Thick Electrodes of a Self-Assembled MXene Hydrogel Composite for High-Rate Energy Storage

Supercapacitors based on two-dimensional MXene(Ti_(3)C_(2)T_(z))have shown extraordinary performance in ultrathin electrodes with low mass loading,but usually there is a significant reduction in high-rate performance as the thickness increases,caused by increasing ion diffusion limitation.Further limitations include restacking of the nanosheets,which makes it challenging to realize the full potential of these electrode materials.Herein,we demonstrate the design of a vertically aligned MXene hydrogel composite,achieved by thermal-assisted self-assembled gelation,for high-rate energy storage.The highly interconnected MXene network in the hydrogel architecture provides very good electron transport properties,and its vertical ion channel structure facilitates rapid ion transport.The resulting hydrogel electrode show excellent performance in both aqueous and organic electrolytes with respect to high capacitance,stability,and high-rate capability for up to 300μm thick electrodes,which represents a significant step toward practical applications.

Phase Engineering of MXene Derivatives Via Molecular Design for High-Rate Sodium-Ion Batteries

Since 2019,research into MXene derivatives has seen a dramatic rise;further progress requires a rational design for specific functionality.Herein,through a molecular design by selecting suitable functional groups in the MXene coating,we have implemented the dual N doping of the derivatives,nitrogen-doped TiO_(2)@nitrogen-doped carbon nanosheets(N-TiO_(2)@NC),to strike a balance between the active anatase TiO_(2)at low temperatures,and carbon activation at high temperatures.The NH_(3)reduction environment generated at 400℃as evidenced by the in situ pyrolysis SVUV-PIMS process is crucial for concurrent phase engineering.With both electrical conductivity and surface Na+availability,the N-TiO_(2)@NC achieves higher interface capacitive-like sodium storage with long-term stability.More than 100 mAh g^(-1)is achieved at 2 A g^(-1)after 5000 cycles.The proposed design may be extended to other MXenes and solidify the growing family of MXene derivatives for energy storage.

Measuring ground deformations caused by 2015 Mw7.8 Nepal earthquake using high-rate GPS data

The April 25, 2015 Mw7.8 Nepal earthquake was successfully recorded by Crustal Movement Observation Network of China （CMONOC） and Nepal Geodetic Array （NGA）. We processed the high-rate GPS data （1 Hz and 5 Hz） by using relative kinematic positioning and derived dynamic ground motions caused by this large earthquake. The dynamic displacements time series clearly indicated the displacement amplitude of each station was related to the rupture directivity. The stations which located in the di- rection of rupture propagation had larger displacement amplitudes than others. Also dynamic ground displacement exceeding 5 cm was detected by the GPS station that was 2000 km away from the epicenter. Permanent coseismic displacements were resolved from the near-field high-rate GPS stations with wavelet decomposition-reconstruction method and P-wave arrivals were also detected with S transform method. The results of this study can be used for earthquake rupture process and Earthquake Early Warning studies.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion

In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.

Evaluation of ERA5 reanalysis temperature data over the Qilian Mountains of China

Air temperature is an important indicator to analyze climate change in mountainous areas.ERA5 reanalysis air temperature data are important products that were widely used to analyze temperature change in mountainous areas.However,the reliability of ERA5 reanalysis air temperature over the Qilian Mountains(QLM)is unclear.In this study,we evaluated the reliability of ERA5 monthly averaged reanalysis 2 m air temperature data using the observations at 17 meteorological stations in the QLM from 1979 to 2017.The results showed that:ERA5 reanalysis monthly averaged air temperature data have a good applicability in the QLM in general(R2=0.99).ERA5 reanalysis temperature data overestimated the observed temperature in the QLM in general.Root mean square error(RMSE)increases with the increasing of elevation range,showing that the reliability of ERA5 reanalysis temperature data is worse in higher elevation than that in lower altitude.ERA5 reanalysis temperature can capture observational warming rates well.All the smallest warming rates of observational temperature and ERA5 reanalysis temperature are found in winter,with the warming rates of 0.393°C/10a and 0.360°C/10a,respectively.This study will provide a reference for the application of ERA5 reanalysis monthly averaged air temperature data at different elevation ranges in the Qilian Mountains.

Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.

Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment

Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks

The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.

Designing N-doped graphene/ReSe_(2)/Ti_(3)C_(2) MXene heterostructure frameworks as promising anodes for high-rate potassium-ion batteries

Developing high-performance anodes for potassium ion batteries(KIBs) is of paramount significance but remains challenging.In the normal sense,electrode materials are prepared by ubiquitous wet chemical routes,which otherwise might not be versatile enough to create desired heterostructures and/or form clean interfacial areas for fast transport of K-ions and electrons.Along this line,rate capability/cycling stability of resulting KIBs are greatly handicapped.Herein we present an all-chemical vapor deposition approach to harness the direct synthesis of nitrogen-doped graphene(NG)/rhenium diselenide(ReSe_2)hybrids over three-dimensional MXene supports as superior heterostructure anode material for KIBs.In such an innovative design,1 T'-ReSe2 nanoparticles are sandwiched in between the NG coatings and MXene frameworks via strong interfacial interactions,thereby affording facile K~+ diffusion,enhancing overall conductivity,boosting high-power performance and reinforcing structural stability of electrodes.Thus-constructed anode delivers an excellent rate performance of 138 mAh g^(-1) at 10.0 A g^(-1) and a high reversible capacity of 90 mAh g^(-1) at 5 A g^(-1) after 300 cycles.Furthermore,the potassium storage mechanism has been systematically probed by advanced in situlex situ characterization techniques in combination with first principles computations.

Surface pseudocapacitance of mesoporous Mo_(3)N_(2) nanowire anode toward reversible high-rate sodium-ion storage

Sodium-ion storage devices are highly desirable for large-scale energy storage applications owing to the wide availability of sodium resources and low cost.Transition metal nitrides(TMNs)are promising anode materials for sodium-ion storage,while their detailed reaction mechanism remains unexplored.Herein,we synthesize the mesoporous Mo3N2 nanowires(Meso-Mo_(3)N_(2)-NWs).The sodium-ion storage mechanism of Mo3N2 is systematically investigated through in-situ XRD,ex-situ experimental characterizations and detailed kinetics analysis.Briefly,the Mo_(3)N_(2) undergoes a surface pseudocapacitive redox charge storage process.Benefiting from the rapid surface redox reaction,the Meso-Mo_(3)N_(2)-NWs anode delivers high specific capacity(282 m Ah g^(-1) at 0.1 A g^(-1)),excellent rate capability(87 m Ah g^(-1) at 16 A g^(-1))and long cycling stability(a capacity retention of 78.6%after 800 cycles at 1 A g^(-1)).The present work highlights that the surface pseudocapacitive sodium-ion storage mechanism enables to overcome the sluggish sodium-ion diffusion process,which opens a new direction to design and synthesize high-rate sodiumion storage materials.

Drying-Mediated Self-Assembly of Graphene for Inkjet Printing of High-Rate Micro-supercapacitors

Scalable fabrication of high-rate micro-supercapacitors(MSCs)is highly desired for on-chip integration of energy storage components.By virtue of the special self-assembly behavior of 2D materials during drying thin films of their liquid dispersion,a new inkjet printing technique of passivated graphene micro-flakes is developed to directly print MSCs with 3D networked porous microstructure.The presence of macroscale through-thickness pores provides fast ion transport pathways and improves the rate capability of the devices even with solid-state electrolytes.During multiple-pass printing,the porous microstructure effectively absorbs the successively printed inks,allowing full printing of 3D structured MSCs comprising multiple vertically stacked cycles of current collectors,electrodes,and sold-state electrolytes.The all-solid-state heterogeneous 3D MSCs exhibit excellent vertical scalability and high areal energy density and power density,evidently outperforming the MSCs fabricated through general printing techniques.

Architecture engineering of carbonaceous anodes for high-rate potassium-ion batteries

The limited lithium resource in earth's crust has stimulated the pursuit of alternative energy storage technologies to lithium-ion battery.Potassium-ion batteries(KIBs)are regarded as a kind of promising candidate for large-scale energy storage owing to the high abundance and low cost of potassium resources.Nevertheless,further development and wide application of KIBs are still challenged by several obstacles,one of which is their fast capacity deterioration at high rates.A considerable amount of effort has recently been devoted to address this problem by developing advanced carbonaceous anode materials with diverse structures and morphologies.This review presents and highlights how the architecture engineering of carbonaceous anode materials gives rise to high-rate performances for KIBs,and also the beneficial conceptions are consciously extracted from the recent progress.Particularly,basic insights into the recent engineering strategies,structural innovation,and the related advances of carbonaceous anodes for high-rate KIBs are under specific concerns.Based on the achievements attained so far,a perspective on the foregoing,and proposed possible directions,and avenues for designing high-rate anodes,are presented finally.

Effect of surface treatment on the structure and high-rate dischargeability properties of AB_5-type hydrogen storage alloy

Surface-treated MmNi3.55Co0.75Mn0.4Al0.3 alloy as negative electrode material of nickel-metal hydride battery was employed to improve the high-rate dischargeability. Surface treatment was realized by dipping and stirring the alloy into a HCl aqueous solution with various concentrations at room temperature. The microstructure of the alloy before and after surface treatment was analyzed by X-ray diffraction （XRD） and scanning electron microscopy （SEM）. The electrochemical properties before and after surface treatment were compared, and the alloy treated in 0.025 mol/L HCl solution showed the optimal high-rate dischargeability.

Tuning dual-atom mediator toward high-rate bidirectional polysulfide conversion in Li-S batteries

An emerging practice in the realm of Li-S batteries lies in the employment of single-atom catalysts(SACs)as effective mediators to promote polysulfide conversion,but monometallic SACs affording isolated geometric dispersion and sole electronic configuration limit the catalytic benefits and curtail the cell performance.Here,we propose a class of dual-atom catalytic moieties comprising hetero-or homo-atomic pairs anchored on N-doped graphene(NG)to unlock the liquid–solid redox puzzle of sulfur,readily realizing Li-S full cell under high-rate-charging conditions.As for Fe-Ni-NG,in-depth experimental and theoretical analysis reveal that the hetero-atomic orbital coupling leads to altered energy levels,unique electronic structures,and varied Fe oxidation states in comparison with homo-atomic structures(FeFe-NG or Ni-Ni-NG).This would weaken the bonding energy of polysulfide intermediates and thus enable facile electrochemical kinetics to gain rapid liquid-solid Li_(2)S_(4)?Li_(2)S conversion.Encouragingly,a Li-S battery based on the S@Fe-Ni-NG cathode demonstrates unprecedented fast-charging capability,documenting impressive rate performance(542.7 mA h g^(-1)at 10.0 C)and favorable cyclic stability(a capacity decay of 0.016%per cycle over 3000 cycles at 10.0 C).This finding offers insights to the rational design and application of dual-atom mediators for Li-S batteries.

Highly active CoP-CO_(2)N confined in nanocarbon enabling efficient electrocatalytic immobilizing-conversion of polysulfide targeting high-rate lithium-sulfur batteries

Lithium-sulfur batteries suffer from poor cycling stability because of the intrinsic shuttling effect of intermediate polysulfides and sluggish reaction kinetics,especially at high rates and high sulfur loading.Herein,we report the construction of a CoP-CO_(2)N@N-doped carbon polyhedron uniformly anchored on three-dimensional carbon nanotubes/graphene(CoP-CO_(2)N@NC/CG)scaffold as a sulfur reservoir to achieve the trapping-diffusion-conversion of polysulfides.Highly active CoP-CO_(2)N shows marvelous catalytic effects by effectively accelerating the reduction of sulfur and the oxidation of Li_(2)S during the discharging and charging process,respectively,while the conductive NC/CG network with massive mesoporous channels ensures fast and continuous long-distance electron/ion transportation.DFT calculations demonstrate that the CoP-CO_(2)N with excellent intrinsic conductivity serves as job-synergistic immobilizing-conversion sites for polysulfides through the formation of P…Li/N…Li and Co…S bonds.As a result,the S@CoP-CO_(2)N@NC/CG cathode(sulfur content 1.7 mg cm^(-2))exhibits a high capacity of988 mAh g^(-1)at 2 C after 500 cycles,which is superior to most of the electrochemical performance reported.Even under high sulfur content(4.3 mg cm^(-2)),it also shows excellent cyclability with high capacity at 1 C.