Pb-Sn mixed perovskite solar cells(PSCs)are crucial components for realizing efficient all-perovskite tandem devices.However,their efficiency and stability are severely limited by oxidative degradation(Sn^(4+)formatio...Pb-Sn mixed perovskite solar cells(PSCs)are crucial components for realizing efficient all-perovskite tandem devices.However,their efficiency and stability are severely limited by oxidative degradation(Sn^(4+)formation)and metallic defects(Sn^(0)/Pb^(0)).In addition,the rapid and uncontrolled Sn^(2+)nucleation kinetics result in nonuniform crystallization.Herein,we introduce a natural redox shuttle glutathione(GSH)in Pb-Sn mixed PSCs,achieving regenerable antioxidation and crystallization regulation simultaneously.The reversible redox reactions between GSH and glutathione disulfide(GSSG)enable the self-healing of Sn^(4+)and Sn^(0)/Pb^(0)impurities,creating a regenerable antioxidation protective shell at the perovskite interfaces.Meanwhile,the strong coordination between GSH and perovskite regulates the crystallization process,optimizing the nucleation and crystallization kinetics.Furthermore,the GSH incorporation creates a high-quality charge separation junction at the perovskite/hole transport layer,facilitating carrier separation and extraction.The optimized Pb-Sn PSCs exhibit impressive power conversion efficiencies(PCEs)of up to 23.71%.The champion all-perovskite tandem PSCs with GSH achieve a PCE of 28.49%and retain 90%of the initial PCE after 560 h of continuous illumination.This work establishes a new nature-inspired redox shuttling strategy and elucidates its working mechanism,advancing the development of efficient and stable all-perovskite tandem solar cells.展开更多
Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical el...Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical electrochemical performance is fundamentally limited by the polysulfide shuttle effect.This challenge is particularly exacerbated in Na-S and K-S systems owing to larger metal-ion radii,weaker solvation energies,slower redox kinetics,and greater electrolyte-electrode incompatibilities compared to Li-S batteries.This review presents a comparative analysis of interface engineering strategies designed to suppress the shuttle effect across these three systems.Following a summary of sulfur cathode properties and reaction mechanisms,we systematically examine the origins of polysulfide shuttling.Our analysis progresses from functional separator design and interlayer enhancements to the implementation of solid-state electrolytes for root-cause inhibition.By evaluating interface engineering research specific to Na-S and K-S batteries,we elucidate both shared principles and unique challenges inherent to alkali M-S systems.Finally,we propose multifaceted solutions to achieve shuttlefree operation and enhance overall battery performance,thereby establishing a foundation for future advancements.展开更多
Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two crit...Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Theconstruction of an integrative shuttle expression vector and potential utility was reported inEscherichiacoliandAnabaena(Nostoc) sp. strain PCC 7120. The vector comprised of the following elements: (a) an intergeni...Theconstruction of an integrative shuttle expression vector and potential utility was reported inEscherichiacoliandAnabaena(Nostoc) sp. strain PCC 7120. The vector comprised of the following elements: (a) an intergenic non-coding region fromAnabaenato facilitate its genomic integration (b) a strong functional PpsbAIpromoter fromAnabaenafor desired gene expression and (c) neomycin phosphotransferase gene with its own promoter for the selection of transfor-mants. The constructed vectorpAnFP was evaluated by cloning, transfer and expression of thegfpgene encoding green fluorescent protein. When theE.coliandAnabaenasp. strain PCC 7120 were transformed, intensive green fluorescence produced by the products of GFP protein was observed. This result indicated that the integrative shuttle vector pAnFP can be promisingly used in genome transformation for expression of heterologous genes inE.coliand microalgae such asAnabaenaandNostocstrains.展开更多
Synthetic biology is a newly developed field of research focused on designing and rebuilding novel biomolecular components, circuits, and networks. Synthetic biology can also help understand biological principles and ...Synthetic biology is a newly developed field of research focused on designing and rebuilding novel biomolecular components, circuits, and networks. Synthetic biology can also help understand biological principles and engineer complex artificial metabolic systems. DNA manipulation on a large genome-wide scale is an inevitable challenge, but a necessary tool for synthetic biology. To improve the methods used for the synthesis of long DNA fragments, here we constructed a novel shuttle vector named p GF(plasmid Genome Fast) for DNA assembly in vivo. The BAC plasmid p CC1 BAC, which can accommodate large DNA molecules, was chosen as the backbone. The sequence of the yeast artificial chromosome(YAC) regulatory element CEN6-ARS4 was synthesized and inserted into the plasmid to enable it to replicate in yeast. The selection sequence HIS3, obtained by polymerase chain reaction(PCR) from the plasmid p BS313, was inserted for screening. This new synthetic shuttle vector can mediate the transformation-associated recombination(TAR) assembly of large DNA fragments in yeast, and the assembled products can be transformed into Escherichia coli for further amplification. We also conducted in vivo DNA assembly using p GF and yeast homologous recombination and constructed a 31-kb long DNA sequence from the cyanophage PP genome. Our findings show that this novel shuttle vector would be a useful tool for efficient genome-scale DNA reconstruction.展开更多
To investigate a contribution of O-methylguanineto mutagenesis in mouse cells,we constructed ashuttle vector plasmid,pYZ289,from a part ofpZ189 plasmid and polyoma virus DNA.The plasmidcontains a supF gene as a marker...To investigate a contribution of O-methylguanineto mutagenesis in mouse cells,we constructed ashuttle vector plasmid,pYZ289,from a part ofpZ189 plasmid and polyoma virus DNA.The plasmidcontains a supF gene as a marker of mutation andcan replicate in both E.coli and mouse cells.ThepYZ289 treated with N-methyl-N-nitrosourea (MNU)展开更多
The Pseudomonas putida TOL plasmid gene xy1E,whichproduces catechol 2,3-dioxygenase （CatO<sub>2</sub>ase;catechol:oxygen 2,3-oxidoreductase （decyclizing）,EC 1.13.11.2 ）was used as a target gene to stud...The Pseudomonas putida TOL plasmid gene xy1E,whichproduces catechol 2,3-dioxygenase （CatO<sub>2</sub>ase;catechol:oxygen 2,3-oxidoreductase （decyclizing）,EC 1.13.11.2 ）was used as a target gene to study the mechanisms ofmutation and induced mutation frequency in展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion...Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.展开更多
Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the eva...Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface...A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface area of 427 m^(2)·g^(-1)and rich surface active sites,which help restrain polysulfides(LiPSs)through good physi-cal and chemical adsorption,while simultaneously accelerating the nucleation and dissolution kinetics of Li_(2)S,effec-tively suppressing the shuttle effect.The assembled lithium-sulfur batteries(LSBs)employing the PVS-based inter-layer delivered a high initial discharge capacity of 1386 mAh·g^(-1)at 0.1C(167.5 mAh·g^(-1)),long-term cycling stabil-ity,and good rate property.展开更多
The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collect...The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.展开更多
In this paper,we obtain a vector bundle valued mixed hard Lefschetz theorem.The argument is mainly based on the works of Tien-Cuong Dinh and Viet-Anh Nguyen.
基金supported by Guangdong Basic and Applied Basic Research Foundation(2025A1515011362)the National Natural Science Foundation of China(52102304,52172238)Open Project of Shaanxi Laboratory of Aerospace Power(2021SXSYS-01-03).
文摘Pb-Sn mixed perovskite solar cells(PSCs)are crucial components for realizing efficient all-perovskite tandem devices.However,their efficiency and stability are severely limited by oxidative degradation(Sn^(4+)formation)and metallic defects(Sn^(0)/Pb^(0)).In addition,the rapid and uncontrolled Sn^(2+)nucleation kinetics result in nonuniform crystallization.Herein,we introduce a natural redox shuttle glutathione(GSH)in Pb-Sn mixed PSCs,achieving regenerable antioxidation and crystallization regulation simultaneously.The reversible redox reactions between GSH and glutathione disulfide(GSSG)enable the self-healing of Sn^(4+)and Sn^(0)/Pb^(0)impurities,creating a regenerable antioxidation protective shell at the perovskite interfaces.Meanwhile,the strong coordination between GSH and perovskite regulates the crystallization process,optimizing the nucleation and crystallization kinetics.Furthermore,the GSH incorporation creates a high-quality charge separation junction at the perovskite/hole transport layer,facilitating carrier separation and extraction.The optimized Pb-Sn PSCs exhibit impressive power conversion efficiencies(PCEs)of up to 23.71%.The champion all-perovskite tandem PSCs with GSH achieve a PCE of 28.49%and retain 90%of the initial PCE after 560 h of continuous illumination.This work establishes a new nature-inspired redox shuttling strategy and elucidates its working mechanism,advancing the development of efficient and stable all-perovskite tandem solar cells.
基金supported by the National Natural Science Foundation of China(52371131)the 10th Youth Talent Lifting Project of the China Association for Science and Technology.
文摘Rechargeable alkali metal-sulfur(M-S)batteries,including Li/Na/K-S chemistries,have the potential to utilize abundant and low-cost sulfur cathodes yet offer high theoretical energy densities.However,their practical electrochemical performance is fundamentally limited by the polysulfide shuttle effect.This challenge is particularly exacerbated in Na-S and K-S systems owing to larger metal-ion radii,weaker solvation energies,slower redox kinetics,and greater electrolyte-electrode incompatibilities compared to Li-S batteries.This review presents a comparative analysis of interface engineering strategies designed to suppress the shuttle effect across these three systems.Following a summary of sulfur cathode properties and reaction mechanisms,we systematically examine the origins of polysulfide shuttling.Our analysis progresses from functional separator design and interlayer enhancements to the implementation of solid-state electrolytes for root-cause inhibition.By evaluating interface engineering research specific to Na-S and K-S batteries,we elucidate both shared principles and unique challenges inherent to alkali M-S systems.Finally,we propose multifaceted solutions to achieve shuttlefree operation and enhance overall battery performance,thereby establishing a foundation for future advancements.
基金the financial support from the Natural Science Foundation of Jiangsu Province(BK20231292)the Jiangsu Agricultural Science and Technology Innovation Fund(CX(24)3091)+6 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_1429)the National Key R&D Program of China(2024YFE0109200)the Fundamental Research Funds for the Central Universities(No.2024300440)Guangdong Basic and Applied Basic Research Foundation(2025A1515011098)the National Natural Science Foundation of China(12464032)the Natural Science Foundation of Jiangxi Province(20232BAB201032)Ji'an Science and Technology Plan Project(2024H-100301)。
文摘Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
文摘Theconstruction of an integrative shuttle expression vector and potential utility was reported inEscherichiacoliandAnabaena(Nostoc) sp. strain PCC 7120. The vector comprised of the following elements: (a) an intergenic non-coding region fromAnabaenato facilitate its genomic integration (b) a strong functional PpsbAIpromoter fromAnabaenafor desired gene expression and (c) neomycin phosphotransferase gene with its own promoter for the selection of transfor-mants. The constructed vectorpAnFP was evaluated by cloning, transfer and expression of thegfpgene encoding green fluorescent protein. When theE.coliandAnabaenasp. strain PCC 7120 were transformed, intensive green fluorescence produced by the products of GFP protein was observed. This result indicated that the integrative shuttle vector pAnFP can be promisingly used in genome transformation for expression of heterologous genes inE.coliand microalgae such asAnabaenaandNostocstrains.
基金supported by the 973 program,Grant No.2012CB721102
文摘Synthetic biology is a newly developed field of research focused on designing and rebuilding novel biomolecular components, circuits, and networks. Synthetic biology can also help understand biological principles and engineer complex artificial metabolic systems. DNA manipulation on a large genome-wide scale is an inevitable challenge, but a necessary tool for synthetic biology. To improve the methods used for the synthesis of long DNA fragments, here we constructed a novel shuttle vector named p GF(plasmid Genome Fast) for DNA assembly in vivo. The BAC plasmid p CC1 BAC, which can accommodate large DNA molecules, was chosen as the backbone. The sequence of the yeast artificial chromosome(YAC) regulatory element CEN6-ARS4 was synthesized and inserted into the plasmid to enable it to replicate in yeast. The selection sequence HIS3, obtained by polymerase chain reaction(PCR) from the plasmid p BS313, was inserted for screening. This new synthetic shuttle vector can mediate the transformation-associated recombination(TAR) assembly of large DNA fragments in yeast, and the assembled products can be transformed into Escherichia coli for further amplification. We also conducted in vivo DNA assembly using p GF and yeast homologous recombination and constructed a 31-kb long DNA sequence from the cyanophage PP genome. Our findings show that this novel shuttle vector would be a useful tool for efficient genome-scale DNA reconstruction.
文摘To investigate a contribution of O-methylguanineto mutagenesis in mouse cells,we constructed ashuttle vector plasmid,pYZ289,from a part ofpZ189 plasmid and polyoma virus DNA.The plasmidcontains a supF gene as a marker of mutation andcan replicate in both E.coli and mouse cells.ThepYZ289 treated with N-methyl-N-nitrosourea (MNU)
文摘The Pseudomonas putida TOL plasmid gene xy1E,whichproduces catechol 2,3-dioxygenase （CatO<sub>2</sub>ase;catechol:oxygen 2,3-oxidoreductase （decyclizing）,EC 1.13.11.2 ）was used as a target gene to study the mechanisms ofmutation and induced mutation frequency in
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金performed at large-scale research facility"Beam-M"of Bauman Moscow State Technical University following the government task by the Ministry of Science and Higher Education of the Russian Federation(No.FSFN-2024-0007).
文摘Thrust-vectoring capability has become a critical feature for propulsion systems as space missions move from static to dynamic.Thrust-vectoring is a well-developed area of rocket engine science.For electric propulsion,however,it is an evolving field that has taken a new leap forward in recent years.A review and analysis of thrust-vectoring schemes for electric propulsion systems have been conducted.The scope of this review includes thrust-vectoring schemes that can be implemented for electrostatic,electromagnetic,and beam-driven thrusters.A classification of electric propulsion schemes that provide thrust-vectoring capability is developed.More attention is given to schemes implemented in laboratory prototypes and flight models.The final part is devoted to a discussion on the suitability of different electric propulsion systems with thrust-vectoring capability for modern space mission operations.The thrust-vectoring capability of electric propulsion is necessary for inner and outer space satellites,which are at a disadvantage with conventional unidirectional propulsion systems due to their limited maneuverability.
基金primarily supported by the National Key R&D Program of China[grant number 2021YFC3000904]the Jiangsu Provincial Key Technology R&D Program[grant number BE2022851]National Natural Science Foundation of China[grant number 42405035]。
文摘Vector winds play a crucial role in weather and climate,as well as the effective utilization of wind energy resources.However,limited research has been conducted on treating the wind field as a vector field in the evaluation of numerical weather prediction models.In this study,the authors treat vector winds as a whole by employing a vector field evaluation method,and evaluate the mesoscale model of the China Meteorological Administration(CMA-MESO)and ECMWF forecast,with reference to ERA5 reanalysis,in terms of multiple aspects of vector winds over eastern China in 2022.The results show that the ECMWF forecast is superior to CMA-MESO in predicting the spatial distribution and intensity of 10-m vector winds.Both models overestimate the wind speed in East China,and CMA-MESO overestimates the wind speed to a greater extent.The forecasting skill of the vector wind field in both models decreases with increasing lead time.The forecasting skill of CMA-MESO fluctuates more and decreases faster than that of the ECMWF forecast.There is a significant negative correlation between the model vector wind forecasting skill and terrain height.This study provides a scientific evaluation of the local application of vector wind forecasts of the CMA-MESO model and ECMWF forecast.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
文摘A functional interlayer based on two-dimensional(2D)porous modified vermiculite nanosheets(PVS)was obtained by acid-etching vermiculite nanosheets.The as-obtained 2D porous nanosheets exhibited a high specific surface area of 427 m^(2)·g^(-1)and rich surface active sites,which help restrain polysulfides(LiPSs)through good physi-cal and chemical adsorption,while simultaneously accelerating the nucleation and dissolution kinetics of Li_(2)S,effec-tively suppressing the shuttle effect.The assembled lithium-sulfur batteries(LSBs)employing the PVS-based inter-layer delivered a high initial discharge capacity of 1386 mAh·g^(-1)at 0.1C(167.5 mAh·g^(-1)),long-term cycling stabil-ity,and good rate property.
基金supported by the National Key R&D Program of China(Grant2022YFF0503700)the National Natural Science Foundation of China(42474200 and 42174186)。
文摘The Macao Science Satellite-1(known as MSS-1)is the first scientific exploration satellite that was designed to measure the Earth's low latitude magnetic field at high resolution and with high precision by collecting data in a near-equatorial orbit.Magnetic field data from MSS-1's onboard Vector Fluxgate Magnetometer(VFM),collected at a sample rate of 50 Hz,allows us to detect and investigate sources of magnetic data contamination,from DC to relevant Nyquist frequency.Here we report two types of artificial disturbances in the VFM data.One is V-shaped events concentrated at night,with frequencies sweeping from the Nyquist frequency down to zero and back up.The other is 5-Hz events(ones that exhibit a distinct 5 Hz spectrum peak);these events are always accompanied by intervals of spiky signals,and are clearly related to the attitude control of the satellite.Our analyses show that VFM noise levels in daytime are systematically lower than in nighttime.The daily average noise levels exhibit a period of about 52 days.The V-shaped events are strongly correlated with higher VFM noise levels.
基金supported by the National key R and D Program of China 2020YFA0713100the NSFC(12141104,12371062 and 12431004).
文摘In this paper,we obtain a vector bundle valued mixed hard Lefschetz theorem.The argument is mainly based on the works of Tien-Cuong Dinh and Viet-Anh Nguyen.
