Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-G...Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data.This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture(CUDA)but also improved the signal-to-noise ratio(SNR)through innovative stacking techniques,such as time-frequency domain phaseweighted stacking(tf-PWS).We validated the program using multiple datasets,confirming its superior computation speed,improved reliability,and higher signal-to-noise ratios for NCFs.Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions,thereby enhancing the precision and efficiency of ambient noise imaging.展开更多
[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Method...[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.展开更多
Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morpho...Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.展开更多
Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct in...Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.展开更多
Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing o...Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.展开更多
In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomogra...In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomography）is employed to quantitatively analyze the initial defects in four series of highperformance concrete （HPC）specimens with additions of different mineral admixtures. The nigh-resolution 3D images of microstructures and filtered defects are reconstructed by micro- CT software. The size distribution and volume fractions of initial defects are analyzed based on 3D and 2D micro-CT images. The analysis results are verified by experimental results of watersuction tests. The results show that the additions of mineral admixtures in concrete as cementitious materials greatly change the geometrical properties of the microstructures and the spatial features of defects by physical-chemistry actions of these mineral admixtures. This is the major cause of the differences between the mechanical behaviors of HPC with and without mineral admixtures when the water-to-binder ratio and the size distribution of aggregates are constant.展开更多
Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing ...Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.展开更多
The increasing demand for high-end equipment in crucial sectors such as aerospace,aeronautics, energy, power, information and electronics continues growing. However, the manufacturing of such advanced equipment poses ...The increasing demand for high-end equipment in crucial sectors such as aerospace,aeronautics, energy, power, information and electronics continues growing. However, the manufacturing of such advanced equipment poses significant challenges owing to high-level requirements for loading, transmission, conduction, energy conversion, and stealth. These challenges are amplified by complex structures, hard-to-cut materials, and strict standards for surface integrity and precision. To overcome these barriers in high-end equipment manufacturing, high-performance manufacturing(HPM) has emerged as an essential solution.This paper firstly discusses the key challenges in manufacturing technology and explores the essence of HPM, outlining a quantitative relationship between design and manufacturing.Subsequently, a generalized framework of HPM is proposed, accompanied by an in-depth exploration of the foundational elements and criteria. Ultimately, the feasible approaches and enabling technologies, supported by the analysis of two illustrative case studies are demonstrated. It is concluded that HPM is not just a precision and computational manufacturing framework with a core focus on multiparameter correlation in design, manufacturing, and service environments. It also represents a performance-geometry-integrated manufacturing framework for an accurate guarantee of the optimal performance.展开更多
Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer on...Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Mod...High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Model of the IAP/LASG （FAMIL） was comprehensively evaluated on Tianhe-2, which was the world＇s top-ranked supercomputer from June 2013 to May 2016. The standardized Atmospheric Model Inter-comparison Project （AMIP） type of experiment was carried out that focused on the computational performance of each node as well as the simulation year per day （SYPD）, the running cost speedup, and the scalability of the FAMIL. The results indicated that （1） based on five indexes （CPU usage, percentage of CPU kernel mode that occupies CPU time and of message passing waiting time （CPU SW）, code vectorization （VEC）, average of Gflops （Gflops_ AVE）, and peak of Gflops （Gflops_PK））, FAMIL shows excellent computational performance on every Tianhe-2 computing node; （2） considering SYPD and the cost speedup of FAMIL systematically, the optimal Message Passing Interface （MPI） numbers of processors （MNPs） choice appears when FAMIL use 384 and 1536 MNPs for C96 （100 km） and C384 （25 km）, respectively; and （3） FAMIL shows positive scalability with increased threads to drive the model. Considering the fast network speed and acceleration card in the MIC architecture on Tianhe-2, there is still significant room to improve the computational performance of FAMIL.展开更多
It is a difficult challenge to simultaneously employ the cationic and anionic redox chemistry in cathode materials for sodium-ion batteries with high energy.Even though layered oxides(classified as two-dimensional oxi...It is a difficult challenge to simultaneously employ the cationic and anionic redox chemistry in cathode materials for sodium-ion batteries with high energy.Even though layered oxides(classified as two-dimensional oxides)demonstrate excellent promise in the high discharge capacity,their poor oxygen transformation via redox reactions is limited by crystal instability.Therefore,a doping strategy was conceived to tackle this issue and increase redox efficiency.K doping was applied to transform the two-dimensional Na_(1.3)Mn_(0.7)O_(2)(NMO)to threedimensional K_(0.2)Na_(1.3)Mn_(0.5)O_(2)(KNMO),preventing the irreversible phase shift and preserving the crystal structure’s stability while cycling.With this modification treatment,KNMO features manganese and oxygen reactive sites,delivering a promising energy density of 190mAh·g^(-1)at 5 mA·g^(-1)in the 2.0–4.5 V voltage range(vs71.4 mAh·g^(-1)for the pristine NMO).Moreover,it displays improved capacity retention of more than 83.5%after 50cycles at 50 mA·g^(-1).The results demonstrated that doped intercalation oxides were promising for redox oxygen transformation in sodium-ion batteries.展开更多
Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible ligh...Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.展开更多
Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousa...Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousands of alarmed tech leaders recently signed an open letter to pause AI research to prepare for the catastrophic threats to humanity from uncontrolled AGI (Artificial General Intelligence). Perceived as an “epistemological nightmare”, AGI is believed to be on the anvil with GPT-5. Two computing rules appear responsible for these risks. 1) Mandatory third-party permissions that allow computers to run applications at the expense of introducing vulnerabilities. 2) The Halting Problem of Turing-complete AI programming languages potentially renders AGI unstoppable. The double whammy of these inherent weaknesses remains invincible under the legacy systems. A recent cybersecurity breakthrough shows that banning all permissions reduces the computer attack surface to zero, delivering a new zero vulnerability computing (ZVC) paradigm. Deploying ZVC and blockchain, this paper formulates and supports a hypothesis: “Safe, secure, ethical, controllable AGI/QC is possible by conquering the two unassailable rules of computability.” Pursued by a European consortium, testing/proving the proposed hypothesis will have a groundbreaking impact on the future digital infrastructure when AGI/QC starts powering the 75 billion internet devices by 2025.展开更多
Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switchi...Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
Carbendazim belongs to the benzimidazole fungicides,which can be used for control lots of fungi pathogens.High-performance liquid chromatography is frequently used for the analysis of carbendazim in all kinds of sampl...Carbendazim belongs to the benzimidazole fungicides,which can be used for control lots of fungi pathogens.High-performance liquid chromatography is frequently used for the analysis of carbendazim in all kinds of samples.In most occasions,the developed methods were applied for the simultaneous detection of a huge number of pesticides.Thus,an analytical method via UPLC-FLD was developed,and the sample preparation process was optimized by studying the effect of extraction solvent,approach,time and purification absorbent on the recovery rate of carbendazim.The results showed the optimized method for analysis was ultrasonication-assisted extraction with acetonitrile for 1 min,and subsequent purification by C18.In this occasion,the established analytical method of carbendazim in tomato samples displayed good linearity,accuracy and precision.展开更多
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ...Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
基金supported by the Key Research and Development Program of China(2021YFC3000704)Institute of Geophysics,China Earthquake Administration Grant DQJB23R18+1 种基金the USTC Research Funds of the Double First-Class Initiative(YD2080002012)NSFC Grant(U2239206)。
文摘Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data.This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture(CUDA)but also improved the signal-to-noise ratio(SNR)through innovative stacking techniques,such as time-frequency domain phaseweighted stacking(tf-PWS).We validated the program using multiple datasets,confirming its superior computation speed,improved reliability,and higher signal-to-noise ratios for NCFs.Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions,thereby enhancing the precision and efficiency of ambient noise imaging.
基金Supported by Regional Science Foundation of China,National Natural Science Foundation(No.82160820)General Program of Guizhou Provincial Natural Science Foundation[QianKeHe Foundation-ZK(2023)General153].
文摘[Objectives]To establish an HPLC method for the quantitative determination of multiple phenolic acid components in Tetracera asiatica medicinal material,providing a basis for establishing its quality standards.[Methods]An Inertsil ODS-C 18 column(250 mm×4.6 mm,5μm)was used.The mobile phase consisted of acetonitrile-0.2% phosphoric acid solution(10:90).The flow rate was 1.0 mL/min.The detection wavelength was 274 nm.The column temperature was 25℃.The injection volume was 10μL.The content of three components,gallic acid,protocatechuic acid,and protocatechualdehyde,was determined in 13 batches of T.asiatica.[Results]Gallic acid showed good linearity within the range of 0.020-6.400μg/mL,protocatechuic acid within 0.201-6.432μg/mL,and protocatechualdehyde within 0.202-6.464μg/mL(r>0.9990).The average recovery rates ranged from 98.61%to 101.17%,with RSD s between 1.21%and 2.69%.[Conclusions]The quantitative determination method established in this study is simple and feasible,and can provide a basis for the quality evaluation of T.asiatica.
基金the National Natural Science Foundation of China(No.22274021)Natural Science Foundation of Fujian Province(No.2022J01535)for financial support。
文摘Covalent organic frameworks(COFs)have demonstrated great potential in chromatographic separation because of unique structure and superior performance.Herein,single-crystal three-dimensional(3D)COFs with regular morphology,good monodispersity and high specific surface area,were used as a stationary phase for high-performance liquid chromatography(HPLC).The single-crystal 3D COFs packed column not only exhibits high efficiency in separating hydrophobic molecules involving substituted benzenes,halogenated benzenes,halogenated nitrobenzenes,aromatic amines,aromatic hydrocarbons(PAHs)and phthalate esters(PAEs),but also achieves baseline separation of acenaphthene and acenaphthylene with similar physical and chemical properties as well as environmental pollutants,which cannot be quickly separated on commercial C18 column and a polycrystalline 3D COFs packed column.Especially,the column efficiency of 17303-24255 plates/m was obtained for PAEs,and the resolution values for acenaphthene and acenaphthylene,and carbamazepine(CBZ)and carbamazepine-10,11-epoxide(CBZEP)were 1.7and 2.2,respectively.This successful application not only confirmed the great potential of the singlecrystal 3D COFs in HPLC separation of the organic molecules,but also facilitates the application of COFs in separation science.
基金supported by National Key Research and Development Program of China(Grant No.2022YFB3809000)Major Science and Technology Project of Gansu Province(Grant No.23ZDGA011)+1 种基金National Natural Science Foundation of China(Grant No.22275199,52105224)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB04701022021).
文摘Compared to subtractive manufacturing and casting,3D printing(additive manufacturing)offers advantages,such as the rapid production of complex structures,reduced material waste,and environmental friendliness.Direct ink writing(DIW)is one of the most popular 3D printing techniques owing to its ability to print multiple materials simultaneously and its high compatibility with printing inks.However,DIW presents significant challenges,particularly in the printing of high-performance polymers.The main challenges are as follows:1.The rigid structures and reaction kinetics of high-performance polymers make developing new inks difficult.2.The limited types of available high-performance polymers underscore the need for new DIW-suitable materials.3.Layer-by-layer stacking weakens interlayer bonding,affecting the mechanical properties of the printed product.4.The accuracy and speed of DIW printing are insufficient for large-scale manufacturing.After introducing the topic,the requirements for DIW printing inks are first reviewed,emphasizing the importance of thixotropic agents.Then,research progress regarding DIW printing of high-performance polymers is comprehensively reviewed according to the requirements of different polymer inks.Additionally,the applications of these materials across various fields are summarized.Finally,the challenges in DIW printing of high-performance polymers,along with corresponding solutions and future development prospects,are discussed in detail.
基金supported in part by the National Natural Science Foundation of China under Grant 52307134the Fundamental Research Funds for the Central Universities(xzy012025022)。
文摘Active distribution network(ADN)planning is crucial for achieving a cost-effective transition to modern power systems,yet it poses significant challenges as the system scale increases.The advent of quantum computing offers a transformative approach to solve ADN planning.To fully leverage the potential of quantum computing,this paper proposes a photonic quantum acceleration algorithm.First,a quantum-accelerated framework for ADN planning is proposed on the basis of coherent photonic quantum computers.The ADN planning model is then formulated and decomposed into discrete master problems and continuous subproblems to facilitate the quantum optimization process.The photonic quantum-embedded adaptive alternating direction method of multipliers(PQA-ADMM)algorithm is subsequently proposed to equivalently map the discrete master problem onto a quantum-interpretable model,enabling its deployment on a photonic quantum computer.Finally,a comparative analysis with various solvers,including Gurobi,demonstrates that the proposed PQA-ADMM algorithm achieves significant speedup on the modified IEEE 33-node and IEEE 123-node systems,highlighting its effectiveness.
基金The Scholarship Supported by Ministry of Education of China for Research Abroad(No.3037[2006])the Excellent Doctoral Dissertation Foundation of Southeast University (No.YBTJ-0512)the National Basic Research Program of China(973Program)(No.2009CB623203)
文摘In order to investigate the effects of two mineral admixtures （i. e., fly ash and ground slag）on initial defects existing in concrete microstructures, a high-resolution X-ray micro-CT（ micro-focus computer tomography）is employed to quantitatively analyze the initial defects in four series of highperformance concrete （HPC）specimens with additions of different mineral admixtures. The nigh-resolution 3D images of microstructures and filtered defects are reconstructed by micro- CT software. The size distribution and volume fractions of initial defects are analyzed based on 3D and 2D micro-CT images. The analysis results are verified by experimental results of watersuction tests. The results show that the additions of mineral admixtures in concrete as cementitious materials greatly change the geometrical properties of the microstructures and the spatial features of defects by physical-chemistry actions of these mineral admixtures. This is the major cause of the differences between the mechanical behaviors of HPC with and without mineral admixtures when the water-to-binder ratio and the size distribution of aggregates are constant.
基金supported by the National Natural the Science Foundation of China(51971042,51901028)the Chongqing Academician Special Fund(cstc2020yszxjcyj X0001)+1 种基金the China Scholarship Council(CSC)Norwegian University of Science and Technology(NTNU)for their financial and technical support。
文摘Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.
文摘The increasing demand for high-end equipment in crucial sectors such as aerospace,aeronautics, energy, power, information and electronics continues growing. However, the manufacturing of such advanced equipment poses significant challenges owing to high-level requirements for loading, transmission, conduction, energy conversion, and stealth. These challenges are amplified by complex structures, hard-to-cut materials, and strict standards for surface integrity and precision. To overcome these barriers in high-end equipment manufacturing, high-performance manufacturing(HPM) has emerged as an essential solution.This paper firstly discusses the key challenges in manufacturing technology and explores the essence of HPM, outlining a quantitative relationship between design and manufacturing.Subsequently, a generalized framework of HPM is proposed, accompanied by an in-depth exploration of the foundational elements and criteria. Ultimately, the feasible approaches and enabling technologies, supported by the analysis of two illustrative case studies are demonstrated. It is concluded that HPM is not just a precision and computational manufacturing framework with a core focus on multiparameter correlation in design, manufacturing, and service environments. It also represents a performance-geometry-integrated manufacturing framework for an accurate guarantee of the optimal performance.
基金supported by the Federal Railroad Administration (FRA)the National Academy of Science (NAS) IDEA program
文摘Railway inspection poses significant challenges due to the extensive use of various components in vast railway networks,especially in the case of high-speed railways.These networks demand high maintenance but offer only limited inspection windows.In response,this study focuses on developing a high-performance rail inspection system tailored for high-speed railways and railroads with constrained inspection timeframes.This system leverages the latest artificial intelligence advancements,incorporating YOLOv8 for detection.Our research introduces an efficient model inference pipeline based on a producer-consumer model,effectively utilizing parallel processing and concurrent computing to enhance performance.The deployment of this pipeline,implemented using C++,TensorRT,float16 quantization,and oneTBB,represents a significant departure from traditional sequential processing methods.The results are remarkable,showcasing a substantial increase in processing speed:from 38.93 Frames Per Second(FPS)to 281.06 FPS on a desktop system equipped with an Nvidia RTX A6000 GPU and from 19.50 FPS to 200.26 FPS on the Nvidia Jetson AGX Orin edge computing platform.This proposed framework has the potential to meet the real-time inspection requirements of high-speed railways.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金supported by the National Natural Science Foundation of China[grant number 41675100],[grant number91337110]the Third Tibetan Plateau Scientific Experiment:Observations for Boundary Layer and Troposphere[GYHY201406001]+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Science(CAS)(QYZDY-SSW-DQC018)the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund(the 2nd phase)
文摘High computational performance is extremely important for climate system models, especially in ultra-high-resolution model development. In this study, the computational performance of the Finite-volume Atmospheric Model of the IAP/LASG （FAMIL） was comprehensively evaluated on Tianhe-2, which was the world＇s top-ranked supercomputer from June 2013 to May 2016. The standardized Atmospheric Model Inter-comparison Project （AMIP） type of experiment was carried out that focused on the computational performance of each node as well as the simulation year per day （SYPD）, the running cost speedup, and the scalability of the FAMIL. The results indicated that （1） based on five indexes （CPU usage, percentage of CPU kernel mode that occupies CPU time and of message passing waiting time （CPU SW）, code vectorization （VEC）, average of Gflops （Gflops_ AVE）, and peak of Gflops （Gflops_PK））, FAMIL shows excellent computational performance on every Tianhe-2 computing node; （2） considering SYPD and the cost speedup of FAMIL systematically, the optimal Message Passing Interface （MPI） numbers of processors （MNPs） choice appears when FAMIL use 384 and 1536 MNPs for C96 （100 km） and C384 （25 km）, respectively; and （3） FAMIL shows positive scalability with increased threads to drive the model. Considering the fast network speed and acceleration card in the MIC architecture on Tianhe-2, there is still significant room to improve the computational performance of FAMIL.
基金financially supported by the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of SZPT(No.6021310029K)Research Projects of Department of Education of Guangdong Province(No.2023KTSCX319)。
文摘It is a difficult challenge to simultaneously employ the cationic and anionic redox chemistry in cathode materials for sodium-ion batteries with high energy.Even though layered oxides(classified as two-dimensional oxides)demonstrate excellent promise in the high discharge capacity,their poor oxygen transformation via redox reactions is limited by crystal instability.Therefore,a doping strategy was conceived to tackle this issue and increase redox efficiency.K doping was applied to transform the two-dimensional Na_(1.3)Mn_(0.7)O_(2)(NMO)to threedimensional K_(0.2)Na_(1.3)Mn_(0.5)O_(2)(KNMO),preventing the irreversible phase shift and preserving the crystal structure’s stability while cycling.With this modification treatment,KNMO features manganese and oxygen reactive sites,delivering a promising energy density of 190mAh·g^(-1)at 5 mA·g^(-1)in the 2.0–4.5 V voltage range(vs71.4 mAh·g^(-1)for the pristine NMO).Moreover,it displays improved capacity retention of more than 83.5%after 50cycles at 50 mA·g^(-1).The results demonstrated that doped intercalation oxides were promising for redox oxygen transformation in sodium-ion batteries.
文摘Photon-counting computed tomography(PCCT)represents a significant advancement in pediatric cardiovascular imaging.Traditional CT systems employ energy-integrating detectors that convert X-ray photons into visible light,whereas PCCT utilizes photon-counting detectors that directly transform X-ray photons into electric signals.This direct conversion allows photon-counting detectors to sort photons into discrete energy levels,thereby enhancing image quality through superior noise reduction,improved spatial and contrast resolution,and reduced artifacts.In pediatric applications,PCCT offers substantial benefits,including lower radiation doses,which may help reduce the risk of malignancy in pediatric patients,with perhaps greater potential to benefit those with repeated exposure from a young age.Enhanced spatial resolution facilitates better visualization of small structures,vital for diagnosing congenital heart defects.Additionally,PCCT’s spectral capabilities improve tissue characterization and enable the creation of virtual monoenergetic images,which enhance soft-tissue contrast and potentially reduce contrast media doses.Initial clinical results indicate that PCCT provides superior image quality and diagnostic accuracy compared to conven-tional CT,particularly in challenging pediatric cardiovascular cases.As PCCT technology matures,further research and standardized protocols will be essential to fully integrate it into pediatric imaging practices,ensuring optimized diagnostic outcomes and patient safety.
文摘Although AI and quantum computing (QC) are fast emerging as key enablers of the future Internet, experts believe they pose an existential threat to humanity. Responding to the frenzied release of ChatGPT/GPT-4, thousands of alarmed tech leaders recently signed an open letter to pause AI research to prepare for the catastrophic threats to humanity from uncontrolled AGI (Artificial General Intelligence). Perceived as an “epistemological nightmare”, AGI is believed to be on the anvil with GPT-5. Two computing rules appear responsible for these risks. 1) Mandatory third-party permissions that allow computers to run applications at the expense of introducing vulnerabilities. 2) The Halting Problem of Turing-complete AI programming languages potentially renders AGI unstoppable. The double whammy of these inherent weaknesses remains invincible under the legacy systems. A recent cybersecurity breakthrough shows that banning all permissions reduces the computer attack surface to zero, delivering a new zero vulnerability computing (ZVC) paradigm. Deploying ZVC and blockchain, this paper formulates and supports a hypothesis: “Safe, secure, ethical, controllable AGI/QC is possible by conquering the two unassailable rules of computability.” Pursued by a European consortium, testing/proving the proposed hypothesis will have a groundbreaking impact on the future digital infrastructure when AGI/QC starts powering the 75 billion internet devices by 2025.
基金supported in part by China Key Research and Development Program(2016YFA0201800)the National Natural Science Foundation of China(91964104,61974081)。
文摘Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
基金Supported by Youth Talent Project of Education Department Scientific Research Plan of Hubei Province(Q20232904).
文摘Carbendazim belongs to the benzimidazole fungicides,which can be used for control lots of fungi pathogens.High-performance liquid chromatography is frequently used for the analysis of carbendazim in all kinds of samples.In most occasions,the developed methods were applied for the simultaneous detection of a huge number of pesticides.Thus,an analytical method via UPLC-FLD was developed,and the sample preparation process was optimized by studying the effect of extraction solvent,approach,time and purification absorbent on the recovery rate of carbendazim.The results showed the optimized method for analysis was ultrasonication-assisted extraction with acetonitrile for 1 min,and subsequent purification by C18.In this occasion,the established analytical method of carbendazim in tomato samples displayed good linearity,accuracy and precision.
基金supported by the"Science and Technology Development Plan Project of Jilin Province,China"(Grant No.20240101018JJ)the Fundamental Research Funds for the Central Universities(Grant No.2412023YQ004)the National Natural Science Foundation of China(Grant Nos.52072065,52272140,52372137,and U23A20568).
文摘Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
