As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-...As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-nomic and trade interactions.Nonetheless,the safety of train operations remains a paramount concern,prompting extensive research into the dynamic behavior of critical components,which is essential to ensuring seamless and secure transportation services.This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings.Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state,alongside delving into the research methodologies employed in developing multi-physics field coupling models.Subsequently,it expounds on the content of investigations focusing on various wheel and track impairments,grounded in the dynamic modeling of the bearing vehicle coupling system.Concurrently,the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated.Concludingly,the article underscores the inadequacy of current multi-source fault diagnosis meth-odologies in tackling the intricacies of complex train operating environments,thereby highlighting its sig-nificance as a pressing and vital research agenda for the future.展开更多
The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR ...The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.展开更多
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ...Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.展开更多
Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications...Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.展开更多
Rechargeable zinc(Zn)-ion batteries(RZIBs) with hydrogel electrolytes(HEs) have gained significant attention in the last decade owing to their high safety, low cost, sufficient material abundance, and superb environme...Rechargeable zinc(Zn)-ion batteries(RZIBs) with hydrogel electrolytes(HEs) have gained significant attention in the last decade owing to their high safety, low cost, sufficient material abundance, and superb environmental friendliness, which is extremely important for wearable energy storage applications. Given that HEs play a critical role in building flexible RZIBs, it is urgent to summarize the recent advances in this field and elucidate the design principles of HEs for practical applications. This review systematically presents the development history, recent advances in the material fundamentals, functional designs, challenges, and prospects of the HEs-based RZIBs. Firstly, the fundamentals, species, and flexible mechanisms of HEs are discussed, along with their compatibility with Zn anodes and various cathodes. Then, the functional designs of hydrogel electrolytes in harsh conditions are comprehensively discussed, including high/low/wide-temperature windows, mechanical deformations(e.g., bending, twisting, and straining), and damages(e.g., cutting, burning, and soaking). Finally, the remaining challenges and future perspectives for advancing HEs-based RZIBs are outlined.展开更多
Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,th...Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.展开更多
Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneit...Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.展开更多
To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
Interaction between the Yangtze River and its tributaries in the Three Gorges Reservoir has an important influence on tributary algal blooms.Taking the Xiaojiang River as a typical tributary,a binary mixing model used...Interaction between the Yangtze River and its tributaries in the Three Gorges Reservoir has an important influence on tributary algal blooms.Taking the Xiaojiang River as a typical tributary,a binary mixing model used stable isotopes of hydrogen and oxygen to quantitatively analyze the water contribution and nutrient source structure of the tributary backwater area.Results showed that the isotope content(δD:−54.7‰,δ^(18)O−7.8‰)in the Yangtze River was higher than that in the tributaries(δD:−74.2‰,δ^(18)O−17.0‰)in the non-flood season and lower than that in the tributaries in the flood season.The Yangtze River contributed more than 50%water volume of the tributary backwater area in the non-flood season.The total nitrogen and total phosphorus concentrations in the backwater area were estimated based on water contribution ratio,and the results were in good agreement with the monitoring results.Load estimation showed that the nitrogen and phosphorus contribution ratio of the Yangtze River to the tributary backwater area was approximately 40%-80%in the non-flood season,and approximately 20%-40%in the flood season,on average.This study showed that the interaction between the Xiaojiang River and the Yangtze River is significant,and that Yangtze River recharge is an important source of nutrients in the Xiaojiang backwater area,which may play a driving role in Xiaojiang River algal blooms.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on co...Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
The growing severity of environmental challenges has accelerated advancements in renewable energy technologies,highlighting the critical need for efficient energy storage solutions.Rechargeable batteries,as primary sh...The growing severity of environmental challenges has accelerated advancements in renewable energy technologies,highlighting the critical need for efficient energy storage solutions.Rechargeable batteries,as primary short-term energy storage devices,have seen significant progress.Among emerging optimization strategies,high-entropy electrolytes have garnered attention for their superior ionic conductivity and ability to broaden batteries’operational temperature ranges.Rooted in the thermodynamic concept of entropy,high-entropy materials,originally exemplified by high-entropy alloys,have demonstrated enhanced structural stability and advanced electrochemical performance through the synergistic integration of multiple components.High-entropy liquid electrolytes,both aqueous and non-aqueous,offer unique opportunities for entropy manipulation due to their inherently disordered structures.However,their complex compositions present challenges,as minor changes in formulation can lead to significant performance variations.This review introduces the fundamentals of entropy tuning,surveys recent advances in high-entropy liquid electrolytes,and analyzes the interplay between entropy and electrochemical behavior.Finally,it discusses design strategies and future perspectives for the practical implementation of high-entropy liquid electrolytes in next-generation energy storage systems.展开更多
Quantifying the spatial and temporal distribution of natural groundwater recharge is essential for effective groundwater modeling and sustainable resource management.This paper presents M-RechargeCal,a user-friendly s...Quantifying the spatial and temporal distribution of natural groundwater recharge is essential for effective groundwater modeling and sustainable resource management.This paper presents M-RechargeCal,a user-friendly software tool developed to estimate natural groundwater recharge using two widely adopted approaches:the Water Balance(WB)method and Water Table Fluctuation(WTF)method.In the WB approach,the catchment area is divided into seven land-use categories,each representing distinct recharge characteristics.The tool includes eighteen different reference Evapotranspiration(ET0)estimation methods,accommodating varying levels of climatic input data availability.Additional required inputs include crop coefficients for major crops and Curve Numbers(CN)for specific land-use types.The WTF approach considers up to three aquifer layers with different specific yields(for unconfined aquifer)or storage coeffi-cient(for confined aquifer).It also takes into account groundwater withdrawal(draft)and lateral water movement within or outside the aquifer system.M-RechargeCal is process-based and does not require cali-bration.Its performance was evaluated using six datasets from humid-subtropical environments,demon-2 strating reliable results(R=0.867,r=0.93,RE=10.6%,PMARE=9.8,ENS=0.93).The model can be applied to defined hydrological or hydrogeological units such as watersheds,aquifers,or catchments,and can be used to assess the impacts of land-use/land-cover changes on hydrological components.However,it has not yet been tested in arid regions.M-RechargeCal provides modelers and planners with a practical,accessible tool for recharge estimation to support groundwater modeling and water resource planning.The software is available free of charge and can be downloaded from the author's institutional website or obtained by contacting the author via email.展开更多
This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)perio...This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.展开更多
Increased population mobility in urban areas drives higher water demand and significant changes in Land Use and Land Cover(LULC),which directly impact groundwater recharge capacity.This study aims to predict LULC chan...Increased population mobility in urban areas drives higher water demand and significant changes in Land Use and Land Cover(LULC),which directly impact groundwater recharge capacity.This study aims to predict LULC changes in 2030 and 2040,analyse groundwater recharge quantities for historical,current,and projected conditions,and evaluate the combined impacts of LULC and climate change.The Cellular Automata-Artificial Neural Network(CA-ANN)method was employed to predict LULC changes,using classified and interpreted land use data from Landsat 7 ETM+(2000 and 2010)and Landsat 8 OLI(2020)imagery.The Soil and Water Assessment Tool(SWAT)model was used to simulate groundwater recharge.Input data for the SWAT model included Digital Elevation Model(DEM),soil type,LULC,slope,and climate data.Climate projections were based on five Regional Climate Models(RCMs)for two time periods,2021–2030 and 2031–2040,under Shared Socioeconomic Pathways(SSP)scenarios 2–45 and 5–85.The results indicate a significant increase in built-up areas,accounting for 71.08%in 2030 and 71.83%in 2040.Groundwater recharge projections show a decline,with average monthly recharge decreas-ing from 83.85 mm/month under SSP2-45 to 78.25 mm/month under SSP5-85 in 2030,and further declin-ing to 82.10 mm/month(SSP2-45)and 77.44 mm/month(SSP5-85)in 2040.The expansion of impervious surfaces due to urbanization is the primary factor driving this decline.This study highlights the innovative integration of CA-ANN-based LULC predictions with climate projections from RCMs,offering a robust framework for analysing urban groundwater dynamics.The findings underscore the need for sustainable urban planning and water resource management to mitigate the adverse effects of urbanization and climate change.Additionally,the methodological framework and insights gained from this research can be applied to other urban areas facing similar challenges,thus contributing to broader efforts in groundwater conserva-tion.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
Antimony(Sb)is regarded as a potential candidate for next-generation anode materials for rechargeable batteries because it has a high theoretical specific capacity,excellent conductivity and appropriate reaction poten...Antimony(Sb)is regarded as a potential candidate for next-generation anode materials for rechargeable batteries because it has a high theoretical specific capacity,excellent conductivity and appropriate reaction potential.However,Sb-based anodes suffer from severe volume expansion of>135%during the lithiation-delithiation process.Hence,we construct a novel Sb@C composite encapsulating the Sb nanoparticles into highly conductive three-dimensional porous carbon frameworks via the one-step magnesiothermic reduction(MR).The porous carbon provides buffer spaces to accommodate the volume expansion of Sb.Meanwhile,the three-dimensional(3D)interconnected carbon frameworks shorten the ion/electron transport pathway and inhibit the overgrowth of unstable solid-electrolyte interfaces(SEIs).Consequently,the 3D Sb@C composite displays remarkable electrochemical performance,including a high average Coulombic efficiency(CE)of>99%,high initial capability of 989 mAh·g^(-1),excellent cycling stability for over 1000 cycles at a high current density of 5 A·g^(-1).Furthermore,employing a similar approach,this 3D Sb@C design paradigm holds promise for broader applications across fast-charging and ultralong-life battery systems beyond Li+.This work aims to advance practical applications for Sb-based anodes in next-generation batteries.展开更多
Energy storage plays a critical role in sustainable development,with secondary batteries serving as vital technologies for efficient energy conversion and utilization.This review provides a comprehensive summary of re...Energy storage plays a critical role in sustainable development,with secondary batteries serving as vital technologies for efficient energy conversion and utilization.This review provides a comprehensive summary of recent advancements across various battery systems,including lithium-ion,sodium-ion,potassium-ion,and multivalent metal-ion batteries such as magnesium,zinc,calcium,and aluminum.Emerging technologies,including dual-ion,redox flow,and anion batteries,are also discussed.Particular attention is given to alkali metal rechargeable systems,such as lithium-sulfur,lithium-air,sodium-sulfur,sodium-selenium,potassium-sulfur,potassium-selenium,potassium-air,and zinc-air batteries,which have shown significant promise for high-energy applications.The optimization of key components—cathodes,anodes,electrolytes,and interfaces—is extensively analyzed,supported by advanced characterization techniques like time-of-flight secondary ion mass spectrometry(TOF-SIMS),synchrotron radiation,nuclear magnetic resonance(NMR),and in-situ spectroscopy.Moreover,sustainable strategies for recycling spent batteries,including pyrometallurgy,hydrometallurgy,and direct recycling,are critically evaluated to mitigate environmental impacts and resource scarcity.This review not only highlights the latest technological breakthroughs but also identifies key challenges in reaction mechanisms,material design,system integration,and waste battery recycling,and presents a roadmap for advancing high-performance and sustainable battery technologies.展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.12393783,12302067,12172235,52072249)Joint Funds of the National Natural Science Foundation of China(Grant No.U24A2003)+3 种基金College Education Scientific Research Project of Hebei Province(Grant No.JZX2024006)Central Guiding Local Scientific and Technological Development Funding Project(Grant No.246Z2206G)the Key Research Project of China State Railway Group Co.,Ltd.(Grant No.N2024T009)S&T Program of Hebei(Grant No.21567622H).
文摘As China's high-speed railway technology advances,high-speed trains have emerged as a pivotal mode of transportation,instrumental in facilitating passenger and freight mobility while fostering robust regional eco-nomic and trade interactions.Nonetheless,the safety of train operations remains a paramount concern,prompting extensive research into the dynamic behavior of critical components,which is essential to ensuring seamless and secure transportation services.This article commences by comprehensively reviewing the current landscape and evolutionary trajectory of dynamic model analysis for both traditional bearings and axle box bearings.Emphasis is placed on elucidating the profound influence of diverse bearing fault types on the system's kinematic state,alongside delving into the research methodologies employed in developing multi-physics field coupling models.Subsequently,it expounds on the content of investigations focusing on various wheel and track impairments,grounded in the dynamic modeling of the bearing vehicle coupling system.Concurrently,the intricate interplay between wheel-rail excitation and axle box bearing faults on the system's performance is elucidated.Concludingly,the article underscores the inadequacy of current multi-source fault diagnosis meth-odologies in tackling the intricacies of complex train operating environments,thereby highlighting its sig-nificance as a pressing and vital research agenda for the future.
基金sponsored by the National Natural Science Foundation of China(Grant No.52178100).
文摘The spatial offset of bridge has a significant impact on the safety,comfort,and durability of high-speed railway(HSR)operations,so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges.Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality,cost-effectiveness,and efficiency.However,existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source,low detection accuracy,and the inability to identify lateral deformations of bridges.This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train.Firstly,dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals,and explore the hidden temporal relationships in the data.Subsequently,a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge.A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train-track-bridge interaction model,and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated.Finally,the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train.The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge,ensuring the long-term operational safety of HSR bridges.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00406320)the Institute of Information&Communica-tions Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization Program Grant funded by the Korea government(MSIT)(IITP-2026-RS-2023-00259678).
文摘Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance.
基金Supported by the National Natural Science Foundation of China(Nos.42376185,41876111)the Shandong Provincial Natural Science Foundation(No.ZR2023MD073)。
文摘Benthic habitat mapping is an emerging discipline in the international marine field in recent years,providing an effective tool for marine spatial planning,marine ecological management,and decision-making applications.Seabed sediment classification is one of the main contents of seabed habitat mapping.In response to the impact of remote sensing imaging quality and the limitations of acoustic measurement range,where a single data source does not fully reflect the substrate type,we proposed a high-precision seabed habitat sediment classification method that integrates data from multiple sources.Based on WorldView-2 multi-spectral remote sensing image data and multibeam bathymetry data,constructed a random forests(RF)classifier with optimal feature selection.A seabed sediment classification experiment integrating optical remote sensing and acoustic remote sensing data was carried out in the shallow water area of Wuzhizhou Island,Hainan,South China.Different seabed sediment types,such as sand,seagrass,and coral reefs were effectively identified,with an overall classification accuracy of 92%.Experimental results show that RF matrix optimized by fusing multi-source remote sensing data for feature selection were better than the classification results of simple combinations of data sources,which improved the accuracy of seabed sediment classification.Therefore,the method proposed in this paper can be effectively applied to high-precision seabed sediment classification and habitat mapping around islands and reefs.
基金supported by the National Natural Science Foundation of China (22379038)Science Research Project of Hebei Education Department (JZX2024015)+4 种基金Shijiazhuang Science and Technology Plan Project (241791357A)Central Guidance for Local Science and Technology Development Funds Project (246Z4408G)Excellent Youth Research Innovation Team of Hebei University (QNTD202410)High-level Talents Research Start-Up Project of Hebei University (521100224223)Hebei Province Innovation Capability Enhancement Plan Project (22567620H)。
文摘Rechargeable zinc(Zn)-ion batteries(RZIBs) with hydrogel electrolytes(HEs) have gained significant attention in the last decade owing to their high safety, low cost, sufficient material abundance, and superb environmental friendliness, which is extremely important for wearable energy storage applications. Given that HEs play a critical role in building flexible RZIBs, it is urgent to summarize the recent advances in this field and elucidate the design principles of HEs for practical applications. This review systematically presents the development history, recent advances in the material fundamentals, functional designs, challenges, and prospects of the HEs-based RZIBs. Firstly, the fundamentals, species, and flexible mechanisms of HEs are discussed, along with their compatibility with Zn anodes and various cathodes. Then, the functional designs of hydrogel electrolytes in harsh conditions are comprehensively discussed, including high/low/wide-temperature windows, mechanical deformations(e.g., bending, twisting, and straining), and damages(e.g., cutting, burning, and soaking). Finally, the remaining challenges and future perspectives for advancing HEs-based RZIBs are outlined.
基金support of the National Natural Science Foundation of China(Grant No.22225801,22178217 and 22308216)supported by the Fundamental Research Funds for the Central Universities,conducted at Tongji University.
文摘Rechargeable magnesium batteries(RMBs)have been considered a promising“post lithium-ion battery”system to meet the rapidly increasing demand of the emerging electric vehicle and grid energy storage market.However,the sluggish diffusion kinetics of bivalent Mg^(2+)in the host material,related to the strong Coulomb effect between Mg^(2+)and host anion lattices,hinders their further development toward practical applications.Defect engineering,regarded as an effective strategy to break through the slow migration puzzle,has been validated in various cathode materials for RMBs.In this review,we first thoroughly understand the intrinsic mechanism of Mg^(2+)diffusion in cathode materials,from which the key factors affecting ion diffusion are further presented.Then,the positive effects of purposely introduced defects,including vacancy and doping,and the corresponding strategies for introducing various defects are discussed.The applications of defect engineering in cathode materials for RMBs with advanced electrochemical properties are also summarized.Finally,the existing challenges and future perspectives of defect engineering in cathode materials for the overall high-performance RMBs are described.
基金Supported by the National Natural Science Foundation of China(42401488,42071351)the National Key Research and Development Program of China(2020YFA0608501,2017YFB0504204)+4 种基金the Liaoning Revitalization Talents Program(XLYC1802027)the Talent Recruited Program of the Chinese Academy of Science(Y938091)the Project Supported Discipline Innovation Team of the Liaoning Technical University(LNTU20TD-23)the Liaoning Province Doctoral Research Initiation Fund Program(2023-BS-202)the Basic Research Projects of Liaoning Department of Education(JYTQN2023202)。
文摘Accurate estimation of understory terrain has significant scientific importance for maintaining ecosystem balance and biodiversity conservation.Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit,this study pro⁃poses a differentiated modeling approach to forest types based on refined land cover classification.Taking Puerto Ri⁃co and Maryland as study areas,a multi-dimensional feature system is constructed by integrating multi-source re⁃mote sensing data:ICESat-2 spaceborne LiDAR is used to obtain benchmark values for understory terrain,topo⁃graphic factors such as slope and aspect are extracted based on SRTM data,and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery.This study incorporates forest type as a classification modeling con⁃dition and applies the random forest algorithm to build differentiated topographic inversion models.Experimental re⁃sults indicate that,compared to traditional whole-area modeling methods(RMSE=5.06 m),forest type-based classi⁃fication modeling significantly improves the accuracy of understory terrain estimation(RMSE=2.94 m),validating the effectiveness of spatial heterogeneity modeling.Further sensitivity analysis reveals that canopy structure parame⁃ters(with RMSE variation reaching 4.11 m)exert a stronger regulatory effect on estimation accuracy compared to forest cover,providing important theoretical support for optimizing remote sensing models of forest topography.
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金supported by the National Natural Science Foundation of China(No.U2040210).
文摘Interaction between the Yangtze River and its tributaries in the Three Gorges Reservoir has an important influence on tributary algal blooms.Taking the Xiaojiang River as a typical tributary,a binary mixing model used stable isotopes of hydrogen and oxygen to quantitatively analyze the water contribution and nutrient source structure of the tributary backwater area.Results showed that the isotope content(δD:−54.7‰,δ^(18)O−7.8‰)in the Yangtze River was higher than that in the tributaries(δD:−74.2‰,δ^(18)O−17.0‰)in the non-flood season and lower than that in the tributaries in the flood season.The Yangtze River contributed more than 50%water volume of the tributary backwater area in the non-flood season.The total nitrogen and total phosphorus concentrations in the backwater area were estimated based on water contribution ratio,and the results were in good agreement with the monitoring results.Load estimation showed that the nitrogen and phosphorus contribution ratio of the Yangtze River to the tributary backwater area was approximately 40%-80%in the non-flood season,and approximately 20%-40%in the flood season,on average.This study showed that the interaction between the Xiaojiang River and the Yangtze River is significant,and that Yangtze River recharge is an important source of nutrients in the Xiaojiang backwater area,which may play a driving role in Xiaojiang River algal blooms.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
基金supported by the Sichuan Science and Technology Program(Nos.2024JDRC0100 and 2023YFQ0091)the National Natural Science Foundation of China(Nos.U21A20167 and 52475138)the Scientific Research Foundation of the State Key Laboratory of Rail Transit Vehicle System(No.2024RVL-T08).
文摘Accurate monitoring of track irregularities is very helpful to improving the vehicle operation quality and to formulating appropriate track maintenance strategies.Existing methods have the problem that they rely on complex signal processing algorithms and lack multi-source data analysis.Driven by multi-source measurement data,including the axle box,the bogie frame and the carbody accelerations,this paper proposes a track irregularities monitoring network(TIMNet)based on deep learning methods.TIMNet uses the feature extraction capability of convolutional neural networks and the sequence map-ping capability of the long short-term memory model to explore the mapping relationship between vehicle accelerations and track irregularities.The particle swarm optimization algorithm is used to optimize the network parameters,so that both the vertical and lateral track irregularities can be accurately identified in the time and spatial domains.The effectiveness and superiority of the proposed TIMNet is analyzed under different simulation conditions using a vehicle dynamics model.Field tests are conducted to prove the availability of the proposed TIMNet in quantitatively monitoring vertical and lateral track irregularities.Furthermore,comparative tests show that the TIMNet has a better fitting degree and timeliness in monitoring track irregularities(vertical R2 of 0.91,lateral R2 of 0.84 and time cost of 10 ms),compared to other classical regression.The test also proves that the TIMNet has a better anti-interference ability than other regression models.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(N_PolyU559/21)a grant from the National Natural Science Foundation of China(52161160333)+1 种基金a grant from the Research Institute for Smart Energy at The Hong Kong Polytechnic University(CDB2)supported by the Hong Kong PhD Fellowship Scheme(PF21-65328).
文摘The growing severity of environmental challenges has accelerated advancements in renewable energy technologies,highlighting the critical need for efficient energy storage solutions.Rechargeable batteries,as primary short-term energy storage devices,have seen significant progress.Among emerging optimization strategies,high-entropy electrolytes have garnered attention for their superior ionic conductivity and ability to broaden batteries’operational temperature ranges.Rooted in the thermodynamic concept of entropy,high-entropy materials,originally exemplified by high-entropy alloys,have demonstrated enhanced structural stability and advanced electrochemical performance through the synergistic integration of multiple components.High-entropy liquid electrolytes,both aqueous and non-aqueous,offer unique opportunities for entropy manipulation due to their inherently disordered structures.However,their complex compositions present challenges,as minor changes in formulation can lead to significant performance variations.This review introduces the fundamentals of entropy tuning,surveys recent advances in high-entropy liquid electrolytes,and analyzes the interplay between entropy and electrochemical behavior.Finally,it discusses design strategies and future perspectives for the practical implementation of high-entropy liquid electrolytes in next-generation energy storage systems.
文摘Quantifying the spatial and temporal distribution of natural groundwater recharge is essential for effective groundwater modeling and sustainable resource management.This paper presents M-RechargeCal,a user-friendly software tool developed to estimate natural groundwater recharge using two widely adopted approaches:the Water Balance(WB)method and Water Table Fluctuation(WTF)method.In the WB approach,the catchment area is divided into seven land-use categories,each representing distinct recharge characteristics.The tool includes eighteen different reference Evapotranspiration(ET0)estimation methods,accommodating varying levels of climatic input data availability.Additional required inputs include crop coefficients for major crops and Curve Numbers(CN)for specific land-use types.The WTF approach considers up to three aquifer layers with different specific yields(for unconfined aquifer)or storage coeffi-cient(for confined aquifer).It also takes into account groundwater withdrawal(draft)and lateral water movement within or outside the aquifer system.M-RechargeCal is process-based and does not require cali-bration.Its performance was evaluated using six datasets from humid-subtropical environments,demon-2 strating reliable results(R=0.867,r=0.93,RE=10.6%,PMARE=9.8,ENS=0.93).The model can be applied to defined hydrological or hydrogeological units such as watersheds,aquifers,or catchments,and can be used to assess the impacts of land-use/land-cover changes on hydrological components.However,it has not yet been tested in arid regions.M-RechargeCal provides modelers and planners with a practical,accessible tool for recharge estimation to support groundwater modeling and water resource planning.The software is available free of charge and can be downloaded from the author's institutional website or obtained by contacting the author via email.
文摘This research examines the hard-rock aquifer system within the Nagavathi River Basin(NRB)South India,by evaluating seasonal fluctuations in groundwater composition during the pre-monsoon(PRM)and post-monsoon(POM)periods.Seasonal variations significantly influence the groundwater quality,particularly fluoride(F−)concentrations,which can fluctuate due to changes in recharge,evaporation,and anthropogenic activities.This study assesses the dynamics of F−levels in PRM and POM seasons,and identifies elevated health risks using USEPA guidelines and Monte Carlo Simulations(MCS).Groundwater in the study area exhibits alkaline pH,with NaCl and Ca-Na-HCO_(3) facies increasing in the POM season due to intensified ion exchange and rock-water interactions,as indicated in Piper and Gibb’s diagrams.Correlation and dendrogram analyses indicate that F−contamination is from geogenic and anthropogenic sources.F−levels exceed the WHO limit(1.5 mg/L)in 51 PRM and 28 POM samples,affecting 371.74 km^(2) and 203.05 km^(2),respectively.Geochemical processes,including mineral weathering,cation exchange,evaporation,and dilution,are identified through CAI I&II.Health risk assessments reveal that HQ values>1 in 78%of children,73%of teens,and 68%of adults during PRM,decreasing to 45%,40%,and 38%,respectively,in POM.MCS show maximum HQ values of 5.67(PRM)and 4.73(POM)in children,with all age groups facing significant risks from fluoride ingestion.Managed Aquifer Recharge(MAR)is recommended in this study to minimize F−contamination,ensuring safe drinking water for the community.
文摘Increased population mobility in urban areas drives higher water demand and significant changes in Land Use and Land Cover(LULC),which directly impact groundwater recharge capacity.This study aims to predict LULC changes in 2030 and 2040,analyse groundwater recharge quantities for historical,current,and projected conditions,and evaluate the combined impacts of LULC and climate change.The Cellular Automata-Artificial Neural Network(CA-ANN)method was employed to predict LULC changes,using classified and interpreted land use data from Landsat 7 ETM+(2000 and 2010)and Landsat 8 OLI(2020)imagery.The Soil and Water Assessment Tool(SWAT)model was used to simulate groundwater recharge.Input data for the SWAT model included Digital Elevation Model(DEM),soil type,LULC,slope,and climate data.Climate projections were based on five Regional Climate Models(RCMs)for two time periods,2021–2030 and 2031–2040,under Shared Socioeconomic Pathways(SSP)scenarios 2–45 and 5–85.The results indicate a significant increase in built-up areas,accounting for 71.08%in 2030 and 71.83%in 2040.Groundwater recharge projections show a decline,with average monthly recharge decreas-ing from 83.85 mm/month under SSP2-45 to 78.25 mm/month under SSP5-85 in 2030,and further declin-ing to 82.10 mm/month(SSP2-45)and 77.44 mm/month(SSP5-85)in 2040.The expansion of impervious surfaces due to urbanization is the primary factor driving this decline.This study highlights the innovative integration of CA-ANN-based LULC predictions with climate projections from RCMs,offering a robust framework for analysing urban groundwater dynamics.The findings underscore the need for sustainable urban planning and water resource management to mitigate the adverse effects of urbanization and climate change.Additionally,the methodological framework and insights gained from this research can be applied to other urban areas facing similar challenges,thus contributing to broader efforts in groundwater conserva-tion.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
基金supported by the National Natural Science Foundation of China(No.22309056)the National Key R&.D Program of China(No.2022YFB2404800)+4 种基金the Basic Research Program of Shenzhen Municipal Science and Technology Innovation Committee(No.JCYJ20210324141613032)the Knowledge Innovation Project of Wuhan City(No.2022010801010303)the City University of Hong Kong Strategic Research Grant(SRG),Hong Kong,China(No.7005505)the City University of Hong Kong Donation Research Grant,Hong Kong,China(No.DON-RMG 9229021)the Postdoctoral Fellowship Program of CPSF(No.GZB20230552).
文摘Antimony(Sb)is regarded as a potential candidate for next-generation anode materials for rechargeable batteries because it has a high theoretical specific capacity,excellent conductivity and appropriate reaction potential.However,Sb-based anodes suffer from severe volume expansion of>135%during the lithiation-delithiation process.Hence,we construct a novel Sb@C composite encapsulating the Sb nanoparticles into highly conductive three-dimensional porous carbon frameworks via the one-step magnesiothermic reduction(MR).The porous carbon provides buffer spaces to accommodate the volume expansion of Sb.Meanwhile,the three-dimensional(3D)interconnected carbon frameworks shorten the ion/electron transport pathway and inhibit the overgrowth of unstable solid-electrolyte interfaces(SEIs).Consequently,the 3D Sb@C composite displays remarkable electrochemical performance,including a high average Coulombic efficiency(CE)of>99%,high initial capability of 989 mAh·g^(-1),excellent cycling stability for over 1000 cycles at a high current density of 5 A·g^(-1).Furthermore,employing a similar approach,this 3D Sb@C design paradigm holds promise for broader applications across fast-charging and ultralong-life battery systems beyond Li+.This work aims to advance practical applications for Sb-based anodes in next-generation batteries.
基金supported by the National Natural Science Foundation of China(Nos.U21A20311 and 22409147)。
文摘Energy storage plays a critical role in sustainable development,with secondary batteries serving as vital technologies for efficient energy conversion and utilization.This review provides a comprehensive summary of recent advancements across various battery systems,including lithium-ion,sodium-ion,potassium-ion,and multivalent metal-ion batteries such as magnesium,zinc,calcium,and aluminum.Emerging technologies,including dual-ion,redox flow,and anion batteries,are also discussed.Particular attention is given to alkali metal rechargeable systems,such as lithium-sulfur,lithium-air,sodium-sulfur,sodium-selenium,potassium-sulfur,potassium-selenium,potassium-air,and zinc-air batteries,which have shown significant promise for high-energy applications.The optimization of key components—cathodes,anodes,electrolytes,and interfaces—is extensively analyzed,supported by advanced characterization techniques like time-of-flight secondary ion mass spectrometry(TOF-SIMS),synchrotron radiation,nuclear magnetic resonance(NMR),and in-situ spectroscopy.Moreover,sustainable strategies for recycling spent batteries,including pyrometallurgy,hydrometallurgy,and direct recycling,are critically evaluated to mitigate environmental impacts and resource scarcity.This review not only highlights the latest technological breakthroughs but also identifies key challenges in reaction mechanisms,material design,system integration,and waste battery recycling,and presents a roadmap for advancing high-performance and sustainable battery technologies.
