Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to en...Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.展开更多
The upregulation of 4-hydroxyphenylpyruvate dioxygenase(HPPD)can protect plants from adverse abiotic stress.Therefore,studying the changes of HPPD under abiotic stress is extremely valuable.In this study,we employed a...The upregulation of 4-hydroxyphenylpyruvate dioxygenase(HPPD)can protect plants from adverse abiotic stress.Therefore,studying the changes of HPPD under abiotic stress is extremely valuable.In this study,we employed a rational molecular design strategy to prepare an HPPD-responsive fluorescent probe consisting of a pyrene fluorophore,a linker and a benquitrione skeleton recognition moiety that functions via an aggregation–disaggregation sensing mechanism.As predicted,this probe exhibited an obvious turn-on fluorescence response towards HPPD.In addition,in vivo imaging demonstrated the excellent capability of this probe to track HPPD in Arabidopsis thaliana,and the dynamic changes in HPPD were monitored under different stress degrees of high temperature and Cadmium(II)ion(Cd^(2+))stress.This work provides an efficient design strategy for acquiring a noninvasive HPPD fluorescence probe,which could evaluate the degree of stress and be further employed for exploring the stress resistance mechanism in plants.展开更多
Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantage...Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.展开更多
The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and ...The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.展开更多
Seepage refers to the flow of water through porous materials.This phenomenon has a crucial role in dam,slope,excavation,tunnel,and well design.Performing seepage analysis usually is a challenging task,as one must cope...Seepage refers to the flow of water through porous materials.This phenomenon has a crucial role in dam,slope,excavation,tunnel,and well design.Performing seepage analysis usually is a challenging task,as one must cope with the uncertainty associated with the parameters such as the hydraulic conductivity in the horizontal and vertical directions that drive this phenomenon.However,at the same time,the data on horizontal and vertical hydraulic conductivities are typically scarce in spatial resolution.In this context,so-called non-traditional approaches for uncertainty quantification(such as intervals and fuzzy variables)offer an interesting alternative to classical probabilistic methods,since they have been shown to be quite effective when limited information on the governing parameters of a phenomenon is available.Therefore,the main contribution of this study is the development of a framework for conducting seepage analysis in saturated soils,where uncertainty associated with hydraulic conductivity is characterized using fuzzy fields.This method to characterize uncertainty extends interval fields towards the domain of fuzzy numbers.In fact,it is illustrated that fuzzy fields are an effective tool for capturing uncertainties with a spatial component,since they allow one to account for available physical measurements.A case study in confined saturated soil shows that with the proposed framework,it is possible to quantify the uncertainty associated with seepage flow,exit gradient,and uplift force effectively.展开更多
Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial applications.Graphene-based materials can a...Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial applications.Graphene-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like materials.In this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material system.We experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin film.Our proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.展开更多
In recent years,with the rapid development of China’s economy and the continuous improvement of people’s living standards,the number of motor vehicles and the number of drivers in the country have grown rapidly.Due ...In recent years,with the rapid development of China’s economy and the continuous improvement of people’s living standards,the number of motor vehicles and the number of drivers in the country have grown rapidly.Due to the increase in the number of vehicles and the number of motorists,the traffic accident rate is increasing,causing serious economic losses to society.According to the traffic accident statistics of the Ministry of Communications of China in 2009,more than 300,000 car accidents occurred in the year,most of which were caused by drunk driving.Therefore,this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system.The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system,combined with alcohol sensor MQ-3 circuit,LCD1602 liquid crystal display circuit,buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system.The main functions of the system are as follows:the alcohol sensor in the car detects the driver’s alcohol concentration value,and displays the value on the LCD screen.The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module.The system can effectively make up for the shortcomings of traffic police detection,which has certain research significance.展开更多
Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising techniqu...Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.展开更多
Two-photon polymerization(TPP)is a cutting-edge micro/nanoscale three-dimensional(3D)printing technology based on the principle of two-photon absorption.TPP surpasses the diffraction limit in achieving feature sizes a...Two-photon polymerization(TPP)is a cutting-edge micro/nanoscale three-dimensional(3D)printing technology based on the principle of two-photon absorption.TPP surpasses the diffraction limit in achieving feature sizes and excels in fabricating intricate 3D micro/nanostructures with exceptional resolution.The concept of 4D entails the fabrication of structures utilizing smart materials capable of undergoing shape,property,or functional changes in response to external stimuli over time.The integration of TPP and 4D printing introduces the possibility of producing responsive structures with micro/nanoscale accuracy,thereby enhancing the capabilities and potential applications of both technologies.This paper comprehensively reviews TPP-based 4D printing technology and its diverse applications.First,the working principles of TPP and its recent advancements are introduced.Second,the optional4D printing materials suitable for fabrication with TPP are discussed.Finally,this review paper highlights several noteworthy applications of TPP-based 4D printing,including domains such as biomedical microrobots,bioinspired microactuators,autonomous mobile microrobots,transformable devices and robots,as well as anti-counterfeiting microdevices.In conclusion,this paper provides valuable insights into the current status and future prospects of TPP-based4D printing technology,thereby serving as a guide for researchers and practitioners.展开更多
Layered magnetic materials,such as MnBi_(2)Te_(4),have drawn much attention owing to their potential for realizing twodimensional(2D)magnetism and possible topological states.Recently,FeBi_(2)Te_(4),which is isostruct...Layered magnetic materials,such as MnBi_(2)Te_(4),have drawn much attention owing to their potential for realizing twodimensional(2D)magnetism and possible topological states.Recently,FeBi_(2)Te_(4),which is isostructural to MnBi_(2)Te_(4),has been synthesized in experiments,but its detailed magnetic ordering and band topology have not been clearly understood yet.Here,based on first-principles calculations,we investigate the magnetic and electronic properties of FeBi_(2)Te_(4)in bulk and 2D forms.We show that different from MnBi_(2)Te_(4),the magnetic ground states of bulk,single-layer,and bilayer FeBi_(2)Te_(4)all favor a 120°noncollinear antiferromagnetic ordering,and they are topologically trivial narrow-gap semiconductors.For the bilayer case,we find that a quantum anomalous Hall effect with a unit Chern number is realized in the ferromagnetic state,which may be achieved in experiment by an external magnetic field or by magnetic proximity coupling.Our work clarifies the physical properties of the new material system of FeBi_(2)Te_(4)and reveals it as a potential platform for studying magnetic frustration down to 2D limit as well as quantum anomalous Hall effect.展开更多
The coupling between electric ordering and magnetic ordering in two-dimensional(2D)materials is important for both fundamental research of 2D multiferroics and future development of magnetism-based information storage...The coupling between electric ordering and magnetic ordering in two-dimensional(2D)materials is important for both fundamental research of 2D multiferroics and future development of magnetism-based information storage and operation.Here,we introduce a scheme for realizing a magnetic phase transition through the transition of electric ordering.We take CuMoP_(2)S_(6) monolayer as an example,which is a member of the large 2D transition-metal chalcogen-phosphates family.Based on first-principles calculations,we find that it is a multiferroic with unprecedented characters,namely,it exhibits two different phases:an antiferroelectric-antiferromagnetic phase and a ferroelectric-ferromagnetic phase,in which the electric and magnetic orderings are strongly coupled.Importantly,the electric polarization is out-of-plane,so the magnetism can be readily switched by using the gate electric field.Our finding reveals a series of 2D multiferroics with special magnetoelectric coupling,which hold great promise for experimental realization and practical applications.展开更多
Li metal anode holds great promise to realize high-energy battery systems.However,the safety issue and limited lifetime caused by the uncontrollable growth of Li dendrites hinder its commercial application.Herein,an i...Li metal anode holds great promise to realize high-energy battery systems.However,the safety issue and limited lifetime caused by the uncontrollable growth of Li dendrites hinder its commercial application.Herein,an interlayer-bridged 3D lithiophilic rGO-Ag-S-CNT composite is proposed to guide uniform and stable Li plating/stripping.The 3D lithiophilic rGO-Ag-S-CNT host is fabricated by incorporating Ag-modified reduced graphene oxide(rGO)with S-doped carbon nanotube(CNT),where the rGO and CNT are closely connected via robust Ag-S covalent bond.This strong Ag-S bond could enhance the structural stability and electrical connection between rGO and CNT,significantly improving the electrochemical kinetics and uniformity of current distribution.Moreover,density functional theory calculation indicates that the introduction of Ag-S bond could further boost the binding energy between Ag and Li,which promotes homogeneous Li nucleation and growth.Consequently,the rGO-Ag-S-CNT-based anode achieves a lower overpotential(7.3 mV at 0.5 mA cm^(−2)),higher Coulombic efficiency(98.1%at 0.5 mA cm^(−2)),and superior long cycling performance(over 500 cycles at 2 mA cm−2)as compared with the rGO-Ag-CNT-and rGO-CNT-based anodes.This work provides a universal avenue and guidance to build a robust Li metal host via constructing a strong covalent bond,effectively suppressing the Li dendrites growth to prompt the development of Li metal battery.展开更多
This paper reviews the state-of-the-art filter designs for 60 GHz applications. The most promising filter solutions at this frequency include fiher-in-package where the filter itself is design in the packaging platfor...This paper reviews the state-of-the-art filter designs for 60 GHz applications. The most promising filter solutions at this frequency include fiher-in-package where the filter itself is design in the packaging platform and filter-on-chip which is an on-chip filter co-design for miniaturized system size with low packaging cost. Design methodology, design technology, key performance parameters, similarities and dift）rences, advantages and drawbacks, and future trends are explored and studied. Filters in the printed circuit board （PCB）, low temperature co-fired ceramics （LTCC）, organie material, and bipolar complementary metal oxide semicomtuctor （BiCMOS） chips are summarized and compared in details. Future design trends and challenges are also given after the review.展开更多
The unique structural features of hard carbon(HC)make it a promising anode candidate for sodium-ion batteries(SIB).However,traditional methods of preparing HC require special equipment,long reaction times,and large en...The unique structural features of hard carbon(HC)make it a promising anode candidate for sodium-ion batteries(SIB).However,traditional methods of preparing HC require special equipment,long reaction times,and large energy consumption,resulting in low throughputs and efficiency.In our contribution,a novel synthesis method is proposed,involving the formation of HC nanosheets(NS-CNs)within minutes by creating an anoxic environment through flame combustion and further introducing sulfur and nitrogen sources to achieve heteroatom doping.The effect of heterogeneous element doping on the microstructure of HC is quantitatively analyzed by high-resolution transmission electron microscopy and image processing technology.Combined with density functional theory calculation,it is verified that the functionalized HC exhibits stronger Na^(+)adsorption ability,electron gain ability,and Na^(+) migration ability.As a result,NS-CNs as SIB anodes provide an ultrahigh reversible capacity of 542.7mAh g^(-1) at 0.1Ag^(-1),and excellent rate performance with a reversible capacity of 236.4mAh g^(-1) at 2Ag^(-1) after 1200 cycles.Furthermore,full cell assembled with NS-CNs as the can present 230mAh g^(-1) at 0.5Ag^(-1) after 150 cycles.Finally,in/ex situ techniques confirm that the excellent sodium storage properties of NS-CNs are due to the construction of abundant active sites based on the novel synthesis method for realizing the reversible adsorption of Na^(+).This work provides a novel strategy to develop novel carbons and gives deep insights for the further investigation of facile preparation methods to develop high-performance carbon anodes for alkali-ion batteries.展开更多
Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart gri...Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart grid DSA has received significant research interests in recent years.展开更多
Exploring low-cost and earth-abundant oxygen reduction reaction(ORR)electrocatalyst is essential for fuel cells and metal–air batteries.Among them,non-metal nanocarbon with multiple advantages of low cost,abundance,h...Exploring low-cost and earth-abundant oxygen reduction reaction(ORR)electrocatalyst is essential for fuel cells and metal–air batteries.Among them,non-metal nanocarbon with multiple advantages of low cost,abundance,high conductivity,good durability,and competitive activity has attracted intense interest in recent years.The enhanced ORR activities of the nanocarbons are normally thought to originate from heteroatom(e.g.,N,B,P,or S)doping or various induced defects.However,in practice,carbon-based materials usually contain both dopants and defects.In this regard,in terms of the co-engineering of heteroatom doping and defect inducing,we present an overview of recent advances in developing non-metal carbon-based electrocatalysts for the ORR.The characteristics,ORR performance,and the related mechanism of these functionalized nanocarbons by heteroatom doping,defect inducing,and in particular their synergistic promotion effect are emphatically analyzed and discussed.Finally,the current issues and perspectives in developing carbon-based electrocatalysts from both of heteroatom doping and defect engineering are proposed.This review will be beneficial for the rational design and manufacturing of highly efficient carbon-based materials for electrocatalysis.展开更多
The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With increasing improvements in design, reversible 4D printing or two-...The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With increasing improvements in design, reversible 4D printing or two-way 4D printing has been proven to be feasible. This technology will fully eliminate the need for human interference, as the programming is completely driven by external stimuli, which allows 4D-printed parts to be actuated in multiple cycles. This study proposes a new reversible 4D print- ing actuation method. The swelling of an elastomer and heat are used in the programming stage, and heat is used in the recovery stage. The main focus of this study is on the self-actuated programming step. To attain control over the bending, a simple predictive model has been developed to study the degree of cur- vature. The parameters, temperature, and elastomer thickness have also been studied in order to gain a better understanding of how well the model predicts the curvature. This understanding of the curvature will provide a great degree of control over the reversible 4D-printed structure.展开更多
The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational co...The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.展开更多
Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and an...Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。展开更多
Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usua...Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.展开更多
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grant No.2021B0909060002)National Natural Science Foundation of China(Grant Nos.62204219,62204140)+1 种基金Major Program of Natural Science Foundation of Zhejiang Province(Grant No.LDT23F0401)Thanks to Professor Zhang Yishu from Zhejiang University,Professor Gao Xu from Soochow University,and Professor Zhong Shuai from Guangdong Institute of Intelligence Science and Technology for their support。
文摘Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.
基金supported by the National Key R&D Program of China(No.2021YFD1700103)National Natural Science Foundation of China(21837001,21676113,21772054,22007035)+1 种基金the 111 Project B17019,the Scholar support program of CCNU(0900–31101090002)Hubei Province Natural Science Foundation(No.2020CFB487).
文摘The upregulation of 4-hydroxyphenylpyruvate dioxygenase(HPPD)can protect plants from adverse abiotic stress.Therefore,studying the changes of HPPD under abiotic stress is extremely valuable.In this study,we employed a rational molecular design strategy to prepare an HPPD-responsive fluorescent probe consisting of a pyrene fluorophore,a linker and a benquitrione skeleton recognition moiety that functions via an aggregation–disaggregation sensing mechanism.As predicted,this probe exhibited an obvious turn-on fluorescence response towards HPPD.In addition,in vivo imaging demonstrated the excellent capability of this probe to track HPPD in Arabidopsis thaliana,and the dynamic changes in HPPD were monitored under different stress degrees of high temperature and Cadmium(II)ion(Cd^(2+))stress.This work provides an efficient design strategy for acquiring a noninvasive HPPD fluorescence probe,which could evaluate the degree of stress and be further employed for exploring the stress resistance mechanism in plants.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grants No.2021B0909060002)National Natural Science Foundation of China(Grants No.62204219,62204140)Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401).
文摘Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.
文摘The growing demand for wireless connectivity has made massive multiple-input multiple-output(MIMO)a cornerstone of modern communication systems.To optimize network performance and resource allocation,an efficient and robust approach is joint device activity detection and channel estimation.In this paper,we present an approach utilizing score-based generative models to address the underdetermined nature of channel estimation,which is data-driven and well-suited for the complex and dynamic environment of massive MIMO systems.Our experimental results,based on a comprehensive dataset generated through Monte-Carlo sampling,demonstrate the high precision of our channel estimation approach,with errors reduced to as low as-45 d B,and exceptional accuracy in detecting active devices.
文摘Seepage refers to the flow of water through porous materials.This phenomenon has a crucial role in dam,slope,excavation,tunnel,and well design.Performing seepage analysis usually is a challenging task,as one must cope with the uncertainty associated with the parameters such as the hydraulic conductivity in the horizontal and vertical directions that drive this phenomenon.However,at the same time,the data on horizontal and vertical hydraulic conductivities are typically scarce in spatial resolution.In this context,so-called non-traditional approaches for uncertainty quantification(such as intervals and fuzzy variables)offer an interesting alternative to classical probabilistic methods,since they have been shown to be quite effective when limited information on the governing parameters of a phenomenon is available.Therefore,the main contribution of this study is the development of a framework for conducting seepage analysis in saturated soils,where uncertainty associated with hydraulic conductivity is characterized using fuzzy fields.This method to characterize uncertainty extends interval fields towards the domain of fuzzy numbers.In fact,it is illustrated that fuzzy fields are an effective tool for capturing uncertainties with a spatial component,since they allow one to account for available physical measurements.A case study in confined saturated soil shows that with the proposed framework,it is possible to quantify the uncertainty associated with seepage flow,exit gradient,and uplift force effectively.
基金Hongtao Wang acknowledges the support from National Key Research and Development Program of China(2017YFB0403602)China Scholarship Council.Baohua Jia acknowledges the support from the Australian Research Council through the Discovery Projects(DP150102972,DP190103186)+5 种基金the Industrial Transformation Training Centres scheme(Grant No.IC180100005)support from Defence Science Institute(DSI)and Defence Science and Technology Group(DSTG).C.W.Q.acknowledges the support from the National Research Foundation,Prime Minister’s Office,Singapore,under its Competitive Research Programme(CRP award NRF CRP22-2019-0006)Advanced Research and Technology Innovation Centre(ARTIC)under the grant(R-261-518-004-720)A STAR under Advanced Manufacturing and Engineering(AME)Individual Research Grant(IRG A2083c0060)Tian Lan acknowledges National Key Basic Research Program 973 Project(2013CB329202)National Major Scientific Instruments and Equipments Development Project supported by National Natural Science Foundation of China(No.61827814).
文摘Ultrathin flat metalenses have emerged as promising alternatives to conventional diffractive lenses,offering new possibilities for myriads of miniaturization and interfacial applications.Graphene-based materials can achieve both phase and amplitude modulations simultaneously at a single position due to the modification of the complex refractive index and thickness by laser conversion from graphene oxide into graphene like materials.In this work,we develop graphene oxide metalenses to precisely control phase and amplitude modulations and to achieve a holistic and systematic lens design based on a graphene-based material system.We experimentally validate our strategies via demonstrations of two graphene oxide metalenses:one with an ultra-long(~16λ)optical needle,and the other with axial multifocal spots,at the wavelength of 632.8 nm with a 200 nm thin film.Our proposed graphene oxide metalenses unfold unprecedented opportunities for accurately designing graphene-based ultrathin integratable devices for broad applications.
基金This work was financially supported by the National Natural Science Foundation(No.61806088)Jiangsu Province Industry-University-Research Cooperation Project(No.BY2018191)Natural Science Fund of Changzhou(CE20175026)and Qing Lan Project of Jiangsu Province.
文摘In recent years,with the rapid development of China’s economy and the continuous improvement of people’s living standards,the number of motor vehicles and the number of drivers in the country have grown rapidly.Due to the increase in the number of vehicles and the number of motorists,the traffic accident rate is increasing,causing serious economic losses to society.According to the traffic accident statistics of the Ministry of Communications of China in 2009,more than 300,000 car accidents occurred in the year,most of which were caused by drunk driving.Therefore,this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system.The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system,combined with alcohol sensor MQ-3 circuit,LCD1602 liquid crystal display circuit,buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system.The main functions of the system are as follows:the alcohol sensor in the car detects the driver’s alcohol concentration value,and displays the value on the LCD screen.The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module.The system can effectively make up for the shortcomings of traffic police detection,which has certain research significance.
文摘Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.
基金the National Natural Science Foundation of China(No.12072142)the Key Talent Recruitment Program of Guangdong Province(No.2019QN01Z438)+2 种基金the Science Technology and Innovation Commission of Shenzhen Municipality(ZDSYS20210623092005017)the China Postdoctoral Science Foundation(No.2022M721471)the Natural Science Foundation of Guangdong Province under the Grant(No.2022A1515010047)。
文摘Two-photon polymerization(TPP)is a cutting-edge micro/nanoscale three-dimensional(3D)printing technology based on the principle of two-photon absorption.TPP surpasses the diffraction limit in achieving feature sizes and excels in fabricating intricate 3D micro/nanostructures with exceptional resolution.The concept of 4D entails the fabrication of structures utilizing smart materials capable of undergoing shape,property,or functional changes in response to external stimuli over time.The integration of TPP and 4D printing introduces the possibility of producing responsive structures with micro/nanoscale accuracy,thereby enhancing the capabilities and potential applications of both technologies.This paper comprehensively reviews TPP-based 4D printing technology and its diverse applications.First,the working principles of TPP and its recent advancements are introduced.Second,the optional4D printing materials suitable for fabrication with TPP are discussed.Finally,this review paper highlights several noteworthy applications of TPP-based 4D printing,including domains such as biomedical microrobots,bioinspired microactuators,autonomous mobile microrobots,transformable devices and robots,as well as anti-counterfeiting microdevices.In conclusion,this paper provides valuable insights into the current status and future prospects of TPP-based4D printing technology,thereby serving as a guide for researchers and practitioners.
基金funding support from the Singapore MOE Ac RF 308 Tier 2(Grant No.T2EP50220-0026)funding support from Shandong Provincial Natural Science Foundation(Grant No.ZR2023QA012)+3 种基金the Special Fund-ing in the Project of Qilu Young Scholar Program of Shandong Universityfunding support from Australian Research Council Future Fellowship(Grant No.FT220100290)funding support from the AINSE postgraduate awardfunding support from the Research and Development Administration Office at the University of Macao(Grants Nos.MYRG2022-00088-IAPME and SRG2021-00003-IAPME)。
文摘Layered magnetic materials,such as MnBi_(2)Te_(4),have drawn much attention owing to their potential for realizing twodimensional(2D)magnetism and possible topological states.Recently,FeBi_(2)Te_(4),which is isostructural to MnBi_(2)Te_(4),has been synthesized in experiments,but its detailed magnetic ordering and band topology have not been clearly understood yet.Here,based on first-principles calculations,we investigate the magnetic and electronic properties of FeBi_(2)Te_(4)in bulk and 2D forms.We show that different from MnBi_(2)Te_(4),the magnetic ground states of bulk,single-layer,and bilayer FeBi_(2)Te_(4)all favor a 120°noncollinear antiferromagnetic ordering,and they are topologically trivial narrow-gap semiconductors.For the bilayer case,we find that a quantum anomalous Hall effect with a unit Chern number is realized in the ferromagnetic state,which may be achieved in experiment by an external magnetic field or by magnetic proximity coupling.Our work clarifies the physical properties of the new material system of FeBi_(2)Te_(4)and reveals it as a potential platform for studying magnetic frustration down to 2D limit as well as quantum anomalous Hall effect.
基金Supported by the National Key R&D Program of China(Grant No.2019YFE0112000)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LR21A040001)the National Natural Science Foundation of China(Grant No.11974307,12088101,11991060,and U1930402).
文摘The coupling between electric ordering and magnetic ordering in two-dimensional(2D)materials is important for both fundamental research of 2D multiferroics and future development of magnetism-based information storage and operation.Here,we introduce a scheme for realizing a magnetic phase transition through the transition of electric ordering.We take CuMoP_(2)S_(6) monolayer as an example,which is a member of the large 2D transition-metal chalcogen-phosphates family.Based on first-principles calculations,we find that it is a multiferroic with unprecedented characters,namely,it exhibits two different phases:an antiferroelectric-antiferromagnetic phase and a ferroelectric-ferromagnetic phase,in which the electric and magnetic orderings are strongly coupled.Importantly,the electric polarization is out-of-plane,so the magnetism can be readily switched by using the gate electric field.Our finding reveals a series of 2D multiferroics with special magnetoelectric coupling,which hold great promise for experimental realization and practical applications.
基金This work is supported by Singapore Ministry of Education academic research grant Tier 2 (MOE2019-T2-1-181).
文摘Li metal anode holds great promise to realize high-energy battery systems.However,the safety issue and limited lifetime caused by the uncontrollable growth of Li dendrites hinder its commercial application.Herein,an interlayer-bridged 3D lithiophilic rGO-Ag-S-CNT composite is proposed to guide uniform and stable Li plating/stripping.The 3D lithiophilic rGO-Ag-S-CNT host is fabricated by incorporating Ag-modified reduced graphene oxide(rGO)with S-doped carbon nanotube(CNT),where the rGO and CNT are closely connected via robust Ag-S covalent bond.This strong Ag-S bond could enhance the structural stability and electrical connection between rGO and CNT,significantly improving the electrochemical kinetics and uniformity of current distribution.Moreover,density functional theory calculation indicates that the introduction of Ag-S bond could further boost the binding energy between Ag and Li,which promotes homogeneous Li nucleation and growth.Consequently,the rGO-Ag-S-CNT-based anode achieves a lower overpotential(7.3 mV at 0.5 mA cm^(−2)),higher Coulombic efficiency(98.1%at 0.5 mA cm^(−2)),and superior long cycling performance(over 500 cycles at 2 mA cm−2)as compared with the rGO-Ag-CNT-and rGO-CNT-based anodes.This work provides a universal avenue and guidance to build a robust Li metal host via constructing a strong covalent bond,effectively suppressing the Li dendrites growth to prompt the development of Li metal battery.
文摘This paper reviews the state-of-the-art filter designs for 60 GHz applications. The most promising filter solutions at this frequency include fiher-in-package where the filter itself is design in the packaging platform and filter-on-chip which is an on-chip filter co-design for miniaturized system size with low packaging cost. Design methodology, design technology, key performance parameters, similarities and dift）rences, advantages and drawbacks, and future trends are explored and studied. Filters in the printed circuit board （PCB）, low temperature co-fired ceramics （LTCC）, organie material, and bipolar complementary metal oxide semicomtuctor （BiCMOS） chips are summarized and compared in details. Future design trends and challenges are also given after the review.
基金supported by the National Natural Science Foundation of China (Grant Nos.51872236,52072307)MOE SUTD Kickstarter Innitiative (SKI 2021_02_16).
文摘The unique structural features of hard carbon(HC)make it a promising anode candidate for sodium-ion batteries(SIB).However,traditional methods of preparing HC require special equipment,long reaction times,and large energy consumption,resulting in low throughputs and efficiency.In our contribution,a novel synthesis method is proposed,involving the formation of HC nanosheets(NS-CNs)within minutes by creating an anoxic environment through flame combustion and further introducing sulfur and nitrogen sources to achieve heteroatom doping.The effect of heterogeneous element doping on the microstructure of HC is quantitatively analyzed by high-resolution transmission electron microscopy and image processing technology.Combined with density functional theory calculation,it is verified that the functionalized HC exhibits stronger Na^(+)adsorption ability,electron gain ability,and Na^(+) migration ability.As a result,NS-CNs as SIB anodes provide an ultrahigh reversible capacity of 542.7mAh g^(-1) at 0.1Ag^(-1),and excellent rate performance with a reversible capacity of 236.4mAh g^(-1) at 2Ag^(-1) after 1200 cycles.Furthermore,full cell assembled with NS-CNs as the can present 230mAh g^(-1) at 0.5Ag^(-1) after 150 cycles.Finally,in/ex situ techniques confirm that the excellent sodium storage properties of NS-CNs are due to the construction of abundant active sites based on the novel synthesis method for realizing the reversible adsorption of Na^(+).This work provides a novel strategy to develop novel carbons and gives deep insights for the further investigation of facile preparation methods to develop high-performance carbon anodes for alkali-ion batteries.
基金supported in part by the Internal Talent Award with Wallenberg-NTU Presidential Postdoctoral Fellowship 2022the National Research Foundation,Singapore and DSO National Laboratories under the AI Singapore Program(AISG2-RP-2020-019)+1 种基金the Joint SDU-NTU Centre for AI Research(C-FAIR),the RIE 2020 Advanced Manufacturing and Engineering(AME)Programmatic Fund,Singapore(A20G8b0102)NOE Tier 1 Projects(RG59/22&RT9/22)。
文摘Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart grid DSA has received significant research interests in recent years.
基金the National Natural Science Foundation of China(51802104)Foundation of State Key Laboratory of Coal Combustion(FSKLCCA2008)State Key Laboratory of Advanced Technology for Materials Synthesis and Processing(Wuhan University of Technology)(2021-KF-4).
文摘Exploring low-cost and earth-abundant oxygen reduction reaction(ORR)electrocatalyst is essential for fuel cells and metal–air batteries.Among them,non-metal nanocarbon with multiple advantages of low cost,abundance,high conductivity,good durability,and competitive activity has attracted intense interest in recent years.The enhanced ORR activities of the nanocarbons are normally thought to originate from heteroatom(e.g.,N,B,P,or S)doping or various induced defects.However,in practice,carbon-based materials usually contain both dopants and defects.In this regard,in terms of the co-engineering of heteroatom doping and defect inducing,we present an overview of recent advances in developing non-metal carbon-based electrocatalysts for the ORR.The characteristics,ORR performance,and the related mechanism of these functionalized nanocarbons by heteroatom doping,defect inducing,and in particular their synergistic promotion effect are emphatically analyzed and discussed.Finally,the current issues and perspectives in developing carbon-based electrocatalysts from both of heteroatom doping and defect engineering are proposed.This review will be beneficial for the rational design and manufacturing of highly efficient carbon-based materials for electrocatalysis.
文摘The rapid development of additive manufacturing and advances in shape memory materials have fueled the progress of four-dimensional (4D) printing. With increasing improvements in design, reversible 4D printing or two-way 4D printing has been proven to be feasible. This technology will fully eliminate the need for human interference, as the programming is completely driven by external stimuli, which allows 4D-printed parts to be actuated in multiple cycles. This study proposes a new reversible 4D print- ing actuation method. The swelling of an elastomer and heat are used in the programming stage, and heat is used in the recovery stage. The main focus of this study is on the self-actuated programming step. To attain control over the bending, a simple predictive model has been developed to study the degree of cur- vature. The parameters, temperature, and elastomer thickness have also been studied in order to gain a better understanding of how well the model predicts the curvature. This understanding of the curvature will provide a great degree of control over the reversible 4D-printed structure.
基金supported by the National Natural Science Foundation of China(Grant Nos.52130805 and 52022070)Shanghai Science and Technology Committee Program(Grant No.20dz1202200)。
文摘The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils.
文摘Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB1600600。
文摘Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.
