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
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter...With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.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
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
Fault-tolerant systems are crucial for ensuring the reliability and availability of missioncritical applications in modern computing environments.The dynamic heterogeneous redundancy(DHR)architecture is a key componen...Fault-tolerant systems are crucial for ensuring the reliability and availability of missioncritical applications in modern computing environments.The dynamic heterogeneous redundancy(DHR)architecture is a key component in constructing fault-tolerant systems,particularly in areas such as national security,power networks,and banking private networks.DHR is transforming the cyberspace security industry chain by accommodating a broader range of applications and increasingly capturing the market.However,the development of applications for DHR architecture encounters challenges due to the complexities of handling heterogeneity,managing dynamism,and maintaining usability.To address these issues,we introduce MimicStudio,a comprehensive development framework with a standardized workflow.To our knowledge,MimicStudio is the first effective solution for DHR software development.We present a detailed implementation of MimicStudio with a heterogeneous microcontroller unit project,encompassing three CPUs with different instruction set architectures.The paper evaluates MimicStudio’s support for essential features,including zero-copy synchronization,parallelized build,multi-core collaborative debugging,and dynamic adjustment of the software system’s structure.Our results show that MimicStudio provides a flexible and efficient solution for supporting the dynamic,heterogeneous,and redundant features of fault-tolerant systems.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
Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitionin...Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.展开更多
Currently,the demand for electromagnetic wave(EMW)absorbing materials with specific functions and capable of withstanding harsh environments is becoming increasingly urgent.Multi-component interface engineering is con...Currently,the demand for electromagnetic wave(EMW)absorbing materials with specific functions and capable of withstanding harsh environments is becoming increasingly urgent.Multi-component interface engineering is considered an effective means to achieve high-efficiency EMW absorption.However,interface modulation engineering has not been fully discussed and has great potential in the field of EMW absorption.In this study,multi-component tin compound fiber composites based on carbon fiber(CF)substrate were prepared by electrospinning,hydrothermal synthesis,and high-temperature thermal reduction.By utilizing the different properties of different substances,rich heterogeneous interfaces are constructed.This effectively promotes charge transfer and enhances interfacial polarization and conduction loss.The prepared SnS/SnS_(2)/SnO_(2)/CF composites with abundant heterogeneous interfaces have and exhibit excellent EMW absorption properties at a loading of 50 wt%in epoxy resin.The minimum reflection loss(RL)is−46.74 dB and the maximum effective absorption bandwidth is 5.28 GHz.Moreover,SnS/SnS_(2)/SnO_(2)/CF epoxy composite coatings exhibited long-term corrosion resistance on Q235 steel surfaces.Therefore,this study provides an effective strategy for the design of high-efficiency EMW absorbing materials in complex and harsh environments.展开更多
Heterogeneous metal-catalyzed chemical conversions with a recyclable catalyst are very ideal and challenging for sustainable organic synthesis.A new bipyridyl-Mo(IV)-carbon nitride(CN-K/Mo-Bpy)was prepared by supporti...Heterogeneous metal-catalyzed chemical conversions with a recyclable catalyst are very ideal and challenging for sustainable organic synthesis.A new bipyridyl-Mo(IV)-carbon nitride(CN-K/Mo-Bpy)was prepared by supporting molybdenum complex on C_(3)N_(4)-K and characterized by FT-IR,XRD,SEM,XPS and ICP-OES.Heterogeneous CN–Mo-Bpy catalyst can be applied to the direct amination of nitroarenes and arylboronic acid,thus constructing various valuable diarylamines in high to excellent yields with a wide substrate scope and good functional group tolerance.It is worth noting that this heterogeneous catalyst has high chemical stability and can be recycled for at least five times without reducing its activity.展开更多
Heterogeneous crystallization is a common occurrence during the formation of solidwastes.It leads to the encapsulation of valuable/hazardous metals within the primary phase,presenting significant challenges for waste ...Heterogeneous crystallization is a common occurrence during the formation of solidwastes.It leads to the encapsulation of valuable/hazardous metals within the primary phase,presenting significant challenges for waste treatment andmetal recovery.Herein,we proposed a novel method involving the in-situ formation of a competitive substrate during the precipitation of jarosite waste,which is an essential process for removing iron in zinc hydrometallurgy.We observed that the in-situ-formed competitive substrate effectively inhibits the heterogeneous crystallization of jarosite on the surface of anglesite,a lead-rich phase present in the jarositewaste.As a result,the iron content on the anglesite surface decreases from34.8%to 1.65%.The competitive substrate was identified as schwertmannite,characterized by its loose structure and large surface area.Furthermore,we have elucidated a novel mechanism underlying this inhibition of heterogeneous crystallization,which involves the local supersaturation of jarosite caused by the release of ferric and sulfate ions from the competitive substrate.The local supersaturation promotes the preferential heterogeneous crystallization of jarosite on the competitive substrate.Interestingly,during the formation of jarosite,the competitive substrate gradually vanished through a dissolution-recrystallization process following the Ostwald rule,where a metastable phase slowly transitions to a stable phase.This effectively precluded the introduction of impurities and reduced waste volume.The goal of this study is to provide fresh insights into the mechanism of heterogeneous crystallization control,and to offer practical crystallization strategies conducive to metal separation and recovery from solid waste in industries.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Dynamic structuralcolors can change in response todifferent environmental stimuli.This ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,prov...Dynamic structuralcolors can change in response todifferent environmental stimuli.This ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,providing a promising sensingmechanism for solving microenvironmentalsensing problems inmicro-robotics and microfluidics.However, the lack of dynamicstructural colors that can encoderapidly, easily integrate, and accuratelyreflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensionaldynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogelwith an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollenstate, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structuralcolors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamicstructural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.展开更多
Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the el...Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.展开更多
Developing alloys with exceptional strength-ductility combinations across a broad temperature range is crucial for advanced structural applications.The emerging face-centered cubic medium-entropy alloys(MEAs)demonstra...Developing alloys with exceptional strength-ductility combinations across a broad temperature range is crucial for advanced structural applications.The emerging face-centered cubic medium-entropy alloys(MEAs)demonstrate outstanding mechanical properties at both ambient and cryogenic temperatures.They are anticipated to extend their applicability to elevated temperatures,owing to their inherent advantages in leveraging multiple strengthening and deformation mechanisms.Here,a dual heterostructure,comprising of heterogeneous grain structure with heterogeneous distribution of the micro-scale Nb-rich Laves phases,is introduced in a CrCoNi-based MEA through thermo-mechanical processing.Additionally,a high-density nano-coherentγ’phase is introduced within the grains through isothermal aging treatments.The superior thermal stability of the heterogeneously distributed precipitates enables the dual heterostructure to persist at temperatures up to 1073 K,allowing the MEA to maintain excellent mechanical properties across a wide temperature range.The yield strength of the dual-heterogeneous-structured MEA reaches up to 1.2 GPa,1.1 GPa,0.8 GPa,and 0.6 GPa,coupled with total elongation values of 28.6%,28.4%,12.6%,and 6.1%at 93 K,298 K,873 K,and 1073 K,respectively.The high yield strength primar-ily stems from precipitation strengthening and hetero-deformation-induced strengthening.The high flow stress and low stacking fault energy of the dual-heterogeneous-structured MEA promote the formation of high-density stacking faults and nanotwins during deformation from 93 K to 1073 K,and their density increase with decreasing deformation temperature.This greatly contributes to the enhanced strainhardening capability and ductility across a wide temperature range.This study offers a practical solution for designing dual-heterogeneous-structured MEAs with both high yield strength and large ductility across a wide temperature range.展开更多
In this work,a heterogeneous structure(HS)with an alternating distribution of coarse and fineαlamella is fabricated in bimodal Ti6242 alloy via insufficient diffusion of alloying elements induced by fast heat-ing tre...In this work,a heterogeneous structure(HS)with an alternating distribution of coarse and fineαlamella is fabricated in bimodal Ti6242 alloy via insufficient diffusion of alloying elements induced by fast heat-ing treatment.Instead of a distinct interface between the primaryα_(p)hase(α_(p))andβ_(t)ransformation microstructure(β_(t))in the equiaxed microstructure(EM),allα_(p)/β_(t)interfaces are eliminated in the HS,and the largeα_(p)phases are replaced by coarseαlamella.Compared to the EM alloy,the heterostruc-tured alloy exhibits a superior strength-ductility combination.The enhanced strength is predominantly attributed to the increased interfaces ofα/βplates and hetero-deformation induced(HDI)strengthening caused by back stress.Meanwhile,good ductility is ascribed to its uniform distribution of coarse and fineαlamella,which effectively inhibits strain localization and generates an extra HDI hardening.This can be evidenced by the accumulated geometrically necessary dislocations(GNDs)induced by strain partitioning of the heterostructure.Significantly,the HDI causes extra<c+a>dislocations piling up in the coarseαlamella,which generates an extra strain hardening to further improve the ductility.Such hetero-interface coordinated deformation mechanism sheds light on a new perspective for tailoring bimodal titanium al-loys with excellent mechanical properties.展开更多
Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack ro...Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack robustness.This study proposes a subgrade compaction strategy that utilizes a heterogeneous dataset to estimate compaction quality across diverse scenarios while maintaining model accuracy.Field compaction tests are conducted in four distinct scenarios,considering various construction parameters.Compaction models are developed using several machine learning algorithms.The datasets are thoroughly assessed in terms of quality,diversity and similarity.The proposed model exhibits good performance in new scenarios by incorporating an additional 5%e8%of new data for retraining.The model's generalization capability is enhanced by conducting a limited number of field tests,which are labor-saving and time-efficient.The model's accuracy consistently improves across diverse scenarios and optimal algorithms.The proposed compaction strategy adopts a physics-and-data dual-driven approach,aimed at practical engineering applications and guiding the compaction procedure.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
For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies ...For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.展开更多
Protein arginine methyltransferase-6 participates in a range of biological functions,particularly RNA processing,transcription,chromatin remodeling,and endosomal trafficking.However,it remains unclear whether protein ...Protein arginine methyltransferase-6 participates in a range of biological functions,particularly RNA processing,transcription,chromatin remodeling,and endosomal trafficking.However,it remains unclear whether protein arginine methyl transferase-6 modifies neuropathic pain and,if so,what the mechanisms of this effect.In this study,protein arginine methyltransferase-6 expression levels and its effect on neuropathic pain were investigated in the spared nerve injury model,chronic constriction injury model and bone cancer pain model,using immunohistochemistry,western blotting,immunoprecipitation,and label-free proteomic analysis.The results showed that protein arginine methyltransferase-6 mostly co-localized withβ-tubulinⅢin the dorsal root ganglion,and that its expression decreased following spared nerve injury,chronic constriction injury and bone cancer pain.In addition,PRMT6 knockout(Prmt6~(-/-))mice exhibited pain hypersensitivity.Furthermore,the development of spared nerve injury-induced hypersensitivity to mechanical pain was attenuated by blocking the decrease in protein arginine methyltransferase-6 expression.Moreover,when protein arginine methyltransferase-6 expression was downregulated in the dorsal root ganglion in mice without spared nerve injury,increased levels of phosphorylated extracellular signal-regulated kinases were observed in the ipsilateral dorsal horn,and the response to mechanical stimuli was enhanced.Mechanistically,protein arginine methyltransferase-6 appeared to contribute to spared nerve injury-induced neuropathic pain by regulating the expression of heterogeneous nuclear ribonucleoprotein-F.Additionally,protein arginine methyltransfe rase-6-mediated modulation of hete rogeneous nuclear ribonucleoprotein-F expression required amino atids 319 to 388,but not classical H3R2 methylation.These findings indicated that protein arginine methyltransferase-6 is a potential therapeutic target fo r the treatment of peripheral neuro pathic pain.展开更多
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.
文摘With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
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 National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金supported by National Key Research and Development Program of China(No.2023YFB 4404200).
文摘Fault-tolerant systems are crucial for ensuring the reliability and availability of missioncritical applications in modern computing environments.The dynamic heterogeneous redundancy(DHR)architecture is a key component in constructing fault-tolerant systems,particularly in areas such as national security,power networks,and banking private networks.DHR is transforming the cyberspace security industry chain by accommodating a broader range of applications and increasingly capturing the market.However,the development of applications for DHR architecture encounters challenges due to the complexities of handling heterogeneity,managing dynamism,and maintaining usability.To address these issues,we introduce MimicStudio,a comprehensive development framework with a standardized workflow.To our knowledge,MimicStudio is the first effective solution for DHR software development.We present a detailed implementation of MimicStudio with a heterogeneous microcontroller unit project,encompassing three CPUs with different instruction set architectures.The paper evaluates MimicStudio’s support for essential features,including zero-copy synchronization,parallelized build,multi-core collaborative debugging,and dynamic adjustment of the software system’s structure.Our results show that MimicStudio provides a flexible and efficient solution for supporting the dynamic,heterogeneous,and redundant features of fault-tolerant systems.
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 Biological Breeding-Major Projects in National Science and Technology(No.2023ZD0404405)the Earmarked Fund for China Agriculture Research System(No.CARS-pig-35)+2 种基金the National Natural Science Foundation of China(No.3227284,32302708)the 2115 Talent Development Program of China Agricultural University,the Chinese Universities Scientific Fund(No.2023TC196)the Seed Industry Revitalization Action Project of Guangdong Province(No.2024-XPY-06-001)。
文摘Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.
基金financially supported by the National Natural Science Foundation of China(No.52377026 and No.52301192)Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)+4 种基金Postdoctoral Fellowship Program of CPSF under Grant Number(No.GZB20240327)Shandong Postdoctoral Science Foundation(No.SDCXZG-202400275)Qingdao Postdoctoral Application Research Project(No.QDBSH20240102023)China Postdoctoral Science Foundation(No.2024M751563)the Qingchuang Talents Induction Program of Shandong Higher Education Institution(Research and Innovation Team of Structural-Functional Polymer Composites).
文摘Currently,the demand for electromagnetic wave(EMW)absorbing materials with specific functions and capable of withstanding harsh environments is becoming increasingly urgent.Multi-component interface engineering is considered an effective means to achieve high-efficiency EMW absorption.However,interface modulation engineering has not been fully discussed and has great potential in the field of EMW absorption.In this study,multi-component tin compound fiber composites based on carbon fiber(CF)substrate were prepared by electrospinning,hydrothermal synthesis,and high-temperature thermal reduction.By utilizing the different properties of different substances,rich heterogeneous interfaces are constructed.This effectively promotes charge transfer and enhances interfacial polarization and conduction loss.The prepared SnS/SnS_(2)/SnO_(2)/CF composites with abundant heterogeneous interfaces have and exhibit excellent EMW absorption properties at a loading of 50 wt%in epoxy resin.The minimum reflection loss(RL)is−46.74 dB and the maximum effective absorption bandwidth is 5.28 GHz.Moreover,SnS/SnS_(2)/SnO_(2)/CF epoxy composite coatings exhibited long-term corrosion resistance on Q235 steel surfaces.Therefore,this study provides an effective strategy for the design of high-efficiency EMW absorbing materials in complex and harsh environments.
基金support for this work by Hebei Education Department(No.JZX2024004)Central Guidance on Local Science and Technology Development Fund of Hebei Province(No.236Z1404G)+3 种基金the National Natural Science Foundation of China(Nos.22301060 and 21272053)China Postdoctoral Science Foundation(No.2023M730914)the Natural Science Foundation of Hebei Province(Biopharmaceutical Joint Fund No.B2022206008)Project of Science and Technology Department of Hebei Province(No.22567622H)。
文摘Heterogeneous metal-catalyzed chemical conversions with a recyclable catalyst are very ideal and challenging for sustainable organic synthesis.A new bipyridyl-Mo(IV)-carbon nitride(CN-K/Mo-Bpy)was prepared by supporting molybdenum complex on C_(3)N_(4)-K and characterized by FT-IR,XRD,SEM,XPS and ICP-OES.Heterogeneous CN–Mo-Bpy catalyst can be applied to the direct amination of nitroarenes and arylboronic acid,thus constructing various valuable diarylamines in high to excellent yields with a wide substrate scope and good functional group tolerance.It is worth noting that this heterogeneous catalyst has high chemical stability and can be recycled for at least five times without reducing its activity.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.52121004)the Major Program Natural Science Foundation of Hunan Province of China(No.2021JC0001)+2 种基金the National Key R&D Programof China(No.2022YFC3900200)the National Natural Science Foundation of China(No.22276218)the Science and Technology Innovation Programof Hunan Province(No.2021RC3013).
文摘Heterogeneous crystallization is a common occurrence during the formation of solidwastes.It leads to the encapsulation of valuable/hazardous metals within the primary phase,presenting significant challenges for waste treatment andmetal recovery.Herein,we proposed a novel method involving the in-situ formation of a competitive substrate during the precipitation of jarosite waste,which is an essential process for removing iron in zinc hydrometallurgy.We observed that the in-situ-formed competitive substrate effectively inhibits the heterogeneous crystallization of jarosite on the surface of anglesite,a lead-rich phase present in the jarositewaste.As a result,the iron content on the anglesite surface decreases from34.8%to 1.65%.The competitive substrate was identified as schwertmannite,characterized by its loose structure and large surface area.Furthermore,we have elucidated a novel mechanism underlying this inhibition of heterogeneous crystallization,which involves the local supersaturation of jarosite caused by the release of ferric and sulfate ions from the competitive substrate.The local supersaturation promotes the preferential heterogeneous crystallization of jarosite on the competitive substrate.Interestingly,during the formation of jarosite,the competitive substrate gradually vanished through a dissolution-recrystallization process following the Ostwald rule,where a metastable phase slowly transitions to a stable phase.This effectively precluded the introduction of impurities and reduced waste volume.The goal of this study is to provide fresh insights into the mechanism of heterogeneous crystallization control,and to offer practical crystallization strategies conducive to metal separation and recovery from solid waste in industries.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金supported by the National Natural Science Foundation of China(Grant No.61925307).
文摘Dynamic structuralcolors can change in response todifferent environmental stimuli.This ability remains effectiveeven when the size of the speciesresponsible for the structural coloris reduced to a few micrometers,providing a promising sensingmechanism for solving microenvironmentalsensing problems inmicro-robotics and microfluidics.However, the lack of dynamicstructural colors that can encoderapidly, easily integrate, and accuratelyreflect changes in physical quantities hinders their use in microscale sensing applications. Herein, we present a 2.5-dimensionaldynamic structural color based on nanogratings of heterogeneous materials, which were obtained by interweaving a pH-responsive hydrogelwith an IP-L photoresist. Transverse gratings printed with pH-responsive hydrogels elongated the period of longitudinal grating in the swollenstate, resulting in pH-tuned structural colors at a 45° incidence. Moreover, the patterned encoding and array printing of dynamic structuralcolors were achieved using grayscale stripe images to accurately encode the periods and heights of the nanogrid structures. Overall, dynamicstructural color networks exhibit promising potential for applications in information encryption and in situ sensing for microfluidic chips.
基金supported by the National Key Research and Development Program of China(2022YFC3205300)the National Natural Science Foundation of China(22176124).
文摘Current research on heterogeneous advanced oxidation processes(HAOPs)predominantly emphasizes catalyst iteration and innovation.Significant efforts have been made to regulate the electron structure and optimize the electron distribution,thereby increasing the catalytic activity.However,this focus often overshadows an equally essential aspect of HAOPs:the adsorption effect.Adsorption is a critical initiator for triggering the interaction of oxidants and contaminants with heterogeneous catalysts.The efficacy of these interactions is influenced by a variety of physicochemical properties,including surface chemistry and pore sizes,which determine the affinities between contaminants and material surfaces.This dispar ity in affinity is pivotal because it underpins the selective removal of contaminants,especially in complex waste streams containing diverse contaminants and competing matrices.Consequently,understanding and mastering these interfacial interactions is fundamentally indispensable not only for improving pro cess efficiency but also for enhancing the selectivity of contaminant removal.Herein,we highlight the importance of adsorption-driven interfacial interactions for fundamentally elucidating the catalytic mechanisms of HAOPs.Such interactions dictate the overall performance of the treatment processes by balancing the adsorption,reaction,and desorption rates on the catalyst surfaces.Elucidating the adsorption effect not only shifts the paradigm in understanding HAOPs but also improves their practical ity in water treatment and wastewater decontamination.Overall,we propose that revisiting adsorption driven interfacial interactions holds great promise for optimizing catalytic processes to develop effective HAOP strategies.
基金supported by the Tianjin Science and Technology Plan Project(No.22JCQNJC01280)the Central Funds Guiding the Local Science and Technology Development of Hebei Province(Nos.226Z1001G and 226Z1012G)+1 种基金the National Natural Science Foundation of China(No.52002109,52071124)the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001).
文摘Developing alloys with exceptional strength-ductility combinations across a broad temperature range is crucial for advanced structural applications.The emerging face-centered cubic medium-entropy alloys(MEAs)demonstrate outstanding mechanical properties at both ambient and cryogenic temperatures.They are anticipated to extend their applicability to elevated temperatures,owing to their inherent advantages in leveraging multiple strengthening and deformation mechanisms.Here,a dual heterostructure,comprising of heterogeneous grain structure with heterogeneous distribution of the micro-scale Nb-rich Laves phases,is introduced in a CrCoNi-based MEA through thermo-mechanical processing.Additionally,a high-density nano-coherentγ’phase is introduced within the grains through isothermal aging treatments.The superior thermal stability of the heterogeneously distributed precipitates enables the dual heterostructure to persist at temperatures up to 1073 K,allowing the MEA to maintain excellent mechanical properties across a wide temperature range.The yield strength of the dual-heterogeneous-structured MEA reaches up to 1.2 GPa,1.1 GPa,0.8 GPa,and 0.6 GPa,coupled with total elongation values of 28.6%,28.4%,12.6%,and 6.1%at 93 K,298 K,873 K,and 1073 K,respectively.The high yield strength primar-ily stems from precipitation strengthening and hetero-deformation-induced strengthening.The high flow stress and low stacking fault energy of the dual-heterogeneous-structured MEA promote the formation of high-density stacking faults and nanotwins during deformation from 93 K to 1073 K,and their density increase with decreasing deformation temperature.This greatly contributes to the enhanced strainhardening capability and ductility across a wide temperature range.This study offers a practical solution for designing dual-heterogeneous-structured MEAs with both high yield strength and large ductility across a wide temperature range.
基金financially supported by the National Natural Science Foundation of China(Nos.52161019 and 52271054)the Science and Technology Project of Guizhou Province,China(No.[2023]047)+1 种基金the GuiZhou DIIT Innovation Project(No.[2023]153)the One Hundred Person Project of Guizhou Province,China(No.[2020]6006).
文摘In this work,a heterogeneous structure(HS)with an alternating distribution of coarse and fineαlamella is fabricated in bimodal Ti6242 alloy via insufficient diffusion of alloying elements induced by fast heat-ing treatment.Instead of a distinct interface between the primaryα_(p)hase(α_(p))andβ_(t)ransformation microstructure(β_(t))in the equiaxed microstructure(EM),allα_(p)/β_(t)interfaces are eliminated in the HS,and the largeα_(p)phases are replaced by coarseαlamella.Compared to the EM alloy,the heterostruc-tured alloy exhibits a superior strength-ductility combination.The enhanced strength is predominantly attributed to the increased interfaces ofα/βplates and hetero-deformation induced(HDI)strengthening caused by back stress.Meanwhile,good ductility is ascribed to its uniform distribution of coarse and fineαlamella,which effectively inhibits strain localization and generates an extra HDI hardening.This can be evidenced by the accumulated geometrically necessary dislocations(GNDs)induced by strain partitioning of the heterostructure.Significantly,the HDI causes extra<c+a>dislocations piling up in the coarseαlamella,which generates an extra strain hardening to further improve the ductility.Such hetero-interface coordinated deformation mechanism sheds light on a new perspective for tailoring bimodal titanium al-loys with excellent mechanical properties.
基金supported by the National Natural Science Foundation of China(Grant Nos.52038005 and 52278342)the Natural Science Foundation of Tianjin Municipal(Grant No.23JCJQJC00160).
文摘Real-time assessment of subgrade compaction quality poses a significant challenge in the implementation of intelligent compaction(IC).Current compaction evaluation models are confined to specific scenarios and lack robustness.This study proposes a subgrade compaction strategy that utilizes a heterogeneous dataset to estimate compaction quality across diverse scenarios while maintaining model accuracy.Field compaction tests are conducted in four distinct scenarios,considering various construction parameters.Compaction models are developed using several machine learning algorithms.The datasets are thoroughly assessed in terms of quality,diversity and similarity.The proposed model exhibits good performance in new scenarios by incorporating an additional 5%e8%of new data for retraining.The model's generalization capability is enhanced by conducting a limited number of field tests,which are labor-saving and time-efficient.The model's accuracy consistently improves across diverse scenarios and optimal algorithms.The proposed compaction strategy adopts a physics-and-data dual-driven approach,aimed at practical engineering applications and guiding the compaction procedure.
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The National Key R&D Program of China(2021ZD0201300)the National Natural Science Foundation of China(624B2058,U1913602 and 61936004)+1 种基金the Innovation Group Project of the National Natural Science Foundation of China(61821003)the 111 Project on Computational Intelligence and Intelligent Control(B18024).
文摘For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.
基金supported by the National Natural Science Foundation of China,Nos.82001178(to LW),81901129(to LH),82001175(to FX)Shanghai Sailing Program,No.20YF1439200(to LW)+1 种基金the Natural Science Foundation of Shanghai,China,No.23ZR1450800(to LH)and the Fundamental Research Funds for the Central Universities,No.YG2023LC15(to ZX)。
文摘Protein arginine methyltransferase-6 participates in a range of biological functions,particularly RNA processing,transcription,chromatin remodeling,and endosomal trafficking.However,it remains unclear whether protein arginine methyl transferase-6 modifies neuropathic pain and,if so,what the mechanisms of this effect.In this study,protein arginine methyltransferase-6 expression levels and its effect on neuropathic pain were investigated in the spared nerve injury model,chronic constriction injury model and bone cancer pain model,using immunohistochemistry,western blotting,immunoprecipitation,and label-free proteomic analysis.The results showed that protein arginine methyltransferase-6 mostly co-localized withβ-tubulinⅢin the dorsal root ganglion,and that its expression decreased following spared nerve injury,chronic constriction injury and bone cancer pain.In addition,PRMT6 knockout(Prmt6~(-/-))mice exhibited pain hypersensitivity.Furthermore,the development of spared nerve injury-induced hypersensitivity to mechanical pain was attenuated by blocking the decrease in protein arginine methyltransferase-6 expression.Moreover,when protein arginine methyltransferase-6 expression was downregulated in the dorsal root ganglion in mice without spared nerve injury,increased levels of phosphorylated extracellular signal-regulated kinases were observed in the ipsilateral dorsal horn,and the response to mechanical stimuli was enhanced.Mechanistically,protein arginine methyltransferase-6 appeared to contribute to spared nerve injury-induced neuropathic pain by regulating the expression of heterogeneous nuclear ribonucleoprotein-F.Additionally,protein arginine methyltransfe rase-6-mediated modulation of hete rogeneous nuclear ribonucleoprotein-F expression required amino atids 319 to 388,but not classical H3R2 methylation.These findings indicated that protein arginine methyltransferase-6 is a potential therapeutic target fo r the treatment of peripheral neuro pathic pain.
