Lightweight aluminum(Al)alloys have been widely used in frontier fields like aerospace and automotive industries,which attracts great interest in additive manufacturing(AM)to process high-value Al parts.As a mainstrea...Lightweight aluminum(Al)alloys have been widely used in frontier fields like aerospace and automotive industries,which attracts great interest in additive manufacturing(AM)to process high-value Al parts.As a mainstream AM technique,laser-directed energy deposition(LDED)shows good scalability to meet the requirements for large-format component manufacturing and repair.However,LDED Al alloys are highly challenging due to their inherent poor printability(e.g.low laser absorption,high oxidation sensitivity and cracking tendency).To further promote the development of LDED high-performance Al alloys,this review offers a deep understanding of the challenges and strategies to improve printability in LDED Al alloys.The porosity,cracking,distortion,inclusions,element evaporation and resultant inferior mechanical properties(worse than laser powder bed fusion)are the key challenges in LDED Al alloys.Processing parameter optimizations,in-situ alloy design,reinforcing particle addition and field assistance are the efficient approaches to improving the printability and performance of LDED Al alloys.The underlying correlations between processes,alloy innovation,characteristic microstructures,and achievable performances in LDED Al alloys are discussed.The benchmark mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized.This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys.Future opportunities and perspectives in LDED high-performance Al alloys are also outlined.展开更多
Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since...Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.展开更多
Plant height,spike,leaf,stem and grain morphologies are key components of plant architecture and related to wheat yield.A wheat(Triticum aestivum L.)mutant,wpa1,displaying temperaturedependent pleiotropic developmenta...Plant height,spike,leaf,stem and grain morphologies are key components of plant architecture and related to wheat yield.A wheat(Triticum aestivum L.)mutant,wpa1,displaying temperaturedependent pleiotropic developmental anomalies,was isolated.The WPA1 gene,encoding a von Willebrand factor type A(vWA)domain protein,was located on chromosome arm 7DS and isolated by map-based cloning.The functionality of WPA1 was validated by multiple independent EMS-induced mutants and gene editing.Phylogenetic analysis revealed that WPA1 is monocotyledon-specific in higher plants.The identification of WPA1 provides opportunity to study the temperature regulated wheat development and grain yield.展开更多
Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconst...Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.展开更多
基金supported by the 2022 MTC Young Individual Research Grants(Grant No.M22K3c0097)the Singapore Research,Innovation and Enterprise(RIE)2025 PlanSingapore Aerospace Programme Cycle 16(Grant No.M2215a0073)。
文摘Lightweight aluminum(Al)alloys have been widely used in frontier fields like aerospace and automotive industries,which attracts great interest in additive manufacturing(AM)to process high-value Al parts.As a mainstream AM technique,laser-directed energy deposition(LDED)shows good scalability to meet the requirements for large-format component manufacturing and repair.However,LDED Al alloys are highly challenging due to their inherent poor printability(e.g.low laser absorption,high oxidation sensitivity and cracking tendency).To further promote the development of LDED high-performance Al alloys,this review offers a deep understanding of the challenges and strategies to improve printability in LDED Al alloys.The porosity,cracking,distortion,inclusions,element evaporation and resultant inferior mechanical properties(worse than laser powder bed fusion)are the key challenges in LDED Al alloys.Processing parameter optimizations,in-situ alloy design,reinforcing particle addition and field assistance are the efficient approaches to improving the printability and performance of LDED Al alloys.The underlying correlations between processes,alloy innovation,characteristic microstructures,and achievable performances in LDED Al alloys are discussed.The benchmark mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized.This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys.Future opportunities and perspectives in LDED high-performance Al alloys are also outlined.
基金support from the National Natural Science Foundation of China (No.62005164,62222507,62175101,and 62005166)the Shanghai Natural Science Foundation (23ZR1443700)+3 种基金Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission (23SG41)the Young Elite Scientist Sponsorship Program by CAST (No.20220042)Science and Technology Commission of Shanghai Municipality (Grant No.21DZ1100500)the Shanghai Municipal Science and Technology Major Project,and the Shanghai Frontiers Science Center Program (2021-2025 No.20).
文摘Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.
基金supported by the Key Research and Development Program of Zhejiang(2024SSYS0099)the National Key Research and Development Program of China(2022YFD1200203)Key Research and Development Program of Hebei province(22326305D).
文摘Plant height,spike,leaf,stem and grain morphologies are key components of plant architecture and related to wheat yield.A wheat(Triticum aestivum L.)mutant,wpa1,displaying temperaturedependent pleiotropic developmental anomalies,was isolated.The WPA1 gene,encoding a von Willebrand factor type A(vWA)domain protein,was located on chromosome arm 7DS and isolated by map-based cloning.The functionality of WPA1 was validated by multiple independent EMS-induced mutants and gene editing.Phylogenetic analysis revealed that WPA1 is monocotyledon-specific in higher plants.The identification of WPA1 provides opportunity to study the temperature regulated wheat development and grain yield.
基金supported in part by the National Natural Science Foundation of China(Grants 62376172,62006163,62376043)in part by the National Postdoctoral Program for Innovative Talents(Grant BX20200226)in part by Sichuan Science and Technology Planning Project(Grants 2022YFSY0047,2022YFQ0014,2023ZYD0143,2022YFH0021,2023YFQ0020,24QYCX0354,24NSFTD0025).
文摘Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.
