices based on 2D TMDs,including large-area synthesis,property modulation,large-scale device applications,and multifunctional device integration.The research in 2D TMDs has clearly exhibited the tremendous promise for scalable diversified applications.In addition,scalable 2D TMD-based devices in terms of mass production,controllability,reproducibility,and low-cost have also been highlighted,showing the importance and benefits in modern industry.Finally,we summarize the remaining challenges and discuss the future directions of scalable 2D TMDs devices.展开更多
Tissue engineering is promising in realizing successful treatments of human body tissue loss that current methods cannot treat well or achieve satisfactory clinical outcomes.In scaffold-based bone tissue engineering,a...Tissue engineering is promising in realizing successful treatments of human body tissue loss that current methods cannot treat well or achieve satisfactory clinical outcomes.In scaffold-based bone tissue engineering,a high performance scaffold underpins the success of a bone tissue engineering strategy and a major direction in the field is to produce bone tissue engineering scaffolds with desirable shape,structural,physical,chemical and biological features for enhanced biological performance and for regenerating complex bone tissues.Three-dimensional(3D)printing can produce customized scaffolds that are highly desirable for bone tissue engineering.The enormous interest in 3D printing and 3D printed objects by the science,engineering and medical communities has led to various developments of the 3D printing technology and wide investigations of 3D printed products in many industries,including biomedical engineering,over the past decade.It is now possible to create novel bone tissue engineering scaffolds with customized shape,architecture,favorable macro-micro structure,wettability,mechanical strength and cellular responses.This article provides a concise review of recent advances in the R&D of 3D printing of bone tissue engineering scaffolds.It also presents our philosophy and research in the designing and fabrication of bone tissue engineering scaffolds through 3D printing.展开更多
Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in...Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.展开更多
基金supported by Guizhou Province Major Science and Technology Achievement Transformation Project(QKHCG[2024]ZD016)the Excellent Young Scientists Fund from the National Natural Science Foundation of China(Grant No.42422105)+1 种基金Guizhou Province Natural Science Research Project(Qian Jiao Ji[2023]No.033)Provincial Science and Technology Program of Guizhou Province(Grant No.20201Y288).
文摘The Sustainable Development Goals(SDGs)are crucial in tackling the sustainability challenges and emerging issues faced by humanity,with government attention being a significant factor in promoting their successful achievement.However,there is limited quantitative research systematically examining the impacts of government attention on SDGs progress.This study employs text analysis and a panel regression model to analyze the impacts of government attention intensity,text similarity,and tone on the achievement of SDGs,utilizing data extracted from China’s Government Work Reports spanning the decade from 2010 to 2020.The findings reveal that the Chinese government attention to the SDGs has generally increased over time.The heightened focus has notably bolstered the achievement of the SDGs,with the most significant impact observed post-2015.Government attention intensity was identified as the most impactful factor.Moreover,government attention intensity,text similarity,and tone have positively influenced the coupling coordination relationship between 17 SDGs,as measured by the coupling coordination degree,leading to a more harmonious and balanced achievement of socioeconomic and environmental goals in China.Financial investment served as a moderating factor,enhancing the positive impacts of attention intensity,text similarity and tone on the promotion of SDGs attainment.The effects of government attention on SDGs progress were notably positive in the eastern region,exhibiting greater significance in areas with stronger governance capacity compared to those with weaker governance capacity.This study provides insightful information for enhancing the modernization and efficiency of China’s national governance system,promoting SDGs at local and global scales,and fostering sustainable transformation.
基金grateful to the National Key Research and Development Program of China(No.2024YFA1211400)the National Natural Science Foundation of China(Nos.U22A20138,52302162,and 52202183)the Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120041).
文摘Simulating ambient light adaptability and polarization sensitivity of biological vision is paramount for developing intelligent optoelectronic devices with multi-dimensional perception capabilities.However,achieving both functionalities in semiconductor devices has historically necessitated complex architectures and high-voltage operation,posing significant challenges for bionic vision systems.Here,we present a light-adaptable and polarization-sensitive bionic vision utilizing a simple yet effective strategy of semiconductor-metal contact engineering in PdSe_(2)transistors.By exploiting the differential coupling strengths at diverse metal-semiconductor interfaces to modulate the dynamics of photogenerated carriers,the device achieves energy-efficient visual adaptive perception across a broad range of lighting conditions,from dim to bright,without the need for additional gate voltage.Furthermore,this transistor enables multi-dimensional perception of visual information through dynamic polarization angle changes and light intensity(dim/bright)detection,providing rich input features for intelligent recognition in complex scenarios.Capitalizing on the intrinsic anisotropy of PdSe_(2)and contact engineering,we have constructed a bionic light-adaptive visual neural network capable of perceiving and recognizing images in complex lighting environments.When enhanced by a residual-generating adversarial network,the system achieves remarkable recognition accuracies of 98%and 97%under dim and bright adaptation conditions,respectively.This research offers a streamlined,versatile,and scalable approach for developing energy-efficient,highly integrated,and multi-dimensional imaging recognition capabilities in light-adaptive and polarization-sensitive bionic vision devices.
基金financially supported by the National Natural Science Foundation of China(Grant No.52202183)the Shenzhen Science and Technology Program(Grant No.202206193000001)+1 种基金Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023B1515120041,2023A1515012072)the Open Project Fund from Guangdong Provincial Key Laboratory of Materials and Technology for Energy Conversion(Grant No.MATEC2023KF003).
文摘Materials are the building blocks of various functional applications.With Moore’s Law approaching Si’s physical limits,traditional semiconductor-based monolithic three-dimensional(M3D)integrated circuits always suffer from the issues,including electrical performance(carrier scattering),chip-overheating(low heat conductivity),electromagnetic interference.Recently,two-dimensional transition metal dichalcogenides(2D TMDs)inherit the atomically-thin thickness of 2D materials and exhibit outstanding natures,such as smooth flatness(excellent compatibility),electronic property(thickness below 1 nm),absence of dangling bonds(decreasing carrier scattering),making them highly promising for next-generation functional devices in comparison with traditional bulk materials.Up to now,2D TMD-based transistors have already exhibited the feasibility of replacing conventional one in terms of performances.Furthermore,the technology of large-area 2D TMDs films has been greatly successful,which lays the foundation for the fabrication of scalable 2D TMD-based devices.Besides,the scalable devices based on 2D TMDs also show the prospects of realizing ultra-high-density M3D integrated circuits owing to the presence of outstanding compatibility.Herein,we focus some thriving research areas and provide a systematic review of recent advances in the field of scalable electronic and optoelectronic devices based on 2D TMDs,including large-area synthesis,property modulation,large-scale device applications,and multifunctional device integration.The research in 2D TMDs has clearly exhibited the tremendous promise for scalable diversified applications.In addition,scalable 2D TMD-based devices in terms of mass production,controllability,reproducibility,and low-cost have also been highlighted,showing the importance and benefits in modern industry.Finally,we summarize the remaining challenges and discuss the future directions of scalable 2D TMDs devices.
基金This work was supported by Dongguan University of Technology High-level Talents(Innovation Team)Research Project(KCYCXPT201603)Youth Innovative Talent Project from the Department of Education of Guangdong Province,China(2016KQNCX168)Natural Science Foundation of Guangdong Province,China(2018A0303130019).
文摘Tissue engineering is promising in realizing successful treatments of human body tissue loss that current methods cannot treat well or achieve satisfactory clinical outcomes.In scaffold-based bone tissue engineering,a high performance scaffold underpins the success of a bone tissue engineering strategy and a major direction in the field is to produce bone tissue engineering scaffolds with desirable shape,structural,physical,chemical and biological features for enhanced biological performance and for regenerating complex bone tissues.Three-dimensional(3D)printing can produce customized scaffolds that are highly desirable for bone tissue engineering.The enormous interest in 3D printing and 3D printed objects by the science,engineering and medical communities has led to various developments of the 3D printing technology and wide investigations of 3D printed products in many industries,including biomedical engineering,over the past decade.It is now possible to create novel bone tissue engineering scaffolds with customized shape,architecture,favorable macro-micro structure,wettability,mechanical strength and cellular responses.This article provides a concise review of recent advances in the R&D of 3D printing of bone tissue engineering scaffolds.It also presents our philosophy and research in the designing and fabrication of bone tissue engineering scaffolds through 3D printing.
文摘Diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest management.The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees.In this study,Dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection.Hyper-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with O.novo-ulmi under green-house conditions.Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes.Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED.Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees.In addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes.Though further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.
