Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Prediction and scheduling of multi-energy microgrid based on BiGRU self-attention mechanism and LQPSO

To predict renewable energy sources such as solar power in microgrids more accurately,a hybrid power prediction method is presented in this paper.First,the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network(BiGRU)to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results.Subsequently,an improved quantum particle swarm optimization(QPSO)algorithm is proposed to optimize the hyperparameters of the combined prediction model.The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively.In addition,considering the coordinated utilization of various energy sources such as electricity,hydrogen,and renewable energy,a multi-objective optimization model that considers both economic and environmental costs was constructed.A two-stage adaptive multi-objective quantum particle swarm optimization algorithm aided by a Lévy flight,named MO-LQPSO,was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system.This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems.The effectiveness and superiority of the proposed scheme are verified through comparative simulations.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Evolutionary game dynamics of combining two different aspiration-driven update rules in structured populations

In evolutionary games,most studies on finite populations have focused on a single updating mechanism.However,given the differences in individual cognition,individuals may change their strategies according to different updating mechanisms.For this reason,we consider two different aspiration-driven updating mechanisms in structured populations:satisfied-stay unsatisfied shift(SSUS)and satisfied-cooperate unsatisfied defect(SCUD).To simulate the game player’s learning process,this paper improves the particle swarm optimization algorithm,which will be used to simulate the game player’s strategy selection,i.e.,population particle swarm optimization(PPSO)algorithms.We find that in the prisoner’s dilemma,the conditions that SSUS facilitates the evolution of cooperation do not enable cooperation to emerge.In contrast,SCUD conditions that promote the evolution of cooperation enable cooperation to emerge.In addition,the invasion of SCUD individuals helps promote cooperation among SSUS individuals.Simulated by the PPSO algorithm,the theoretical approximation results are found to be consistent with the trend of change in the simulation results.

Reversible Mn^(2+)/Mn^(4+)double-electron redox in P3-type layer-structured sodium-ion cathode

The balance between cationic redox and oxygen redox in layer-structured cathode materials is an important issue for sodium batteries to obtain high energy density and considerable cycle stability.Oxygen redox can contribute extra capacity to increase energy density,but results in lattice instability and capacity fading caused by lattice oxygen gliding and oxygen release.In this work,reversible Mn^(2+)/Mn^(4+)redox is realized in a P3-Na_(0.65)Li_(0.2)Co_(0.05)Mn_(0.75)O_(2)cathode material with high specific capacity and structure stability via Co substitution.The contribution of oxygen redox is suppressed significantly by reversible Mn^(2+)/Mn^(4+)redox without sacrificing capacity,thus reducing lattice oxygen release and improving the structure stability.Synchrotron X-ray techniques reveal that P3 phase is well maintained in a wide voltage window of 1.5-4.5 V vs.Na^(+)/Na even at 10 C and after long-term cycling.It is disclosed that charge compensation from Co/Mn-ions contributes to the voltage region below 4.2 V and O-ions contribute to the whole voltage range.The synergistic contributions of Mn^(2+)/Mn^(4+),Co^(2+)/Co^(3+),and O^(2-)/(O_n)^(2-)redox in P3-Na_(0.65)Li_(0.2)Co_(0.05)Mn_(0.75)O_(2)lead to a high reversible capacity of 215.0 m A h g^(-1)at 0.1 C with considerable cycle stability.The strategy opens up new opportunities for the design of high capacity cathode materials for rechargeable batteries.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Construction of core@double-shell structured energetic composites with simultaneously enhanced thermal stability and safety performance

The poor thermal stability and high sensitivity severely hinder the practical application of hexanitrohexaazaisowurtzitane(CL-20).Herein,a kind of novel core@double-shell CL-20 based energetic composites were fabricated to address the above issues.The coordination complexes which consist of natural polyphenol tannic acid(TA) and Fe~Ⅲ were chosen to construct the inner shell,while the graphene sheets were used to build the outer shell.The resulting CL-20/TA-Fe~Ⅲ/graphene composites exhibited simultaneously improved thermal stability and safety performance with only 1 wt% double-shell content,which should be ascribed to the intense physical encapsulation effect from inner shell combined with the desensitization effect of carbon nano-materials from outer shell.The phase transition(ε to γ) temperature increased from 173.70 ℃ of pure CL-20 to 191.87℃ of CL-20/TA-Fe~Ⅲ/graphene composites.Meanwhile,the characteristic drop height(H_(50)) dramatically increased from 14.7 cm of pure CL-20 to112.8 cm of CL-20/TA-Fe~Ⅲ/graphene composites,indicating much superior safety performance after the construction of the double-shell structure.In general,this work has provided an effective and versatile strategy to conquer the thermal stability and safety issues of CL-20 and contributes to the future application of high energy density energetic materials.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Algorithms and statistical analysis for linear structured weighted total least squares problem

Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.

Analysis of the Application Effect of Structured Healthcare Education in Brittle Diabetic Patients

Objective:To explore the application effect of structured healthcare education in patients with brittle diabetes mellitus.Methods:188 brittle diabetic patients admitted to our hospital from May 2021 to December 2023 were selected as the study subjects,and were divided into the control group(n=94)and the observation group(n=94)according to the random number table method.The control group used conventional nursing intervention and the observation group used structured healthcare education.The general information,glycemic indexes,self-efficacy,compliance,and nursing satisfaction of patients in the two groups were observed.Results:There was no statistical significance in the basic information of the two groups of patients(P>0.05);after the intervention,the fasting plasma glucose,2-hour postprandial blood glucose,and HbA1c of the patients in the observation group were lower than those of the control group(P<0.001);after the intervention,the self-efficacy scores of the patients in the two groups increased,and the scores of the observation group were significantly higher than those of the control group(P<0.001);the total adherence rate of the patients in the observation group(90/95.75%)was significantly higher than that of the control group(80/90.10%)(χ^(2)=6.144,P<0.05);and the total satisfaction rate of patients in the observation group(92/97.87%)was significantly higher than that of the control group(78/82.98%)(χ^(2)=12.042,P<0.05).Conclusion:In patients with brittle diabetes mellitus,structured healthcare education can effectively control patients’blood glucose levels,improve patients’self-efficacy and adherence,and enhance patient satisfaction.

Application of Structured Health Education and Combined 5A Nursing Model in Self-control of Vulnerable Chronic Diseases: Take Coronary Heart Disease as an Example

Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124 elderly CAD patients admitted to the First Affiliated Hospital of Bengbu Medical University were randomly divided into an experimental group and a control group.The control group line routine health education,experimental group take structured health education combined with 5A nursing before and after the intervention using a coronary heart disease assessment questionnaire,coronary heart disease self-control scale evaluation of two groups of intervention,compare two groups before and after intervention blood pressure,blood sugar,body mass index,lipid index level and complications within 8 months after discharge.Results:After the course intervention,the disease cognition and self-behavior of the experimental group were higher than that of the control group,and the differences were statistically significant(all P<0.1).Conclusion:This course is suitable for elderly patients with coronary heart disease.The 5A model improves the cognitive and management ability of elderly patients to a certain extent,which is worthy of clinical application.

Hollow Gradient-Structured Iron-Anchored Carbon Nanospheres for Enhanced Electromagnetic Wave Absorption

In the present paper,a microwave absorber with nanoscale gradient structure was proposed for enhancing the electromagnetic absorption performance.The inorganic-organic competitive coating strategy was employed,which can effectively adjust the thermodynamic and kinetic reactions of iron ions during the solvothermal process.As a result,Fe nanoparticles can be gradually decreased from the inner side to the surface across the hollow carbon shell.The results reveal that it offers an outstanding reflection loss value in combination with broadband wave absorption and flexible adjustment ability,which is superior to other relative graded distribution structures and satisfied with the requirements of lightweight equipment.In addition,this work elucidates the intrinsic microwave regulation mechanism of the multiscale hybrid electromagnetic wave absorber.The excellent impedance matching and moderate dielectric parameters are exhibited to be the dominative factors for the promotion of microwave absorption performance of the optimized materials.This strategy to prepare gradient-distributed microwave absorbing materials initiates a new way for designing and fabricating wave absorber with excellent impedance matching property in practical applications.

Beads-on-string hierarchical structured electrocatalysts for efficient oxygen reduction reaction

Rational design of hierarchically structured electrocatalysts is particularly important for electrocatalytic oxygen reduction reaction(ORR).Here,ZIF-67 crystals are stringed on core-shell Ag@C nanocables using a coordinationmodulated process.Upon pyrolysis,Ag@C strings of Co nanoparticles embedded with three-dimensional porous carbon with beads-on-string hierarchical structures are developed.Due to the advantages of the rich electrochemical active sites of Co-based“beads”and the efficient electron transfer pathways via Ag@C“strings,”the resultant NH_(3)-Ag@C@Co-N-C-700 catalyst shows an improved electrocatalytic activity toward ORR.NH_(3)-Ag@C@Co-N-C-700 shows a high onset potential of 0.99 V versus RHE,a high half-wave potential of 0.88 V versus RHE,and a large limiting current of 5.8 mA cm^(-2),which are better than those of commercial Pt/C electrocatalysts.Additionally,the NH_(3)-Ag@C@Co-N-C-700 catalyst shows high stability and preeminent methanol tolerance,which makes NH_(3)-Ag@C@Co-N-C-700 a promising catalyst for oxygen electrocatalysis in fuel cell applications.

Advancing the pressure sensing performance of conductive CNT/PDMS composite film by constructing a hierarchical-structured surface

Flexible pressure sensors have attracted wide attention due to their applications to electronic skin,health monitoring,and human-machine interaction.However,the tradeoff between their high sensitivity and wide response range remains a challenge.Inspired by human skin,we select commercial silicon carbide sandpaper as a template to fabricate carbon nanotube(CNT)/polydimethylsiloxane(PDMS)composite film with a hierarchical structured surface(h-CNT/PDMS)through solution blending and blade coating and then assemble the h-CNT/PDMS composite film with interdigitated electrodes and polyurethane(PU)scotch tape to obtain an h-CNT/PDMS-based flexible pressure sensor.Based on in-situ optical images and finite element analysis,the significant compressive contact effect between the hierarchical structured surface of h-CNT/PDMS and the interdigitated electrode leads to enhanced pressure sensitivity and a wider response range(0.1661 kPa^(-1),0.4574 kPa^(-1)and 0.0989 kPa^(-1)in the pressure range of 0–18 kPa,18–133 kPa and 133–300 kPa)compared with planar CNT/PDMS composite film(0.0066 kPa^(-1)in the pressure range of 0–240 kPa).The prepared pressure sensor displays rapid response/recovery time,excellent stability,durability,and stable response to different loading modes(bending and torsion).In addition,our pressure sensor can be utilized to accurately monitor and discriminate various stimuli ranging from human motions to pressure magnitude and spatial distribution.This study supplies important guidance for the fabrication of flexible pressure sensors with superior sensing performance in next-generation wearable electronic devices.

Cell-nucleus structured electrolyte for low-temperature aqueous zinc batteries

Rechargeable aqueous zinc(Zn) batteries hold great promise for large-scale energy storage,but their implementation is plagued by poor Zn reversibility and unsatisfactory low-temperature performance.Herein,we design a cell-nucleus structured electrolyte by introducing low-polarity 1,2-dimethoxyethane(DME) into dilute 1 M zinc trifluoromethanesulfonate(Zn(OTf)_(2)) aqueous solution,which features an OTf--rich Zn2^(+)-primary solvation sheath(PSS,inner nucleus) and the DMEmodulated Zn^(2+)-outer solvation sheath(outer layer).We find that DME additives with a low dosage do not participate in the Zn2+-PSS but reinforce the Zn-OTf-coordination,which guarantees good reaction kinetics under ultralow temperatures.Moreover,DME breaks the original H-bonding network of H2O,depressing the freezing point of electrolyte to-52.4℃.Such a cell-nucleus-solvation structure suppresses the H_(2)O-induced side reactions and forms an anion-derived solid electrolyte interphase on Zn and can be readily extended to 1,2-diethoxyethane.The as-designed electrolyte enables the Zn electrode deep cycling stability over 3500 h with a high depth-of-discharge of 51.3% and endows the Zn‖V_(2)O_(5)full battery with stable cycling over 1000 cycles at 40℃.This work would inspire the solvation structure design for low-temperature aqueous batteries.

Heterostructured Mn_(3)O_(4)-MnS Multi-Shelled Hollow Spheres for Enhanced Polysulfide Regulation in Lithium-Sulfur Batteries

Constructing heterojunctions and hollow multi-shelled structures can render materials with fascinating physicochemical properties,and have been regarded as two promising strategies to overcome the severe shuttling and sluggish kinetics of polysulfide in lithium-sulfur(Li-S)batteries.However,a single strategy can only take limited effect.Modulating catalytic hosts with synergistic effects are urgently desired.Herein,Mn_(3)O_(4)-MnS heterogeneous multi-shelled hollow spheres are meticulously designed by controlled sulfuration of Mn2O3 hollow spheres,and then applied as advanced encapsulation hosts for Li-S batteries.Benefiting from the separated spatial confinement by hollow multi-shelled structure,ample exposed active sites and built-in electric field by heterogeneous interface,and synergistic effects between Mn_(3)O_(4)(strong adsorption)and MnS(fast conversion)components,the assembled battery achieves prominent rate capability and decent cyclability(0.016%decay per cycle at 2 C,1000 cycles).More crucially,satisfactory areal capacity reaches up to 7.1 mAh cm^(-2)even with high sulfur loading(8.0 mg cm^(-2))and lean electrolyte(E/S=4.0 pL mg^(-1))conditions.This work will provide inspiration for the rational design of hollow multi-shelled heterostructure for various electrocatalysis applications.