The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input mult...The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.展开更多
The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial...The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial operation at the Lingbao Converter Station in Henan Province,China,on December 28,2025,as shown in Figure 1.This milestone signifies the resolution of the“commutation failure”challenge that has plagued global HVDC transmission systems for over half a century.展开更多
Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers u...D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.展开更多
The net capturing method holds great potential for space debris removal due to its adaptability to the various target shapes and high fault tolerance.However,the capture mechanisms of current rope nets,which rely sole...The net capturing method holds great potential for space debris removal due to its adaptability to the various target shapes and high fault tolerance.However,the capture mechanisms of current rope nets,which rely solely on a passive wrap-ping mechanism,limit their capacity to capture objects within a specific size range and make it challenging to handle unexpected situations.Inspired by spider webs,which combine wrapping and adhering to capture prey of various sizes,we present a new type of net(envelope diameter:208.49 mm)for on-orbit capture.This net adopts a spiral symmetric structure similar to spider webs,incorporates electrostatic-microstructure hybrid adhesives,and increases the maximum contact area by 38.31%,allowing it to capture debris ranging from fragments smaller than the mesh size(envelope diam-eter:2.7 mm-4.4 mm)to larger objects(envelope diameter:270 mm),and effectively grasps flexible items(450 mm2),planar items(350 mm2)and three-dimensional items(160 mm3).Moreover,to validate the net's capability for wrapping and adhesion,simulations and experiments are demonstrated that this dual capture method can effectively handle various targets.展开更多
The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-ass...The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-assembly method is used to fabricate controllable MOF-on-MOF multiscale heterostructures,where triangular host MOF(ZIF-67)nanosheets undergo in situ epitaxial growth to form uniform orthogonal vip MOF(CoFe PBA)nanosheets.Phosphorus(P)is further introduced in situ to fabricate CoP and Fe_(2)P heterostructured nanosheets(CoFe-P-NS),which exhibit excellent bifunctional electrocatalytic performance due to the enhancement of intrinsic electrocatalytic activity by p-d orbital hybridization.Specifically,the CoFe-P-NS requires low overpotential of 259 and 307 mV to reach 500 mA cm−2 for HER and OER,respectively.Remarkably,the assembled electrolysis cell maintained a large current density of 300 mA cm−2 for over 360 h with negligible voltage increase during alkaline seawater electrolysis.Experiments and theoretical calculations show that the synergistic catalytic activity of bimetallic phosphides arises from p-d orbital hybridization,where the CoP-P sites enhance HER by optimizing H*adsorption in the Volmer-Heyrovsky steps,while the Fe_(2)P-Fe sites accelerate OER by lowering the energy barrier of the rate-determining step from O*to OOH*.This study provides valuable insights into the design of a controllable MOF-on-MOF-based electrocatalyst toward alkaline seawater splitting.展开更多
Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindere...Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindered by the limited energy density of traditional carbon-based cathodes.Here,we rationally design porous carbon nanofibers embedded with atomically dispersed Zn and Fe dual-metal sites(ZnFe/PCNFs),synthesized via electrospinning followed by controlled carbonization.The introduction of Fe modulates the local electronic structure of Zn centers,thereby facilitating enhanced d-orbital hybridization and stronger ion adsorption through the formation of ZnFeN_(6) coordination motifs.Coupled with high surface area and hierarchical porosity,these atomic-level interactions facilitate exceptional ion accessibility and rapid charge-transfer kinetics.As a cathode for ZIHCs,ZnFe/PCNFs deliver a specific capacity of 213 mAh g^(-1),exceptional high-rate capability,and longterm cycling stability over 20000 cycles.This work elucidates mechanisms of dual-metal atomic coordination and provides a robust design strategy for high-performance,durable aqueous energy storage systems.展开更多
Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been propo...Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.展开更多
Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and rene...Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems.This study investigates a hybrid heating system combining a natural gas boiler(NGB)with an air-to-water heat pump(AWHP),evaluated through a combination of laboratory experiments and dynamic modelling.A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators,the collection of experimental data,and the calibration of a MATLAB/Simulink model,including emissions and exhaust analyses.Sensitivity analyses were performed to identify optimal configurations for energy efficiency and system control,accounting for interactions between subsystems.Results highlight that hybridisation significantly improves primary energy efficiency and reduces fuel consumption compared to conventional NGB-only systems.Environmental performance,assessed through CO_(2) and NOx emissions and renewable energy integration,demonstrates the benefits of partial electrification in the residential sector.Economic assessment further quantifies decarbonization costs and fuel savings,illustrating tradeoffs between low-capital,moderate-performance systems and high-efficiency,high-renewable solutions requiring larger investments.The analysis shows that strategic decisions for residential decarbonisation cannot be separated from system-wide considerations,including control strategies,component integration,and economic feasibility.The study underlines the importance of hybrid and renewable-based solutions as pivotal pathways for energy transition in the residential building sector.展开更多
This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and s...This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.展开更多
Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used i...Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.展开更多
The breakthrough in super hybrid rice yield has significantly contributed to China’s and global food security.However,the inherent conflict between high productivity and environmentally sustainable agriculture poses ...The breakthrough in super hybrid rice yield has significantly contributed to China’s and global food security.However,the inherent conflict between high productivity and environmentally sustainable agriculture poses substantial challenges.Issues such as water scarcity,energy crises,escalating greenhouse gas emissions,and diminishing farm profitability threaten longterm agricultural sustainability.In response,we applied a holistic food–carbon–nitrogen–water–energy–profit (FCNWEP)nexus framework to comprehensively assess the sustainability of distinct crop management strategies across three subsites in Central China.Field experiments were conducted in Hubei and Hunan provinces from 2017 to 2021 using a widely adopted elite super hybrid rice cultivar (Y-liangyou 900).Four crop management treatments were implemented:a control(CK,0 kg N ha^(-1)),conventional crop management (CCM,210–250 kg N ha^(-1),7:3 basal:mid-tiller fertilizer ratio),and two integrated crop management (ICM) treatments (ICM1,180–210 kg N ha^(-1),5:2:3 basal:mid-tiller:panicle initiation fertilizer ratio;ICM2,240–270 kg N ha^(-1),5:2:2:1 basal:mid-tiller:panicle initiation:flowering fertilizer ratio).Variables assessed included grain yield,carbon footprint,nitrogen footprint,water footprint,energy footprint,nitrogen use efficiency,and economic benefits.Our results showed significant yield variations,with ICM2 consistently outperforming CCM and ICM1across all three sites.In Jingzhou,Suizhou,and Changsha,ICM2’s grain yield was 30.2,24.7,and 13.3%higher than CCM,respectively.Net profits under ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2%in Jingzhou,32.2 and 109.9%in Suizhou,and 15.4 and 34.0%in Changsha,respectively.Integrated crop management,particularly ICM2,demonstrated improved nitrogen and energy use efficiency,leading to reduced carbon,nitrogen,water,and energy footprints.Overall,composite sustainability scores derived from the FCNWEP framework indicated that both ICM2 and ICM1 exhibited higher sustainability levels compared to CCM.This study provides valuable insights into practical management methodologies and offers recommendations for enhancing agricultural sustainability.展开更多
The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)mode...The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)model to achieve higher forecasting performance for global VTEC predictions under the condition of data acquisition delays.Initially,this study uses the first Hybrid Attentional UNet(HA-UNet)model to predict the intermediate missing data.The missing data are caused by delays in data processing,making the Global Ionosphere Map(GIM)for the current day unavailable.Subsequently,the predicted results from the first HA-UNet model are concatenated with the input data to serve as the input data for the second HA-UNet model,yielding the final prediction results.The performance of DHA-UNet model is then evaluated under varying solar and geomagnetic activity conditions.Evaluation results demonstrate that the DHA-UNet model exhibits higher forecasting accuracy and stability compared to commonly used temporal and spatiotemporal forecasting models.Compared to CODG VTEC,the DHA-UNet model achieves Mean Absolute Error(MAE)values of 2.60 TECU,3.07 TECU,3.78 TECU,and 6.45TECU during quiet,weak,moderate,and strong geomagnetic storm periods,respectively,in years of high solar activity.In years of low solar activity,the model achieves MAE values of 1.00 TECU,1.15 TECU,and 1.54 TECU during quiet,weak,and moderate geomagnetic storm periods,respectively.Even during strong geomagnetic storms,55%of the residuals from the DHA-UNet model fall within the-5.0 TECU to 5.0 TECU range,surpassing other commonly used models.Compared to the C1PG forecasting product,the DHA-UNet model shows particularly notable improvements in accuracy during the spring and winter seasons,as well as in mid-to high-latitude regions.展开更多
The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in serie...The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.展开更多
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw...Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.展开更多
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D...In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.展开更多
Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains...Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains a confusing topic.Here we develop a hydrogen-bond-oriented interfacial super-assembly strategy to custom-tailor nanosheet-intertwined spherical carbon superstructures(SCSs)for Zn-ion storage with double-high capacitive activity and durability.Tetrachlorobenzoquinone(H-bond acceptor)and dimethylbenzidine(H-bond donator)can interact to form organic nanosheet modules,which are sequentially assembled,orientally compacted and densified into well-orchestrated superstructures through multiple H-bonds(N-H···O).Featured with rich surface-active heterodiatomic motifs,more exposed nanoporous channels,and successive charge migration paths,SCSs cathode promises high accessibility of built-in zincophilic sites and rapid ion diffusion with low energy barriers(3.3Ωs-0.5).Consequently,the assembled Zn||SCSs capacitor harvests all-round improvement in Zn-ion storage metrics,including high energy density(166 Wh kg-1),high-rate performance(172 m Ah g^(-1)at 20 A g^(-1)),and long-lasting cycling lifespan(95.5%capacity retention after 500,000 cycles).An opposite chargecarrier storage mechanism is rationalized for SCSs cathode to maximize spatial capacitive charge storage,involving high-kinetics physical Zn^(2+)/CF_(3)SO_(3)-adsorption and chemical Zn^(2+)redox with carbonyl/pyridine groups.This work gives insights into H-bond-guided interfacial superassembly design of superstructural carbons toward advanced energy storage.展开更多
The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectivel...The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.展开更多
A surface pyrolysis and gas-phase combustion of the Ammonium Perchlorate(AP)/Hydroxy Terminated Polybutadiene(HTPB)composite propellant reaction kinetic mechanism with five-step chemical reaction is adopted.The effect...A surface pyrolysis and gas-phase combustion of the Ammonium Perchlorate(AP)/Hydroxy Terminated Polybutadiene(HTPB)composite propellant reaction kinetic mechanism with five-step chemical reaction is adopted.The effects of helium injection on the burning rate and combustion of AP/HTPB propellant are analyzed in details,and the characteristics of motor performance are obtained.The numerical simulation results demonstrate that helium injection enhances the combustion chamber pressure,thereby increasing the burning rate of propellant.However,the primary combustion reaction of the AP/HTPB propellant takes place within a thin layer on the burning surface,so the low-temperature helium has minimal impact on the gasphase combustion.Ultimately,the helium not only elevates the nozzle exit velocity,resulting in specific impulse gain,but also reduces the exhaust plume temperature.With an increase of helium mass flow rate,the area of the velocity increase zone at the nozzle exit continuously decreases,but the average velocity in the motor exit continuously increases.Overall,when the helium flow rate is 2.5 kg/s,the specific impulse can reach 10.5%.Reducing the helium injection hole diameter enhances mixing of helium and combustion gas and expands the velocity increase zone,thereby maximizing the exit velocity gain in average velocity at the nozzle exit.When the injection hole diameter is reduced from 100 mm to 20 mm,the specific impulse gain increases from 3.1%to 10.6%.Furthermore,increasing helium injection temperature greatly boosts the velocity of the mixed gas with the same helium mass fraction ultimately improving specific impulse.展开更多
基金co-supported by the National Natural Science Foundation of China(No.52125504)the Liaoning Revitalization Talents Program(No.XLYC2202017)Dalian Support Policy Project for Innovation of Technological Talents(No.2023RG001)。
文摘The high-quality assembly of Large Aircraft Components(LACs)is essential in modern aviation manufacturing.Numerical control locators are employed for the posture adjustment of LAC,yet the system's multi-input multi-output,nonlinearity,and strong coupling presents significant challenges.The substantial internal force generated during the adjustment process can potentially damage the LAC and degrade the assembly quality.Hence,a workspace-based hybrid force position control scheme was developed to achieve high quality assembly with high-precision and lower internal force.Firstly,an offline workspace analysis with inherent geometric characteristics to form time-varying posture error constraint.Then,the posture error is integrated into the online position axis control to ensure tracking the ideal posture,while the force control axis compensates for posture deviation by minimizing internal force,thereby achieving high precision and low internal force.Finally,the effectiveness was demonstrated through experiments.The root mean square errors of orientation and position are 104 rad and 0.1 mm,respectively.A reduction in internal force can range from 10.96%to 57.4%compared to the traditional method.Key points'max position error is decreased from 0.32 mm to 0.18 mm,satisfying the 0.5 mm tolerance.Therefore,the proposed method will help promote the development of high-performance manufacturing.
文摘The world’s first hybrid commutated converter(HCC)—a next-generation high-voltage direct current(HVDC)transmission valve based on integrated gate commutated thyristor(IGCT)technology—officially commenced commercial operation at the Lingbao Converter Station in Henan Province,China,on December 28,2025,as shown in Figure 1.This milestone signifies the resolution of the“commutation failure”challenge that has plagued global HVDC transmission systems for over half a century.
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
文摘D-π hybridization is a key structural feature that may significantly affect the intrinsic electronic properties of metallopolymers.Herein,we present the electrosynthesis and memristive properties of metallopolymers using the distinct d-π hybridization monomers R_(1) and R_(2).R_(1)(Ru^(Ⅱ)-(tpz)Cl_(2))features tetradentate ligands(tpz,6,6'-di(1H-pyrazol-1-yl)-2,2'-bipyridine)enforcing quasi-octahedral geometry;R_(2)(Ru^(Ⅱ)-(bpp)_(2))incorporates tridentate ligands(bpp,2,6-di(1H-pyrazol-1-yl)pyridine)inducing pronounced geometric distortion.The planar ligand(tpz)in R_(1) facilitates ordered molecular assembly through high conformational rigidity and extensive π-π stacking,resulting in increased molecular densities and enhanced morphological uniformity compared to R_(2) metallopolymers.Due to pyrazole’s weaker π-acceptance and strongerσ-donation compared to pyridine,R_(1) exhibits a 119 nm red-shift in metal-to-ligand charge transfer(MLCT)band and a 30 mV anodic shift in Ru^(+2/+3)redox potential relative to R_(2).Coupled with a reduced HOMO-LUMO gap,the uniform and ordered structure leads to a lower conductance decay constant in R_(1).Additionally,R_(2) metallopolymers exhibit superior memristive performance(characterized by lower switching voltage and higher switching ratio)via redox-induced aromatic transitions in axial ligands enhancing electronic delocalization.This work compares two metallopolymers with different ligand geometries,revealing how this difference leads to distinct charge transport and memristive behaviors.
基金the New Chongqing Innovative Young Talent Project under Grant 2024NSCQ-qncxX0468Dreams Foundation of Jianghuai Advance Technology Center under Grant 2023-ZM01Z007.
文摘The net capturing method holds great potential for space debris removal due to its adaptability to the various target shapes and high fault tolerance.However,the capture mechanisms of current rope nets,which rely solely on a passive wrap-ping mechanism,limit their capacity to capture objects within a specific size range and make it challenging to handle unexpected situations.Inspired by spider webs,which combine wrapping and adhering to capture prey of various sizes,we present a new type of net(envelope diameter:208.49 mm)for on-orbit capture.This net adopts a spiral symmetric structure similar to spider webs,incorporates electrostatic-microstructure hybrid adhesives,and increases the maximum contact area by 38.31%,allowing it to capture debris ranging from fragments smaller than the mesh size(envelope diam-eter:2.7 mm-4.4 mm)to larger objects(envelope diameter:270 mm),and effectively grasps flexible items(450 mm2),planar items(350 mm2)and three-dimensional items(160 mm3).Moreover,to validate the net's capability for wrapping and adhesion,simulations and experiments are demonstrated that this dual capture method can effectively handle various targets.
基金financial support of the National Natural Science Foundation of China (21875247,21072221, 21172252)the Project of Talent Cultivation for Carbon Peak and Carbon Neutrality of the University of Chinese of Academy of Science
文摘The design and fabrication of ordered epitaxial MOF-on-MOF heterostructures as highly efficient electrocatalysts for water splitting is crucial but still challenging.In this study,a simple coordination-driven self-assembly method is used to fabricate controllable MOF-on-MOF multiscale heterostructures,where triangular host MOF(ZIF-67)nanosheets undergo in situ epitaxial growth to form uniform orthogonal vip MOF(CoFe PBA)nanosheets.Phosphorus(P)is further introduced in situ to fabricate CoP and Fe_(2)P heterostructured nanosheets(CoFe-P-NS),which exhibit excellent bifunctional electrocatalytic performance due to the enhancement of intrinsic electrocatalytic activity by p-d orbital hybridization.Specifically,the CoFe-P-NS requires low overpotential of 259 and 307 mV to reach 500 mA cm−2 for HER and OER,respectively.Remarkably,the assembled electrolysis cell maintained a large current density of 300 mA cm−2 for over 360 h with negligible voltage increase during alkaline seawater electrolysis.Experiments and theoretical calculations show that the synergistic catalytic activity of bimetallic phosphides arises from p-d orbital hybridization,where the CoP-P sites enhance HER by optimizing H*adsorption in the Volmer-Heyrovsky steps,while the Fe_(2)P-Fe sites accelerate OER by lowering the energy barrier of the rate-determining step from O*to OOH*.This study provides valuable insights into the design of a controllable MOF-on-MOF-based electrocatalyst toward alkaline seawater splitting.
基金supported by the Major Basic Research Projects of Shandong Natural Science Foundation(ZR2024ZD37)the Taishan Scholar Program of Shandong Province,China(No.tsqn202211048)+3 种基金the National Natural Science Foundation of China(No.22179123,22579155)the National Science Fund for Distinguished Young Scholars(52125305)the Science and Technology Key Project of Wuhan(No.2023010302020030)and the Science and Technology Major Project of Xinjiang Autonomous Region(No.2022A03009).
文摘Zinc-ion hybrid supercapacitors(ZIHCs)are compelling candidates for next-generation energy storage owing to their intrinsic safety,low cost,and high power density.However,their practical implementation remains hindered by the limited energy density of traditional carbon-based cathodes.Here,we rationally design porous carbon nanofibers embedded with atomically dispersed Zn and Fe dual-metal sites(ZnFe/PCNFs),synthesized via electrospinning followed by controlled carbonization.The introduction of Fe modulates the local electronic structure of Zn centers,thereby facilitating enhanced d-orbital hybridization and stronger ion adsorption through the formation of ZnFeN_(6) coordination motifs.Coupled with high surface area and hierarchical porosity,these atomic-level interactions facilitate exceptional ion accessibility and rapid charge-transfer kinetics.As a cathode for ZIHCs,ZnFe/PCNFs deliver a specific capacity of 213 mAh g^(-1),exceptional high-rate capability,and longterm cycling stability over 20000 cycles.This work elucidates mechanisms of dual-metal atomic coordination and provides a robust design strategy for high-performance,durable aqueous energy storage systems.
基金funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.:5199990914048).
文摘Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning.As research in this domain continues to expand,various segmentation techniques have been proposed across classical image processing,machine learning(ML),deep learning(DL),and hybrid/ensemble models.This study conducts a systematic literature review using the PRISMA methodology,analyzing 57 selected articles to explore how these methods have evolved and been applied.The review highlights the strengths and limitations of each approach,identifies commonly used public datasets,and observes emerging trends in model integration and clinical relevance.By synthesizing current findings,this work provides a structured overview of segmentation strategies and outlines key considerations for developing more adaptable and explainable tools for breast cancer detection.Overall,our synthesis suggests that classical and ML methods are suitable for limited labels and computing resources,while DL models are preferable when pixel-level annotations and resources are available,and hybrid pipelines are most appropriate when fine-grained clinical precision is required.
基金supported by European Commission and is a part of the HORIZON2020 project RES Heatfunding from the European Union’s Horizon 2020 program in the field of research and innovation on the basis of grant agreement No.956255.
文摘Decarbonising the building sector,particularly residential heating,represents a critical challenge for achieving carbon-neutral energy systems.Efficient solutions must integrate both technological performance and renewable energy sources while considering operational constraints of existing systems.This study investigates a hybrid heating system combining a natural gas boiler(NGB)with an air-to-water heat pump(AWHP),evaluated through a combination of laboratory experiments and dynamic modelling.A prototype developed in the Electrical and Energy Engineering Laboratory enabled the characterization of both heat generators,the collection of experimental data,and the calibration of a MATLAB/Simulink model,including emissions and exhaust analyses.Sensitivity analyses were performed to identify optimal configurations for energy efficiency and system control,accounting for interactions between subsystems.Results highlight that hybridisation significantly improves primary energy efficiency and reduces fuel consumption compared to conventional NGB-only systems.Environmental performance,assessed through CO_(2) and NOx emissions and renewable energy integration,demonstrates the benefits of partial electrification in the residential sector.Economic assessment further quantifies decarbonization costs and fuel savings,illustrating tradeoffs between low-capital,moderate-performance systems and high-efficiency,high-renewable solutions requiring larger investments.The analysis shows that strategic decisions for residential decarbonisation cannot be separated from system-wide considerations,including control strategies,component integration,and economic feasibility.The study underlines the importance of hybrid and renewable-based solutions as pivotal pathways for energy transition in the residential building sector.
文摘This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.
基金funded by the Directorate General of Research and Development,Ministry of Higher Education,Science and Technology of the Republic of Indonesia,with grant number 2.6.63/UN32.14.1/LT/2025.
文摘Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.
基金funded by the National Natural Science Foundation of China (32172108 and 32301940)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2023QNRC001)+2 种基金the China Postdoctoral Science Foundation (2022M710489)the Chinese Scholarship Council (202310930003)the National Key Research and Development Program of China (2022YFD2301004)。
文摘The breakthrough in super hybrid rice yield has significantly contributed to China’s and global food security.However,the inherent conflict between high productivity and environmentally sustainable agriculture poses substantial challenges.Issues such as water scarcity,energy crises,escalating greenhouse gas emissions,and diminishing farm profitability threaten longterm agricultural sustainability.In response,we applied a holistic food–carbon–nitrogen–water–energy–profit (FCNWEP)nexus framework to comprehensively assess the sustainability of distinct crop management strategies across three subsites in Central China.Field experiments were conducted in Hubei and Hunan provinces from 2017 to 2021 using a widely adopted elite super hybrid rice cultivar (Y-liangyou 900).Four crop management treatments were implemented:a control(CK,0 kg N ha^(-1)),conventional crop management (CCM,210–250 kg N ha^(-1),7:3 basal:mid-tiller fertilizer ratio),and two integrated crop management (ICM) treatments (ICM1,180–210 kg N ha^(-1),5:2:3 basal:mid-tiller:panicle initiation fertilizer ratio;ICM2,240–270 kg N ha^(-1),5:2:2:1 basal:mid-tiller:panicle initiation:flowering fertilizer ratio).Variables assessed included grain yield,carbon footprint,nitrogen footprint,water footprint,energy footprint,nitrogen use efficiency,and economic benefits.Our results showed significant yield variations,with ICM2 consistently outperforming CCM and ICM1across all three sites.In Jingzhou,Suizhou,and Changsha,ICM2’s grain yield was 30.2,24.7,and 13.3%higher than CCM,respectively.Net profits under ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2%in Jingzhou,32.2 and 109.9%in Suizhou,and 15.4 and 34.0%in Changsha,respectively.Integrated crop management,particularly ICM2,demonstrated improved nitrogen and energy use efficiency,leading to reduced carbon,nitrogen,water,and energy footprints.Overall,composite sustainability scores derived from the FCNWEP framework indicated that both ICM2 and ICM1 exhibited higher sustainability levels compared to CCM.This study provides valuable insights into practical management methodologies and offers recommendations for enhancing agricultural sustainability.
基金funded by the National Key R&D Program of China(No.2022YFB3904402)the National Natural Science Foundation of China(Nos.42474037 and U2233217)。
文摘The Vertical Total Electron Content(VTEC)of the ionosphere is a crucial parameter for describing the distribution and dynamic changes within the ionosphere.The study utilizes Dual Hybrid Attentional UNet(DHA-UNet)model to achieve higher forecasting performance for global VTEC predictions under the condition of data acquisition delays.Initially,this study uses the first Hybrid Attentional UNet(HA-UNet)model to predict the intermediate missing data.The missing data are caused by delays in data processing,making the Global Ionosphere Map(GIM)for the current day unavailable.Subsequently,the predicted results from the first HA-UNet model are concatenated with the input data to serve as the input data for the second HA-UNet model,yielding the final prediction results.The performance of DHA-UNet model is then evaluated under varying solar and geomagnetic activity conditions.Evaluation results demonstrate that the DHA-UNet model exhibits higher forecasting accuracy and stability compared to commonly used temporal and spatiotemporal forecasting models.Compared to CODG VTEC,the DHA-UNet model achieves Mean Absolute Error(MAE)values of 2.60 TECU,3.07 TECU,3.78 TECU,and 6.45TECU during quiet,weak,moderate,and strong geomagnetic storm periods,respectively,in years of high solar activity.In years of low solar activity,the model achieves MAE values of 1.00 TECU,1.15 TECU,and 1.54 TECU during quiet,weak,and moderate geomagnetic storm periods,respectively.Even during strong geomagnetic storms,55%of the residuals from the DHA-UNet model fall within the-5.0 TECU to 5.0 TECU range,surpassing other commonly used models.Compared to the C1PG forecasting product,the DHA-UNet model shows particularly notable improvements in accuracy during the spring and winter seasons,as well as in mid-to high-latitude regions.
基金supported by the Smart Grid-National Science and Technology Major Project(2025ZD0804500)the National Natural Science Foundation of China under Grant 52307232the Hunan Provincial Natural Science Foundation of China under Grant 2024JJ4055.
文摘The hybrid series-parallel microgrid attracts more attention by combining the advantages of both the series-stacked voltage and parallel-expanded capacity.Low-voltage distributed generations(DGs)are connected in series to form the intra-string,and then multiple strings are interconnected in parallel.For the existing control strategies,both intra-string and inter-string depend on the centralized or distributed control with high communication reliance.It has limited scalability and redundancy under abnormal conditions.Alternatively,in this study,an intra-string distributed and inter-string decentralized control framework is proposed.Within the string,a few DGs close to the AC bus are the leaders to get the string power information and the rest DGs are the followers to acquire the synchronization information through the droop-based distributed consistency.Specifically,the output of the entire string has the active power−angular frequency(ω-P)droop characteristic,and the decentralized control among strings can be autonomously guaranteed.Moreover,the secondary control is designed to realize multi-mode objectives,including on/off-grid mode switching,grid-connected power interactive management,and off-grid voltage quality regulation.As a result,the proposed method has the ability of plug-and-play capabilities,single-point failure redundancy,and seamless mode-switching.Experimental results are provided to verify the effectiveness of the proposed practical solution.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202431,52172353).
文摘Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
基金funded by the Institute of Smart Energy,Huaiyin Institute of Technology,under Grant No.HIT-ISE-2024-07.
文摘In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation.
基金financially supported by the National Natural Science Foundation of China(Nos.22272118,22172111,and 22309134)the Science and Technology Commission of Shanghai Municipality,China(Nos.22ZR1464100,20ZR1460300,and 19DZ2271500)+2 种基金the China Postdoctoral Science Foundation(2022M712402),the Shanghai Rising-Star Program(23YF1449200)the Zhejiang Provincial Science and Technology Project(2022C01182)the Fundamental Research Funds for the Central Universities(2023-3-YB-07)。
文摘Carbon superstructures with multiscale hierarchies and functional attributes represent an appealing cathode candidate for zinc hybrid capacitors,but their tailor-made design to optimize the capacitive activity remains a confusing topic.Here we develop a hydrogen-bond-oriented interfacial super-assembly strategy to custom-tailor nanosheet-intertwined spherical carbon superstructures(SCSs)for Zn-ion storage with double-high capacitive activity and durability.Tetrachlorobenzoquinone(H-bond acceptor)and dimethylbenzidine(H-bond donator)can interact to form organic nanosheet modules,which are sequentially assembled,orientally compacted and densified into well-orchestrated superstructures through multiple H-bonds(N-H···O).Featured with rich surface-active heterodiatomic motifs,more exposed nanoporous channels,and successive charge migration paths,SCSs cathode promises high accessibility of built-in zincophilic sites and rapid ion diffusion with low energy barriers(3.3Ωs-0.5).Consequently,the assembled Zn||SCSs capacitor harvests all-round improvement in Zn-ion storage metrics,including high energy density(166 Wh kg-1),high-rate performance(172 m Ah g^(-1)at 20 A g^(-1)),and long-lasting cycling lifespan(95.5%capacity retention after 500,000 cycles).An opposite chargecarrier storage mechanism is rationalized for SCSs cathode to maximize spatial capacitive charge storage,involving high-kinetics physical Zn^(2+)/CF_(3)SO_(3)-adsorption and chemical Zn^(2+)redox with carbonyl/pyridine groups.This work gives insights into H-bond-guided interfacial superassembly design of superstructural carbons toward advanced energy storage.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20231445)Aeronautical Science Foundation of China(20230028052001).
文摘The proton exchange membrane fuel cell(PEMFC)and the hydrogen hybrid power system are studied by the fuzzy-PID(FPID)controlmethod and the fuzzy-PID controlmethod by Artificial Bee Colony algorithm(ABCFPID),respectively.The results reveal that compared with the FPID control method,the temperature overshoot of the PEMFC stack under the ABC-FPID control method is decreased by 0.6%.Moreover,the circulating water flow rate within the full operating envelope(about 3 min)is reduced by 19.46 L,which means the ABC-FPID control method is more effective in regulating the stack temperature.Then,the ABC-FPID control method is proposed to study the hydrogen hybrid power system,and the system output power matching,operating characteristic curve of the fuel cell,state of charge(SOC)of the lithium battery,system efficiency and hydrogen demand are obtained.The results indicate that the maximum system efficiency reaches 46.3%,the average system efficiency is 33.8%,and the average hydrogen demand is 0.192 kg/s.Overall,the ABC-FPID control method can efficiently ensure the stability of the fuel cell’s output power,and actively prompt the lithium battery to fulfill the function of“peak shaving and valley filling”under variable load power conditions.
基金co-supported by the Fundamental Research Funds for Central Universities,China(No.3072024XX0206)the Natural Science Foundation of Heilongjiang Province,China(No.LH2024E069)。
文摘A surface pyrolysis and gas-phase combustion of the Ammonium Perchlorate(AP)/Hydroxy Terminated Polybutadiene(HTPB)composite propellant reaction kinetic mechanism with five-step chemical reaction is adopted.The effects of helium injection on the burning rate and combustion of AP/HTPB propellant are analyzed in details,and the characteristics of motor performance are obtained.The numerical simulation results demonstrate that helium injection enhances the combustion chamber pressure,thereby increasing the burning rate of propellant.However,the primary combustion reaction of the AP/HTPB propellant takes place within a thin layer on the burning surface,so the low-temperature helium has minimal impact on the gasphase combustion.Ultimately,the helium not only elevates the nozzle exit velocity,resulting in specific impulse gain,but also reduces the exhaust plume temperature.With an increase of helium mass flow rate,the area of the velocity increase zone at the nozzle exit continuously decreases,but the average velocity in the motor exit continuously increases.Overall,when the helium flow rate is 2.5 kg/s,the specific impulse can reach 10.5%.Reducing the helium injection hole diameter enhances mixing of helium and combustion gas and expands the velocity increase zone,thereby maximizing the exit velocity gain in average velocity at the nozzle exit.When the injection hole diameter is reduced from 100 mm to 20 mm,the specific impulse gain increases from 3.1%to 10.6%.Furthermore,increasing helium injection temperature greatly boosts the velocity of the mixed gas with the same helium mass fraction ultimately improving specific impulse.
