In the modal analysis and control of nonlinear dynamical systems,participation factors(PFs)of state variables with respect to a critical or selected mode serve as a pivotal tool for simplifying stability studies by fo...In the modal analysis and control of nonlinear dynamical systems,participation factors(PFs)of state variables with respect to a critical or selected mode serve as a pivotal tool for simplifying stability studies by focusing on a subset of highly influential state variables.For linear systems,PFs are uniquely determined by the mode’s composition and shape,which are defined by the system’s left and right eigenvectors,respectively.However,the uniqueness of other types of PFs has not been thoroughly addressed in literatures.This paper establishes sufficient conditions for the uniqueness of nonlinear PFs and five other PF variants,taking into account uncertain scaling factors in a mode’s shape and composition.These scaling factors arise from variations in the choice of physical units,which depend on the value ranges of real-world state variables.Understanding these sufficient conditions is essential for the correct application of PFs in practical stability analysis and control design.展开更多
The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demon...The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demonstrated using various material combinations.One class of devices replicates the ion-concentrationgradient buildup that precedes neurotransmitter release in biological synapses.Some of these devices incorporate amino-acid-rich solutions as an active layer.This work presents a density functional theory study of such a device.The interaction between an Ag-filamentary memristor and different Hydrogen concentrations in a tyrosine-rich environment was evaluated.Two mutually exclusive structures were studied,and the resulting source-to-drain currents were compared with experimental observations.One structure was based on Tyrosine-H blocks linked to Ag atoms as a charge conduction path,while the other placed these blocks in parallel with Ag partial filaments between the source and drain.The results indicate that the second aligns with experiments and supports the hypothesis that tyrosine can act as an enabler for proton-mediated charge transport.Furthermore,the insights into the electronic transport properties of specific molecules can provide a theoretical background for designing advanced Hydrogen sensors and amino acid detectors.展开更多
Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat clima...Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.展开更多
Solving optimization problems plays a vital role in ensuring the secure and economic operation of distribution systems.To enhance computational efficiency,this paper proposes a general simplification and acceleration ...Solving optimization problems plays a vital role in ensuring the secure and economic operation of distribution systems.To enhance computational efficiency,this paper proposes a general simplification and acceleration method for distribution system optimization problems.Firstly,the capacity boundary and voltage boundary model of distribution systems are established.The relative position between the two boundaries reflects the strength of capacity and voltage constraints,leading to the definition of two critical feeder lengths(CFLs)to quantify these strengths.Secondly,simplification criteria and an acceleration method are proposed.Given a distribution system,if the distance from the end load/DG node to the slack bus is less than the corresponding CFL,we can conclude that the capacity constraints are stricter than the voltage constraints.Then,the distribution system can be simplified by adopting DC power flow model or disregarding the voltage constraints.After that,the reference value tables of CFL are presented.Finally,the effectiveness of the proposed method is verified by exemplifying the method in network reconfiguration and reactive power optimization problems.By implementing the proposed acceleration method,a significant reduction in computation time is achieved while ensuring accuracy.This method applies to most urban distribution systems in optimization problems involving power flow equations or voltage constraints.展开更多
Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑...Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment protocol.Compared with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space complexity.To tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)length.In this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings.Compared with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes.Particularly,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×smaller.Our constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge assumptions.We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.展开更多
Introduction:Seasonal influenza poses a significant public health burden,causing substantial morbidity and mortality worldwide each year.In this context,timely and accurate vaccine strain selection is critical to miti...Introduction:Seasonal influenza poses a significant public health burden,causing substantial morbidity and mortality worldwide each year.In this context,timely and accurate vaccine strain selection is critical to mitigating the impact of influenza outbreaks.This article aims to develop an adaptive,universal,and convenient method for predicting antigenic variation in influenza A(H1N1),thereby providing a scientific basis to enhance the biannual influenza vaccine selection process.Methods:The study integrates adaptive Fourier decomposition(AFD)theory with multiple techniques—including matching pursuit,the maximum selection principle,and bootstrapping—to investigate the complex nonlinear interactions between amino acid substitutions in hemagglutinin(HA)proteins(the primary antigenic protein of influenza virus)and their impact on antigenic changes.Results:Through comparative analysis with classical methods such as Lasso,Ridge,and random forest,we demonstrate that the AFD-type method offers superior accuracy and computational efficiency in identifying antigenic change-associated amino acid substitutions,thus eliminating the need for timeconsuming and expensive experimental procedures.AAW Conclusion:In summary,AFD-based methods represent effective mathematical models for predicting antigenic variations based on HA sequences and serological data,functioning as ensemble algorithms with guaranteed convergence.Following the sequence of indicators specified in I,we perform a series of operations on A_(1),including feature extension,extraction,and rearrangement,to generate a new input dataset for the prediction step.With this newly prepared input,we can compute the predicted results as.展开更多
Miniature fluorescence microscopes are a standard tool in systems biology.However,widefield miniature microscopes capture only 2D information,and modifications that enable 3D capabilities increase the size and weight ...Miniature fluorescence microscopes are a standard tool in systems biology.However,widefield miniature microscopes capture only 2D information,and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth range.Here,we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective’s aperture stop.Placing the phase mask at the aperture stop significantly reduces the size of the device,and varying the focal lengths enables a uniform resolution across a wide depth range.The phase mask encodes the 3D fluorescence intensity into a single 2D measurement,and the 3D volume is recovered by solving a sparsity-constrained inverse problem.We provide methods for designing and fabricating the phase mask and an efficient forward model that accounts for the fieldvarying aberrations in miniature objectives.We demonstrate a prototype that is 17mm tall and weighs 2.5 grams,achieving 2.76μm lateral,and 15μm axial resolution across most of the 900×700×390μm^(3) volume at 40 volumes per second.The performance is validated experimentally on resolution targets,dynamic biological samples,and mouse brain tissue.Compared with existing miniature single-shot volume-capture implementations,our system is smaller and lighter and achieves a more than 2×better lateral and axial resolution throughout a 10×larger usable depth range.Our microscope design provides single-shot 3D imaging for applications where a compact platform matters,such as volumetric neural imaging in freely moving animals and 3D motion studies of dynamic samples in incubators and lab-on-a-chip devices.展开更多
Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced ...Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.展开更多
Wearable devices have found numerous applications in healthcare ranging from physiological diseases,such as cardiovascular diseases,hypertension and muscle disorders to neurocognitive disorders,such as Parkinson’s di...Wearable devices have found numerous applications in healthcare ranging from physiological diseases,such as cardiovascular diseases,hypertension and muscle disorders to neurocognitive disorders,such as Parkinson’s disease,Alzheimer’s disease and other psychological diseases.Different types of wearables are used for this purpose,for example,skin-based wearables including tattoobased wearables,textile-based wearables,and biofluidic-based wearables.Recently,wearables have also shown encouraging improvements as a drug delivery system;therefore,enhancing its utility towards personalized healthcare.These wearables contain inherent challenges,which need to be addressed before their commercialization as a fully personalized healthcare system.This paper reviews different types of wearable devices currently being used in the healthcare field.It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices(HWDs)for diagnostic and treatment purposes.Additionally,current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.展开更多
It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials.However,predicting such effects is challenging due to the la...It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials.However,predicting such effects is challenging due to the large range of atomic configurations that are possible.Here,we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations.A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks.Bandgaps calculated by ab initio calculations,and corresponding structures,were used as training datasets.The trained networks were then used to predict bandgaps of systems with various configurations.For 4×4 and 5×5 supercells they accurately predict bandgaps,with a R^(2) of >90% and root-mean-square error of~0.1 eV.The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6×6 supercells.This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials.Moreover,given the ubiquitous existence of configurations in materials,this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.展开更多
Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material processing.However,the limitations of existing varifoc...Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material processing.However,the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality.The varifocal tools that are the least burdensome to operate(e.g.liquid crystal,elastomeric or optofluidic lenses)suffer from low(≈100 Hz)refresh rates.Conversely,the fastest devices sacrifice either critical capabilities such as their dwelling capacity(e.g.acoustic gradient lenses or monolithic micromechanical mirrors)or low operating overhead(e.g.deformable mirrors).Here,we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed,dwelling capacity and lightweight drive by employing lowrigidity micromirrors that exploit the robustness of defocusing phase profiles.Geometrically,the device consists of an 8.2mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2πphase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8μs(i.e.,15.44 kHz refresh rate).The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires<30 V per channel.Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth planes.Overall,the features of the proposed array offer the potential for compact,straightforward methods of tackling bottlenecked applications,including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.展开更多
Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated fro...Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in vitro.Through the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various disciplines.Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to stimuli.This synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic interventions.Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.展开更多
Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and biotechnology.Despite this...Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and biotechnology.Despite this role as gold-standard,staining protocols face several challenges,such as a need for extensive,manual processing of samples,substantial time delays,altered tissue homeostasis,limited choice of contrast agents,2D imaging instead of 3D tomography and many more.Label-free optical technologies,on the other hand,do not rely on exogenous and artificial markers,by exploiting intrinsic optical contrast mechanisms,where the specificity is typically less obvious to the human observer.Over the past few years,digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings.In this review article,we provide an in-depth analysis of the current state-of-the-art in this field,suggest methods of good practice,identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.展开更多
The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural engineering.Bioelectronic devices implanted t...The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural engineering.Bioelectronic devices implanted through dura mater and thick epineurium would ideally create minimal compression and acute damage as they reach the neurons of interest.We demonstrate that a three-dimensional diamond shuttle can be easily made with a vertical support to deliver ultra-compliant polymer microelectrodes(4.5-µm thick)through dura mater and thick epineurium.The diamond shuttle has 54%less cross-sectional area than an equivalently stiff silicon shuttle,which we simulated will result in a 37%reduction in blood vessel damage.We also discovered that higher frequency oscillation of the shuttle(200Hz)significantly reduced tissue compression regardless of the insertion speed,while slow speeds also independently reduced tissue compression.Insertion and recording performance are demonstrated in rat and feline models,but the large design space of these tools are suitable for research in a variety of animal models and nervous system targets.展开更多
We combined lightweight and mechanically flexible printed transistors and actuators with a paper unmanned aerial vehicle(UAV)glider prototype to demonstrate electrically controlled glide path modification in a lightwe...We combined lightweight and mechanically flexible printed transistors and actuators with a paper unmanned aerial vehicle(UAV)glider prototype to demonstrate electrically controlled glide path modification in a lightweight,disposable UAV system.The integration of lightweight and mechanically flexible electronics that is offered by printed electronics is uniquely attractive in this regard because it enables flight control in an inexpensive,disposable,and easily integrated system.Here,we demonstrate electroactive polymer(EAP)actuators that are directly printed into paper that act as steering elements for low cost,lightweight paper UAVs.We drive these actuators by using ion gel-gated organic thin film transistors(OTFTs)that are ideally suited as drive transistors for these actuators in terms of drive current and frequency requirements.By using a printing-based fabrication process on a paper glider,we are able to deliver an attractive path to the realization of inexpensive UAVs for ubiquitous sensing and monitoring flight applications.展开更多
We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the l...We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment.It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines.The architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various platforms.GranatumX can be run on the cloud or private servers and generate reproducible results.It is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench scientists.GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.展开更多
Unlike time-based path tracking controllers,theε-controller is a spatial path tracking controller.It is a purely geometric path tracking controller and essentially a P-controller to maintain the reasonable spatial di...Unlike time-based path tracking controllers,theε-controller is a spatial path tracking controller.It is a purely geometric path tracking controller and essentially a P-controller to maintain the reasonable spatial distance,ε,from the vehicle to the desired path.In this paper,we present some enhancement schemes using the non-conventional PI control laws via optimization.We propose to use a nonlinear termε^(1/3)for the proportional controller.A fractionalorder integral used to achieve a PI^(α)control.Among the schemes,an optimization search procedure applied to-nd optimal controller gains by meshing the regions around the values from approximate linear designs.The performance index for parametric optimization is the integration of the absolute purely spatial deviation from the desired path.Three different types of road shape were chosen and the Gazebo-ROS simulation results were presented to show the effectiveness of the proposed enhancement schemes.The results show that in some cases a smaller J_(area)and O_(ι)can be achieved by using P^(1/3)controller,but its disadvantage is there may be some oscillation.For PI^(α)controller,there is an additional adjustable parameterα,better performance can be achieved without signi-cant disadvantages which is worth in-depth research.展开更多
In this paper,we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles(UAVs),by spreading biochar mulch on top of the detected methane emissions area/source.Soil microorganisms...In this paper,we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles(UAVs),by spreading biochar mulch on top of the detected methane emissions area/source.Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming.However,people lack methods to manage soil methane emissions,especially quantification of methane emissions from the soil.Current measurement and suppression of methane methods are often limited due to the maintenance,installation,and calibration requirements of these sensing systems.To overcome these drawbacks,we present a new method called FADE-MAS2D(Fractional Advection Diffusion Mobile Actuator and Sensor)in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios(from single to multi-source disturbances)utilizing an anomalous diffusion model with different time,and space fractional orders subject to wind fields.This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation,and swarming UAVs can be applied to tackle the optimal coverage control issue.To simulate methane diffusion under the wind,we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation.In addition,we integrated emissions control,UAV control efforts,and UAV location error in our cost function.Finally,we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain,respectively.The results show that when α=0.8 and β=1.8,the shape and emissions of methane perform well.Furthermore,our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.展开更多
Atomic layers of hexagonal boron nitride(h-BN)crystal are excellent candidates for structural materials as enabling ultrathin,two-dimensional(2D)nanoelectromechanical systems(NEMS)due to the outstanding mechanical pro...Atomic layers of hexagonal boron nitride(h-BN)crystal are excellent candidates for structural materials as enabling ultrathin,two-dimensional(2D)nanoelectromechanical systems(NEMS)due to the outstanding mechanical properties and very wide bandgap(5.9 eV)of h-BN.In this work,we report the experimental demonstration of h-BN 2D nanomechanical resonators vibrating at high and very high frequencies(from~5 to~70 MHz),and investigations of the elastic properties of h-BN by measuring the multimode resonant behavior of these devices.First,we demonstrate a dry-transferred doubly clamped h-BN membrane with~6.7 nm thickness,the thinnest h-BN resonator known to date.In addition,we fabricate circular drumhead h-BN resonators with thicknesses ranging from~9 to 292 nm,from which we measure up to eight resonance modes in the range of~18 to 35 MHz.Combining measurements and modeling of the rich multimode resonances,we resolve h-BN’s elastic behavior,including the transition from membrane to disk regime,with built-in tension ranging from 0.02 to 2 N m−1.The Young’s modulus of h-BN is determined to be EY≈392 GPa from the measured resonances.The ultrasensitive measurements further reveal subtle structural characteristics and mechanical properties of the suspended h-BN diaphragms,including anisotropic built-in tension and bulging,thus suggesting guidelines on how these effects can be exploited for engineering multimode resonant functions in 2D NEMS transducers.展开更多
文摘In the modal analysis and control of nonlinear dynamical systems,participation factors(PFs)of state variables with respect to a critical or selected mode serve as a pivotal tool for simplifying stability studies by focusing on a subset of highly influential state variables.For linear systems,PFs are uniquely determined by the mode’s composition and shape,which are defined by the system’s left and right eigenvectors,respectively.However,the uniqueness of other types of PFs has not been thoroughly addressed in literatures.This paper establishes sufficient conditions for the uniqueness of nonlinear PFs and five other PF variants,taking into account uncertain scaling factors in a mode’s shape and composition.These scaling factors arise from variations in the choice of physical units,which depend on the value ranges of real-world state variables.Understanding these sufficient conditions is essential for the correct application of PFs in practical stability analysis and control design.
文摘The development of electronic circuits designed to emulate the functionality of biological neural networks has increased significantly in recent years.Specifically,memristor-based neuromorphic operation has been demonstrated using various material combinations.One class of devices replicates the ion-concentrationgradient buildup that precedes neurotransmitter release in biological synapses.Some of these devices incorporate amino-acid-rich solutions as an active layer.This work presents a density functional theory study of such a device.The interaction between an Ag-filamentary memristor and different Hydrogen concentrations in a tyrosine-rich environment was evaluated.Two mutually exclusive structures were studied,and the resulting source-to-drain currents were compared with experimental observations.One structure was based on Tyrosine-H blocks linked to Ag atoms as a charge conduction path,while the other placed these blocks in parallel with Ag partial filaments between the source and drain.The results indicate that the second aligns with experiments and supports the hypothesis that tyrosine can act as an enabler for proton-mediated charge transport.Furthermore,the insights into the electronic transport properties of specific molecules can provide a theoretical background for designing advanced Hydrogen sensors and amino acid detectors.
基金supported by SGC project5 entitled"Mobile Biochar Production for Methane Emission Reduction and Soil Amendment".Grant Agreement#CCR20014supported in part by NSF CBET#1856112supported in part by an F3 R&D GSR Award (Farms Food Future Innovation Initiative (or F3),as funded by US Dept.of Commerce,Economic Development Administration Build Back Better Regional Challenge).
文摘Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.
基金supported by the National Natural Science Foundation of China(No.52177105).
文摘Solving optimization problems plays a vital role in ensuring the secure and economic operation of distribution systems.To enhance computational efficiency,this paper proposes a general simplification and acceleration method for distribution system optimization problems.Firstly,the capacity boundary and voltage boundary model of distribution systems are established.The relative position between the two boundaries reflects the strength of capacity and voltage constraints,leading to the definition of two critical feeder lengths(CFLs)to quantify these strengths.Secondly,simplification criteria and an acceleration method are proposed.Given a distribution system,if the distance from the end load/DG node to the slack bus is less than the corresponding CFL,we can conclude that the capacity constraints are stricter than the voltage constraints.Then,the distribution system can be simplified by adopting DC power flow model or disregarding the voltage constraints.After that,the reference value tables of CFL are presented.Finally,the effectiveness of the proposed method is verified by exemplifying the method in network reconfiguration and reactive power optimization problems.By implementing the proposed acceleration method,a significant reduction in computation time is achieved while ensuring accuracy.This method applies to most urban distribution systems in optimization problems involving power flow equations or voltage constraints.
基金supported by the National Key R&D Program of China under Grant 2020YFA0712303Zhedong Wang is supported by National Natural Science Foundation of China(Grant No.62202305)Shanghai Pujiang Program under Grant 22PJ1407700.
文摘Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment protocol.Compared with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space complexity.To tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)length.In this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings.Compared with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes.Particularly,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×smaller.Our constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge assumptions.We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.
基金Supported by Major Project of Guangzhou National Laboratory,(Grant No.GZNL2024A01004)the National Natural Science Foundation of China(Grant No.82361168672)+4 种基金the Science and Technology Development Fund of Macao SAR(Grant No.FDCT 0111/2023/AFJ,0155/2024/RIA2,005/2022/ALC,0128/2022/A,0020/2023/RIB1)National Key Research and Development Program of China(Grant No.2024YFE0214800)Self-supporting Program of Guangzhou Laboratory(Grant No.SRPG22-007)National Key Research and Development Program of China(Grant No.SQ2024YFE0202244)Engineering Technology Research(Development)Center of Ordinary Colleges and Universities in Guangdong Province(Grant No.2024GCZX010).
文摘Introduction:Seasonal influenza poses a significant public health burden,causing substantial morbidity and mortality worldwide each year.In this context,timely and accurate vaccine strain selection is critical to mitigating the impact of influenza outbreaks.This article aims to develop an adaptive,universal,and convenient method for predicting antigenic variation in influenza A(H1N1),thereby providing a scientific basis to enhance the biannual influenza vaccine selection process.Methods:The study integrates adaptive Fourier decomposition(AFD)theory with multiple techniques—including matching pursuit,the maximum selection principle,and bootstrapping—to investigate the complex nonlinear interactions between amino acid substitutions in hemagglutinin(HA)proteins(the primary antigenic protein of influenza virus)and their impact on antigenic changes.Results:Through comparative analysis with classical methods such as Lasso,Ridge,and random forest,we demonstrate that the AFD-type method offers superior accuracy and computational efficiency in identifying antigenic change-associated amino acid substitutions,thus eliminating the need for timeconsuming and expensive experimental procedures.AAW Conclusion:In summary,AFD-based methods represent effective mathematical models for predicting antigenic variations based on HA sequences and serological data,functioning as ensemble algorithms with guaranteed convergence.Following the sequence of indicators specified in I,we perform a series of operations on A_(1),including feature extension,extraction,and rearrangement,to generate a new input dataset for the prediction step.With this newly prepared input,we can compute the predicted results as.
基金supported in part by the Defense Advanced Research Projects Agency(DARPA),contract no.N66001-17-C-4015Gordon and Betty Moore Foundation Data-Driven Discovery Initiative(grant GBMF4562)+3 种基金National Institutes of Health(NIH)grant 1R21EY027597-01the National Science Foundation(grant no.1617794)an Alfred P.Sloan Foundation fellowshipfunding from the National Science Foundation Graduate Research Fellowship Program(NSF GRFP).
文摘Miniature fluorescence microscopes are a standard tool in systems biology.However,widefield miniature microscopes capture only 2D information,and modifications that enable 3D capabilities increase the size and weight and have poor resolution outside a narrow depth range.Here,we achieve the 3D capability by replacing the tube lens of a conventional 2D Miniscope with an optimized multifocal phase mask at the objective’s aperture stop.Placing the phase mask at the aperture stop significantly reduces the size of the device,and varying the focal lengths enables a uniform resolution across a wide depth range.The phase mask encodes the 3D fluorescence intensity into a single 2D measurement,and the 3D volume is recovered by solving a sparsity-constrained inverse problem.We provide methods for designing and fabricating the phase mask and an efficient forward model that accounts for the fieldvarying aberrations in miniature objectives.We demonstrate a prototype that is 17mm tall and weighs 2.5 grams,achieving 2.76μm lateral,and 15μm axial resolution across most of the 900×700×390μm^(3) volume at 40 volumes per second.The performance is validated experimentally on resolution targets,dynamic biological samples,and mouse brain tissue.Compared with existing miniature single-shot volume-capture implementations,our system is smaller and lighter and achieves a more than 2×better lateral and axial resolution throughout a 10×larger usable depth range.Our microscope design provides single-shot 3D imaging for applications where a compact platform matters,such as volumetric neural imaging in freely moving animals and 3D motion studies of dynamic samples in incubators and lab-on-a-chip devices.
文摘Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.
基金support from NSF CAREER Award 1942487,NIH R15AI127214the seed award from I-SENSE Institute,and College of Engineering and Computer Science,Florida Atlantic University,Boca Raton,FL.
文摘Wearable devices have found numerous applications in healthcare ranging from physiological diseases,such as cardiovascular diseases,hypertension and muscle disorders to neurocognitive disorders,such as Parkinson’s disease,Alzheimer’s disease and other psychological diseases.Different types of wearables are used for this purpose,for example,skin-based wearables including tattoobased wearables,textile-based wearables,and biofluidic-based wearables.Recently,wearables have also shown encouraging improvements as a drug delivery system;therefore,enhancing its utility towards personalized healthcare.These wearables contain inherent challenges,which need to be addressed before their commercialization as a fully personalized healthcare system.This paper reviews different types of wearable devices currently being used in the healthcare field.It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices(HWDs)for diagnostic and treatment purposes.Additionally,current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.
基金J.L.acknowledges financial support from University of Missouri-Columbia start-up fund,NASA Missouri Space Consortium(Project:00049784)Unite States Department of Agriculture(Award number:2018-67017-27880)+2 种基金This material is based upon work partially supported by the Department of Energy National Energy Technology Laboratory under Award Number DE-FE0031645J.C.acknowledges National Science Foundation(Award numbers:DBI1759934 and IIS1763246)The computations were performed on the HPC resources at the University of Missouri Bioinformatics Consortium(UMBC),supported in part by NSF(award number:1429294).
文摘It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials.However,predicting such effects is challenging due to the large range of atomic configurations that are possible.Here,we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations.A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks.Bandgaps calculated by ab initio calculations,and corresponding structures,were used as training datasets.The trained networks were then used to predict bandgaps of systems with various configurations.For 4×4 and 5×5 supercells they accurately predict bandgaps,with a R^(2) of >90% and root-mean-square error of~0.1 eV.The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6×6 supercells.This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials.Moreover,given the ubiquitous existence of configurations in materials,this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.
基金the McKnight Technological Innovations in Neuroscience Award as well as the Burroughs Wellcome Fund Career Award At the Scientific Interface(5113244)to N.Psupported by the U.S.Department of Energy,Office of Science,under Contract No.DE-AC02-06CH11357.
文摘Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material processing.However,the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality.The varifocal tools that are the least burdensome to operate(e.g.liquid crystal,elastomeric or optofluidic lenses)suffer from low(≈100 Hz)refresh rates.Conversely,the fastest devices sacrifice either critical capabilities such as their dwelling capacity(e.g.acoustic gradient lenses or monolithic micromechanical mirrors)or low operating overhead(e.g.deformable mirrors).Here,we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed,dwelling capacity and lightweight drive by employing lowrigidity micromirrors that exploit the robustness of defocusing phase profiles.Geometrically,the device consists of an 8.2mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2πphase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8μs(i.e.,15.44 kHz refresh rate).The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires<30 V per channel.Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth planes.Overall,the features of the proposed array offer the potential for compact,straightforward methods of tackling bottlenecked applications,including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.
基金NIH[R01HD101130,R15HD108720]NSF[CMMI-2130192,CBET-1943798]Research Seed Grants(2021 and 2023)from UNT Research and Innovation Office(H.X.Y.),Syracuse University intramural CUSE grant[II-3245-2022](Z.M.).
文摘Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in vitro.Through the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various disciplines.Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to stimuli.This synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic interventions.Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.
基金This project has received funding from the European Union’s Horizon 2022 Marie Skłodowska-Curie Action(grant agreement 101103200,‘MICS’to L.K.)K.C.Z.was supported in part by Schmidt Science Fellows,in partnership with the Rhodes Trust+2 种基金K.C.L.was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(grant number:HI21C0977060102002)Commercialization Promotion Agency for R&D Outcomes(COMPA)funded by the Ministry of Science and ICT(MSIT)(1711198540)This material is based upon work supported in part by the Air Force Office of Scientific Research under award number FA9550-21-1-0401,the National Science Foundation under Grant 2238845,and a Hartwell Foundation Individual Biomedical Researcher Award.
文摘Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and biotechnology.Despite this role as gold-standard,staining protocols face several challenges,such as a need for extensive,manual processing of samples,substantial time delays,altered tissue homeostasis,limited choice of contrast agents,2D imaging instead of 3D tomography and many more.Label-free optical technologies,on the other hand,do not rely on exogenous and artificial markers,by exploiting intrinsic optical contrast mechanisms,where the specificity is typically less obvious to the human observer.Over the past few years,digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings.In this review article,we provide an in-depth analysis of the current state-of-the-art in this field,suggest methods of good practice,identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.
基金This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health(R21EB020811,and SPARC program Awards U18EB021760,OT2OD024907,and OT2OD023873)Kavli Foundation funding,and Seed Funding for Innovative Projects in Neuroscience from the University of Michigan Brain Initiative Working Group(MiBrain).
文摘The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural engineering.Bioelectronic devices implanted through dura mater and thick epineurium would ideally create minimal compression and acute damage as they reach the neurons of interest.We demonstrate that a three-dimensional diamond shuttle can be easily made with a vertical support to deliver ultra-compliant polymer microelectrodes(4.5-µm thick)through dura mater and thick epineurium.The diamond shuttle has 54%less cross-sectional area than an equivalently stiff silicon shuttle,which we simulated will result in a 37%reduction in blood vessel damage.We also discovered that higher frequency oscillation of the shuttle(200Hz)significantly reduced tissue compression regardless of the insertion speed,while slow speeds also independently reduced tissue compression.Insertion and recording performance are demonstrated in rat and feline models,but the large design space of these tools are suitable for research in a variety of animal models and nervous system targets.
文摘We combined lightweight and mechanically flexible printed transistors and actuators with a paper unmanned aerial vehicle(UAV)glider prototype to demonstrate electrically controlled glide path modification in a lightweight,disposable UAV system.The integration of lightweight and mechanically flexible electronics that is offered by printed electronics is uniquely attractive in this regard because it enables flight control in an inexpensive,disposable,and easily integrated system.Here,we demonstrate electroactive polymer(EAP)actuators that are directly printed into paper that act as steering elements for low cost,lightweight paper UAVs.We drive these actuators by using ion gel-gated organic thin film transistors(OTFTs)that are ideally suited as drive transistors for these actuators in terms of drive current and frequency requirements.By using a printing-based fabrication process on a paper glider,we are able to deliver an attractive path to the realization of inexpensive UAVs for ubiquitous sensing and monitoring flight applications.
基金This research was supported by grants from the National Institute of Environmental Health Sciences(NIEHS)through funds provided by the trans-NIH Big Data to Knowledge(BD2K)initiative(www.bd2k.nih.govGrant No.K01ES025434)+4 种基金the National Institutes of Health/National Institute of General Medical Sciences(NIH/NIGMSGrant No.P20 COBRE GM103457)the National Library of Medicine(NLMGrant No.R01 LM012373)the National Institute of Child Health and Human Development(NICHD,Grant No.R01 HD084633)awarded to LXG.
文摘We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data analysis.GranatumX is inspired by the interactive webtool Granatum.GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment.It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines.The architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various platforms.GranatumX can be run on the cloud or private servers and generate reproducible results.It is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench scientists.GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.
文摘Unlike time-based path tracking controllers,theε-controller is a spatial path tracking controller.It is a purely geometric path tracking controller and essentially a P-controller to maintain the reasonable spatial distance,ε,from the vehicle to the desired path.In this paper,we present some enhancement schemes using the non-conventional PI control laws via optimization.We propose to use a nonlinear termε^(1/3)for the proportional controller.A fractionalorder integral used to achieve a PI^(α)control.Among the schemes,an optimization search procedure applied to-nd optimal controller gains by meshing the regions around the values from approximate linear designs.The performance index for parametric optimization is the integration of the absolute purely spatial deviation from the desired path.Three different types of road shape were chosen and the Gazebo-ROS simulation results were presented to show the effectiveness of the proposed enhancement schemes.The results show that in some cases a smaller J_(area)and O_(ι)can be achieved by using P^(1/3)controller,but its disadvantage is there may be some oscillation.For PI^(α)controller,there is an additional adjustable parameterα,better performance can be achieved without signi-cant disadvantages which is worth in-depth research.
基金support by a CSC Scholarship.DH is supported by an NSF NRT Fellowship-Grant DGE 1633722.
文摘In this paper,we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles(UAVs),by spreading biochar mulch on top of the detected methane emissions area/source.Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming.However,people lack methods to manage soil methane emissions,especially quantification of methane emissions from the soil.Current measurement and suppression of methane methods are often limited due to the maintenance,installation,and calibration requirements of these sensing systems.To overcome these drawbacks,we present a new method called FADE-MAS2D(Fractional Advection Diffusion Mobile Actuator and Sensor)in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios(from single to multi-source disturbances)utilizing an anomalous diffusion model with different time,and space fractional orders subject to wind fields.This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation,and swarming UAVs can be applied to tackle the optimal coverage control issue.To simulate methane diffusion under the wind,we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation.In addition,we integrated emissions control,UAV control efforts,and UAV location error in our cost function.Finally,we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain,respectively.The results show that when α=0.8 and β=1.8,the shape and emissions of methane perform well.Furthermore,our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.
基金We are grateful for support from the National Academy of Engineering(NAE)Grainger Foundation Frontier of Engineering(FOE)Award(FOE2013-005)the National Science Foundation CAREER Award(Grant ECCS-1454570)partial support from the Department of Energy(DOE)EERE Award(Grant DE-EE0006719),a ThinkEnergy Fellowship(X.-Q.Zheng),and the Case School of Engineering.A portion of the device fabrication was performed at the Cornell NanoScale Science and Technology Facility(CNF),a member of the National Nanotechnology Infrastructure Network(NNIN)supported by the National Science Foundation(Grant ECCS-0335765).
文摘Atomic layers of hexagonal boron nitride(h-BN)crystal are excellent candidates for structural materials as enabling ultrathin,two-dimensional(2D)nanoelectromechanical systems(NEMS)due to the outstanding mechanical properties and very wide bandgap(5.9 eV)of h-BN.In this work,we report the experimental demonstration of h-BN 2D nanomechanical resonators vibrating at high and very high frequencies(from~5 to~70 MHz),and investigations of the elastic properties of h-BN by measuring the multimode resonant behavior of these devices.First,we demonstrate a dry-transferred doubly clamped h-BN membrane with~6.7 nm thickness,the thinnest h-BN resonator known to date.In addition,we fabricate circular drumhead h-BN resonators with thicknesses ranging from~9 to 292 nm,from which we measure up to eight resonance modes in the range of~18 to 35 MHz.Combining measurements and modeling of the rich multimode resonances,we resolve h-BN’s elastic behavior,including the transition from membrane to disk regime,with built-in tension ranging from 0.02 to 2 N m−1.The Young’s modulus of h-BN is determined to be EY≈392 GPa from the measured resonances.The ultrasensitive measurements further reveal subtle structural characteristics and mechanical properties of the suspended h-BN diaphragms,including anisotropic built-in tension and bulging,thus suggesting guidelines on how these effects can be exploited for engineering multimode resonant functions in 2D NEMS transducers.
