This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to t...This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.展开更多
Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting d...Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy informa...In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy information leakage.This poses a great challenge to conventional privacy protection mechanisms(CPPM).The existing data partitioning methods ignore the number of data replications and information exchanges,resulting in complex distance calculations and inefficient indexing for high-dimensional data.Therefore,CPPM often fails to meet the stringent requirements of efficiency and reliability,especially in dynamic spatiotemporal environments.Addressing this concern,we proposed the Principal Component Enhanced Vantage-point tree(PEV-Tree),which is an enhanced data structure based on the idea of dimension reduction,and constructed a Distributed Spatio-Temporal Privacy Preservation Mechanism(DST-PPM)on it.In this work,principal component analysis and the vantage tree are used to establish the PEV-Tree.In addition,we designed three distributed anonymization algorithms for data streams.These algorithms are named CK-AA,CL-DA,and CT-CA,fulfill the anonymization rules of K-Anonymity,L-Diversity,and T-Closeness,respectively,which have different computational complexities and reliabilities.The higher the complexity,the lower the risk of privacy leakage.DST-PPM can reduce the dimension of high-dimensional information while preserving data characteristics and dividing the data space into vantage points based on distance.It effectively enhances the data processing workflow and increases algorithmefficiency.To verify the validity of the method in this paper,we conducted empirical tests of CK-AA,CL-DA,and CT-CA on conventional datasets and the PEV-Tree,respectively.Based on the big data background of the Internet of Vehicles,we conducted experiments using artificial simulated on-board network data.The results demonstrated that the operational efficiency of the CK-AA,CL-DA,and CT-CA is enhanced by 15.12%,24.55%,and 52.74%,respectively,when deployed on the PEV-Tree.Simultaneously,during homogeneity attacks,the probabilities of information leakage were reduced by 2.31%,1.76%,and 0.19%,respectively.Furthermore,these algorithms showcased superior utility(scalability)when executed across PEV-Trees of varying scales in comparison to their performance on conventional data structures.It indicates that DST-PPM offers marked advantages over CPPM in terms of efficiency,reliability,and scalability.展开更多
Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of wat...Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of water supply,inundation risks,and environment management under the perspective of climate change.To have a comprehensive understanding of the Mediterranean water-cycle system,a deterministic distributed hydrologic modeling approach has been developed and presented in this study based on an application in the Var catchment(2800 km^(2))located at the French Mediterranean region.A 1D and 2D coupled model of MIKE SHE and MIKE 11 has been set up under a series of hypotheses to represent the whole hydrologic and hydrodynamic processes including rainfall-runoff,snow-melting,channel flow,overland flow,and the water exchange between land surface and unsaturated/saturated zones.The developed model was first calibrated with 4 years daily records from 2008 to 2011,then to be validated and further run within hourly time interval to produce detailed representation of the catchment water-cycle from 2012 to 2014.The deterministic distributed modeling approach presented in this study is able to represent its complicated water-cycle and used for supporting the decision‐making process of the water resources management of the catchment.展开更多
This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into accoun...This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks.展开更多
Metal halides have attracted worldwide attention as exceptional optoelectronic materials.Over the past decade,research on metal halides has yielded remarkable progress,and their color-conversion applications have show...Metal halides have attracted worldwide attention as exceptional optoelectronic materials.Over the past decade,research on metal halides has yielded remarkable progress,and their color-conversion applications have shown considerable promise for commercialization.With the reporting of self-trapped exciton(STE)emission in perovskites,the application of metal halides as broadband emitting materials in the lighting field has gained increas-ing interest.Herein,we provide a comprehensive review of metal halide STE emitters,especially for lighting applications.We begin with highlighting the ideal spectral characteristics and corresponding performance metrics for lighting.This is followed by a systematic summary of the mechanisms,optimization strategies,and recent advances of STE emission in metal halides.Finally,we outline the major challenges and prospective trends for metal halide STE emitters.This review aims to offer valuable insights into metal halide STE emitters and their lighting applications for facilitating the future commercialization.展开更多
Ulva lactuca whole components served as a carbon source for high-density fermentation of Bifidobacterium,yielding a low-cost,highly biocompatible Ulva lactuca bifidobacterium ferment filtrate(ULBFF).Its peptide profil...Ulva lactuca whole components served as a carbon source for high-density fermentation of Bifidobacterium,yielding a low-cost,highly biocompatible Ulva lactuca bifidobacterium ferment filtrate(ULBFF).Its peptide profile and molecular weight distribution were characterized by LC/MS.Cosmetic potential was systematically evaluated through stability testing,HET-CAM test,ABTS+∙/DPPH∙scavenging,NO inhibition,melanin suppression,and type Ⅰ collagen promotion.The results indicated a rich distribution of peptides,predominantly low-molecular-weight oligopeptides,with 92.92%of peptides containing≤20 amino acids and 84.89%of components having a molecular weight<500 Da.Formulations exhibited excellent stability and low irritation.Furthermore,they showed significantly enhanced efficacy in key areas,including antioxidant and antiinflammatory activity,melanin suppression,and collagen promotion.The ULBFF improved multifunctional performance while maintaining formulation stability and safety,demonstrating high scalability and potential for marine bioresource utilization in functional cosmetics.展开更多
The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies...The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.展开更多
Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant pe...Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.展开更多
Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass patho...Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass pathological sections into complete digital images through high-resolution scanning, it provides a new method for pathological diagnosis. Based on this, this paper studies the application of WSI technology in clinical pathological diagnosis, elaborates on its application value, analyzes the current application status, and proposes corresponding application countermeasures, aiming to provide reference for the standardized and popularized development of this technology in clinical pathological diagnosis.展开更多
Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation bi...Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation biases and inefficient multidimensional data integration.Artificial intelligence(AI),particularly deep learning and machine learning technologies,has emerged as a transformative tool in addressing these issues.Clinically,AI has been widely applied in imaging screening to improve detection rates and reduce reading time,digital pathology for precise tumor typing and gene mutation prediction,treatment decisionsupport systems to enhance guideline compliance,and drug research and development to accelerate target identification and virtual screening.Despite these achievements,AI implementation faces challenges,such as data standardization issues,limited model generalization,low clinical accessibility,and unclear ethical-legal responsibilities,which require targeted solutions that include national data standards,multi-center training,hierarchical physician training,and explainable AI.Future directions involve multimodal data integration,human-AI collaborative multidisciplinary team models,and extension to full-cycle health management from prevention-to-rehabilitation.This review provides a systematic overview of the role of AI in breast cancer care,offering insights for clinical practice and scientific research innovation,and supporting the transition toward personalized and intelligent medicine in oncology.展开更多
Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications i...Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications in aquatic and terrestrial ecosystems and assessing China’s standardization progress.It delineates four developmental phases from single-species detection to high-throughput sequencing,and highlights China’s contribution to the development of technical standards.While significant progress has been made,challenges persist in quantitative accuracy,methodological consistency,and large-scale implementation.Future efforts should prioritize enhanced standardization,improved quantification techniques,broader applications,and international collaboration to drive innovation in eDNA technology.展开更多
Lignin,the most abundant natural aromatic polymer globally,has garnered considerable interest due to its rich and diverse active functional groups and its antioxidant,antimicrobial,and adhesive properties.Recent resea...Lignin,the most abundant natural aromatic polymer globally,has garnered considerable interest due to its rich and diverse active functional groups and its antioxidant,antimicrobial,and adhesive properties.Recent research has significantly improved the performance of lignin-based hydrogels,suggesting their substantial potential in fields such as biomedicine,environmental science,and agriculture.This paper reviews the process of lignin extraction,systematically introduces synthesis strategies for preparing lignin-based hydrogels,and discusses the current state of research on these hydrogels in biomedical and environmental protection fields.It concludes by identifying the existing challenges in lignin hydrogel research and envisioning future prospects and development trends.展开更多
Biomass is a resourcewhose organic carbon is formed from atmospheric carbon dioxide.It has numerous characteristics such as low carbon emissions,renewability,and environmental friendliness.The efficient utilization of...Biomass is a resourcewhose organic carbon is formed from atmospheric carbon dioxide.It has numerous characteristics such as low carbon emissions,renewability,and environmental friendliness.The efficient utilization of biomass plays a significant role in promoting the development of clean energy,alleviating environmental pressures,and achieving carbon neutrality goals.Among the numerous processing technologies of biomass,hydrothermal carbonization(HTC)is a promising thermochemical process that can decompose and convert biomass into hydrochar under relatively mild conditions of approximately 180℃–300℃,thereby enabling its efficient resource utilization.In addition,HTC can directly process feedstocks with high moisture content without the need for high-temperature drying,resulting in lower energy consumption.Based on a systematic analysis of the critical articles mainly published in 2011-2025 related to biomass,HTC,and hydrochar applications,in this review,the category of biomass was first classified and the chemical compositions were summarized.Then,the main chemical reaction pathways involved in biomass decomposition and transformation during the HTC process were introduced.Meanwhile,the roles of key process parameters,including reaction temperature,residence time,pH,feedstock type,pressure,mass ratio of biomass to water,and the use of catalysts on HTC,were carefully discussed.Finally,the applications of hydrochar in energy utilization,environmental remediation,soil improvement,adsorbent,microbial fermentation,and phosphorus recovery fields were highlighted.The future directions of the HTC process were also provided,which would respond to climate change by promoting the development of the sustainable carbon materials field.展开更多
The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked m...The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked molecular markers(SyGI[Shy Girl],FrBy[Friendly Boy],and SmY1)in sex identification was evaluated in various Actinidia species.The selected marker products were subsequently cloned and sequenced in six wild Actinidia species.Ninety-six wild A.chinensis chinensis accessions and 74 A.chinensis deliciosa accessions,most of which were wild,with only one cultivated,were used for comprehensive primer validation.Thirty-three juvenile A.chinensis chinensis hybrid seedlings were used for practical application tests.The results showed that the marker SyGI accurately identified the sex of 20 samples from six Actinidia species and 96 A.chinensis chinensis accessions with 100%reliability.For Actinidia chinensis deliciosa,the identification accuracy reached 98.65%.Sequence analysis revealed that SyGI shared the highest similarity with the male-specific genomic region.Furthermore,SyGI achieved 100%accuracy in identifying the sex of 33 juvenile A.chinensis chinensis individuals.The findings confirm that the SyGI marker possesses high accuracy,strong specificity,and broad applicability,making it a valuable tool for kiwifruit breeding programs.The cloned sequences from wild Actinidia species also provide important references for future research on the mechanisms of sexual evolution and determination.展开更多
The escalating global crisis of antibiotic resistance necessitates urgent development of novel antimicrobial agents.In this context,antimicrobial peptides(AMPs)derived from fish emerge as a highly promising strategic ...The escalating global crisis of antibiotic resistance necessitates urgent development of novel antimicrobial agents.In this context,antimicrobial peptides(AMPs)derived from fish emerge as a highly promising strategic resource,owing to their unique structural diversity and the exceptional adaptability and tolerance conferred by evolutionary pressures in aquatic environments.This review systematically synthesizes key advances in fish-derived AMP research.It details their diverse sourcing avenues,encompassing tissues from live fish(e.g.,skin,mucus,gills,intestines)and processing byproducts(e.g.,scales,skins,viscera).The discussion covers efficient isolation,purification,and synthesis strategies,and critically examines their defining feature:unique multi-target synergistic antimicrobial mechanisms(including microbial membrane disruption,intracellular targeting,and immunomodulation),which contribute to a reduced propensity for resistance development.To address inherent limitations of natural AMPs(such as susceptibility to proteolysis and potential toxicity),the review highlights innovative optimization approaches,including computational-aided rational design,amino acid modification,cyclization,and hybrid peptide construction.Furthermore,the review elaborates on their significant application potential across crucial domains:food preservation(inhibiting spoilage organisms,extending shelf-life),sustainable aquaculture(as antibiotic alternatives,enhancing disease resistance,improving water quality),and the development of novel anti-infective therapeutics(particularly against drug-resistant infections).Therefore,this work aims to provide a comprehensive theoretical foundation and innovative strategic insights to foster in-depth research and the sustainable exploitation of this vital strategic biological resource.展开更多
Graphitic carbon nitride(g-CN)stands out as the most promising candidate for solar energy conversion owing to its easy preparation,metal-free nature,flexible molecular structure,moderate bandgap,and excellent thermal/...Graphitic carbon nitride(g-CN)stands out as the most promising candidate for solar energy conversion owing to its easy preparation,metal-free nature,flexible molecular structure,moderate bandgap,and excellent thermal/chemical stability.To enhance the performance of intrinsic g-CN,a supramolecular self-assembly strategy has been proposed to regulate the molecular structure of supramolecular precursors through non-covalent interactions across molecular building blocks,thereby optimizing the electronic structure of g-CN.This review provides a comprehensive overview of the recent progress in supramolecular self-assembly-derived graphitic carbon nitride(SM-CN)from both experimental and theoretical computational research in synthesis strategies,including synthesis methods and influencing factors,providing a theoretical foundation for the design of supramolecular assembly.It also discusses modification strategies,such as internal modification of the conjugated plane,interlayer optimization,and construction of heterointerfaces to improve the electronic structure of SM-CN owing to its unique layered structure.This review further summarizes the applications of SM-CN in environment and energy,including wastewater treatment,sterilization and disinfection/air purification,water splitting,H_(2)O_(2)production,organic synthesis/biomass conversion,CO_(2)reduction,photocatalytic coupling technology.Finally,perspectives and outlooks for the future development of SM-CN aim to inspire further innovation in the design and construction of high-performance SM-CN for broader applications.展开更多
Natural evolution has endowed biological surfaces with unique microstructural features,enabling them to achieve complex functions such as grasping,climbing,and self-cleaning through precise regulation of adhesion.Insp...Natural evolution has endowed biological surfaces with unique microstructural features,enabling them to achieve complex functions such as grasping,climbing,and self-cleaning through precise regulation of adhesion.Inspired by this,bioinspired adhesive microstructures have shown tremendous application potential in the rapidly advancing and highly innovative biomedical field.This paper systematically reviews the adhesion systems of biological surfaces like those of geckos and tree frogs,and conducts an in-depth analysis of the adhesion mechanisms underlying various microstructures and their corresponding bioinspired adhesives from the critical perspective of structural characteristics.It reviews different types of interfacial adhesion models,with special emphasis on the suitability of the Cantor-Borodich profile model for accurately describing multiscale hierarchical adhesive structures in diverse and complex biological systems.The paper focuses on elaborating the significant contributions of bioinspired adhesives in biomedical engineering,particularly their practical and impactful applications in wearable medical devices such as stable adhesion in dynamic physiological environments,surgical instruments such as low-damage soft tissue gripping,and drug delivery systems such as enhanced transdermal delivery efficiency.Additionally,it outlines current development prospects and key challenges such as long-term biocompatibility,environmental adaptability,and structure-function synergistic optimization,providing new ideas and valuable references for further research and application of bioinspired adhesive microstructures in biomedical engineering.展开更多
Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance ...Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.展开更多
基金supported by the National Key Research and Development Program of China(2025YFE0213100)the National Natural Science Foundation of China(62422315,62573348)+1 种基金the Natural Science Basic Research Program of Shaanxi(2025JC-YBMS-667)the“Shuang Yi Liu”Construction Foundation(25GH02010366)。
文摘This paper investigates the distributed continuoustime aggregative optimization problem for second-order multiagent systems,where the local cost function is not only related to its own decision variables,but also to the aggregation of the decision variables of all the agents.By using the gradient descent method,the distributed average tracking(DAT)technique and the time-base generator(TBG)technique,a distributed continuous-time aggregative optimization algorithm is proposed.Subsequently,the optimality of the system's equilibrium point is analyzed,and the convergence of the closed-loop system is proved using the Lyapunov stability theory.Finally,the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.
基金supported by the Hubei Provincial Science and Technology Project,China(2025CSA039)the National Natural Science Foundation of China(32001467)。
文摘Coordinating light and nitrogen(N)distribution within a canopy is essential for improving rice yield and resource use efficiency.However,limited research has examined light and N distribution in response to planting density and N rate,and their relationships with grain yield,radiation use efficiency(RUE),and N use efficiency for grain production(NUEg)in rice.A two-year field experiment was conducted with two hybrid varieties under three N levels,0 kg ha^(-1)(N1),90 kg ha^(-1)(N2)and 180 kg ha^(-1)(N3),and two planting densities,22.2 hills m-2(D1)and 33.3 hills m^(-2)(D2).Results showed 3.4%higher yield and 4.4%higher NUEg under N2D2 compared with N3D1.The extinction coefficient for N(K_(N))and light(K_(L))and their ratio(K_(N)/K_(L))at heading stage were significantly influenced by N rate,planting density,and their interaction.K_(N)decreased with the increase of N input or planting density.Compared to N1,K_(N)decreased by 43.5 and 58.8%under N2 and N3,respectively,while K_(N)under D2 decreased by 16.0%compared to D1.Higher K_(L)and K_(N)/K_(L)values occurred under low N rates,with opposite trends under high N rates.Increased planting density led to decreased K_(L)and K_(N)/K_(L)values.N2D2 demonstrated higher K_(L)and K_(N),and thus comparable K_(N)/K_(L),compared to N3D1.Correlation analysis revealed K_(L)negatively correlated with RUE,while K_(N)and K_(N)/K_(L)positively correlated with NUEg.These findings indicate that increasing planting density under reduced N input could maintain rice yield while enhancing resource use efficiency through regulation of canopy light and N distribution.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
基金supported by the Natural Science Foundation of Sichuan Province(No.2024NSFSC1450)the Fundamental Research Funds for the Central Universities(No.SCU2024D012)the Science and Engineering Connotation Development Project of Sichuan University(No.2020SCUNG129).
文摘In the era of big data,the growing number of real-time data streams often contains a lot of sensitive privacy information.Releasing or sharing this data directly without processing will lead to serious privacy information leakage.This poses a great challenge to conventional privacy protection mechanisms(CPPM).The existing data partitioning methods ignore the number of data replications and information exchanges,resulting in complex distance calculations and inefficient indexing for high-dimensional data.Therefore,CPPM often fails to meet the stringent requirements of efficiency and reliability,especially in dynamic spatiotemporal environments.Addressing this concern,we proposed the Principal Component Enhanced Vantage-point tree(PEV-Tree),which is an enhanced data structure based on the idea of dimension reduction,and constructed a Distributed Spatio-Temporal Privacy Preservation Mechanism(DST-PPM)on it.In this work,principal component analysis and the vantage tree are used to establish the PEV-Tree.In addition,we designed three distributed anonymization algorithms for data streams.These algorithms are named CK-AA,CL-DA,and CT-CA,fulfill the anonymization rules of K-Anonymity,L-Diversity,and T-Closeness,respectively,which have different computational complexities and reliabilities.The higher the complexity,the lower the risk of privacy leakage.DST-PPM can reduce the dimension of high-dimensional information while preserving data characteristics and dividing the data space into vantage points based on distance.It effectively enhances the data processing workflow and increases algorithmefficiency.To verify the validity of the method in this paper,we conducted empirical tests of CK-AA,CL-DA,and CT-CA on conventional datasets and the PEV-Tree,respectively.Based on the big data background of the Internet of Vehicles,we conducted experiments using artificial simulated on-board network data.The results demonstrated that the operational efficiency of the CK-AA,CL-DA,and CT-CA is enhanced by 15.12%,24.55%,and 52.74%,respectively,when deployed on the PEV-Tree.Simultaneously,during homogeneity attacks,the probabilities of information leakage were reduced by 2.31%,1.76%,and 0.19%,respectively.Furthermore,these algorithms showcased superior utility(scalability)when executed across PEV-Trees of varying scales in comparison to their performance on conventional data structures.It indicates that DST-PPM offers marked advantages over CPPM in terms of efficiency,reliability,and scalability.
基金supported by the National Key R&D Program of China(No.2023YFC3006702)the Natural Science Foundation of Beijing Municipality(IS23117).
文摘Characterized by special morphologic,geographic,hydrologic,and societal behaviors,the water resources management of the Mediterranean catchment often shows a higher level of complexity including security issues of water supply,inundation risks,and environment management under the perspective of climate change.To have a comprehensive understanding of the Mediterranean water-cycle system,a deterministic distributed hydrologic modeling approach has been developed and presented in this study based on an application in the Var catchment(2800 km^(2))located at the French Mediterranean region.A 1D and 2D coupled model of MIKE SHE and MIKE 11 has been set up under a series of hypotheses to represent the whole hydrologic and hydrodynamic processes including rainfall-runoff,snow-melting,channel flow,overland flow,and the water exchange between land surface and unsaturated/saturated zones.The developed model was first calibrated with 4 years daily records from 2008 to 2011,then to be validated and further run within hourly time interval to produce detailed representation of the catchment water-cycle from 2012 to 2014.The deterministic distributed modeling approach presented in this study is able to represent its complicated water-cycle and used for supporting the decision‐making process of the water resources management of the catchment.
基金supported in part by the National Key Research and Development Program of China(2022ZD0120001)the National Natural Science Foundation of China(62233004,62273090,62073076)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)
文摘This paper investigates a class of constrained distributed zeroth-order optimization(ZOO) problems over timevarying unbalanced graphs while ensuring privacy preservation among individual agents. Not taking into account recent progress and addressing these concerns separately, there remains a lack of solutions offering theoretical guarantees for both privacy protection and constrained ZOO over time-varying unbalanced graphs.We hereby propose a novel algorithm, termed the differential privacy(DP) distributed push-sum based zeroth-order constrained optimization algorithm(DP-ZOCOA). Operating over time-varying unbalanced graphs, DP-ZOCOA obviates the need for supplemental suboptimization problem computations, thereby reducing overhead in comparison to distributed primary-dual methods. DP-ZOCOA is specifically tailored to tackle constrained ZOO problems over time-varying unbalanced graphs,offering a guarantee of convergence to the optimal solution while robustly preserving privacy. Moreover, we provide rigorous proofs of convergence and privacy for DP-ZOCOA, underscoring its efficacy in attaining optimal convergence without constraints. To enhance its applicability, we incorporate DP-ZOCOA into the federated learning framework and formulate a decentralized zeroth-order constrained federated learning algorithm(ZOCOA-FL) to address challenges stemming from the timevarying imbalance of communication topology. Finally, the performance and effectiveness of the proposed algorithms are thoroughly evaluated through simulations on distributed least squares(DLS) and decentralized federated learning(DFL) tasks.
文摘Metal halides have attracted worldwide attention as exceptional optoelectronic materials.Over the past decade,research on metal halides has yielded remarkable progress,and their color-conversion applications have shown considerable promise for commercialization.With the reporting of self-trapped exciton(STE)emission in perovskites,the application of metal halides as broadband emitting materials in the lighting field has gained increas-ing interest.Herein,we provide a comprehensive review of metal halide STE emitters,especially for lighting applications.We begin with highlighting the ideal spectral characteristics and corresponding performance metrics for lighting.This is followed by a systematic summary of the mechanisms,optimization strategies,and recent advances of STE emission in metal halides.Finally,we outline the major challenges and prospective trends for metal halide STE emitters.This review aims to offer valuable insights into metal halide STE emitters and their lighting applications for facilitating the future commercialization.
文摘Ulva lactuca whole components served as a carbon source for high-density fermentation of Bifidobacterium,yielding a low-cost,highly biocompatible Ulva lactuca bifidobacterium ferment filtrate(ULBFF).Its peptide profile and molecular weight distribution were characterized by LC/MS.Cosmetic potential was systematically evaluated through stability testing,HET-CAM test,ABTS+∙/DPPH∙scavenging,NO inhibition,melanin suppression,and type Ⅰ collagen promotion.The results indicated a rich distribution of peptides,predominantly low-molecular-weight oligopeptides,with 92.92%of peptides containing≤20 amino acids and 84.89%of components having a molecular weight<500 Da.Formulations exhibited excellent stability and low irritation.Furthermore,they showed significantly enhanced efficacy in key areas,including antioxidant and antiinflammatory activity,melanin suppression,and collagen promotion.The ULBFF improved multifunctional performance while maintaining formulation stability and safety,demonstrating high scalability and potential for marine bioresource utilization in functional cosmetics.
基金National Key Research and Development Program of China under Grant No.2022YFC3003601National Natural Science Foundation of China under Grant No.52478570+1 种基金Heilongjiang Provincial Natural Science Foundation Outstanding Youth Program under Grant No.J020245002the Key Research and Development Program of Xinjiang Production and Construction Corps under Grant No.2024AB077。
文摘The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.
基金supported by the National Natural Science Foundation of China under Grant No.12271034the Open Fund Project of Key Laboratory of Market Regulation under Grant No.2023SYSKF02003。
文摘Distributed learning is a well-established method for estimation tasks over extensively distributed datasets.However,non-randomly stored data can introduce bias into local parameter estimates,leading to significant performance degradation in classical distributed algorithms.In this paper,the authors propose a novel Distributed Quasi-Newton Pilot(DQNP)method for distributed learning with non-randomly distributed data.The proposed approach accommodates both randomly and non-randomly distributed data settings and imposes no constraints on the uniformity of local sample sizes.Additionally,it avoids the need to transfer the Hessian matrix or compute its inversion,thereby greatly reducing computational and communication complexity.The authors theoretically demonstrate that the resulting estimator achieves statistical efficiency under mild conditions.Extensive numerical experiments on synthetic and real-world data validate the theoretical findings and illustrate the effectiveness of the proposed method.
文摘Whole Slide Imaging (WSI) technology, as a revolutionary digital technology in the field of pathology, is gradually changing the traditional clinical pathological diagnosis model. By converting traditional glass pathological sections into complete digital images through high-resolution scanning, it provides a new method for pathological diagnosis. Based on this, this paper studies the application of WSI technology in clinical pathological diagnosis, elaborates on its application value, analyzes the current application status, and proposes corresponding application countermeasures, aiming to provide reference for the standardized and popularized development of this technology in clinical pathological diagnosis.
基金supported by the National Natural Science Foundation of China(Grant No.82404074)the Science and Technology Major Project(Grant No.2024ZD0519805).
文摘Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation biases and inefficient multidimensional data integration.Artificial intelligence(AI),particularly deep learning and machine learning technologies,has emerged as a transformative tool in addressing these issues.Clinically,AI has been widely applied in imaging screening to improve detection rates and reduce reading time,digital pathology for precise tumor typing and gene mutation prediction,treatment decisionsupport systems to enhance guideline compliance,and drug research and development to accelerate target identification and virtual screening.Despite these achievements,AI implementation faces challenges,such as data standardization issues,limited model generalization,low clinical accessibility,and unclear ethical-legal responsibilities,which require targeted solutions that include national data standards,multi-center training,hierarchical physician training,and explainable AI.Future directions involve multimodal data integration,human-AI collaborative multidisciplinary team models,and extension to full-cycle health management from prevention-to-rehabilitation.This review provides a systematic overview of the role of AI in breast cancer care,offering insights for clinical practice and scientific research innovation,and supporting the transition toward personalized and intelligent medicine in oncology.
基金supported by the National Natural Science Foundation of China(Grant No.32160172)the Key Science-Technology Project of Inner Mongolia(2023KYPT0010)+1 种基金the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2025QN03006)the 2023 Inner Mongolia Public Institution High-Level Talent Introduction Scientific Research Support Project.
文摘Environmental DNA(eDNA)technology has revolutionized biodiversity monitoring with its non-invasive,sensitive,and cost-efficient approach.This paper systematically reviews eDNA advancements,examining its applications in aquatic and terrestrial ecosystems and assessing China’s standardization progress.It delineates four developmental phases from single-species detection to high-throughput sequencing,and highlights China’s contribution to the development of technical standards.While significant progress has been made,challenges persist in quantitative accuracy,methodological consistency,and large-scale implementation.Future efforts should prioritize enhanced standardization,improved quantification techniques,broader applications,and international collaboration to drive innovation in eDNA technology.
基金supported by the National Natural Science Foundation of China(21706052,22278114)Natural Science Foundation of Henan Province(242300421575).
文摘Lignin,the most abundant natural aromatic polymer globally,has garnered considerable interest due to its rich and diverse active functional groups and its antioxidant,antimicrobial,and adhesive properties.Recent research has significantly improved the performance of lignin-based hydrogels,suggesting their substantial potential in fields such as biomedicine,environmental science,and agriculture.This paper reviews the process of lignin extraction,systematically introduces synthesis strategies for preparing lignin-based hydrogels,and discusses the current state of research on these hydrogels in biomedical and environmental protection fields.It concludes by identifying the existing challenges in lignin hydrogel research and envisioning future prospects and development trends.
基金supported by National Natural Science Foundation of China(22578155,22478147)the Natural Science Foundation of Huaian City(HAB2024051).
文摘Biomass is a resourcewhose organic carbon is formed from atmospheric carbon dioxide.It has numerous characteristics such as low carbon emissions,renewability,and environmental friendliness.The efficient utilization of biomass plays a significant role in promoting the development of clean energy,alleviating environmental pressures,and achieving carbon neutrality goals.Among the numerous processing technologies of biomass,hydrothermal carbonization(HTC)is a promising thermochemical process that can decompose and convert biomass into hydrochar under relatively mild conditions of approximately 180℃–300℃,thereby enabling its efficient resource utilization.In addition,HTC can directly process feedstocks with high moisture content without the need for high-temperature drying,resulting in lower energy consumption.Based on a systematic analysis of the critical articles mainly published in 2011-2025 related to biomass,HTC,and hydrochar applications,in this review,the category of biomass was first classified and the chemical compositions were summarized.Then,the main chemical reaction pathways involved in biomass decomposition and transformation during the HTC process were introduced.Meanwhile,the roles of key process parameters,including reaction temperature,residence time,pH,feedstock type,pressure,mass ratio of biomass to water,and the use of catalysts on HTC,were carefully discussed.Finally,the applications of hydrochar in energy utilization,environmental remediation,soil improvement,adsorbent,microbial fermentation,and phosphorus recovery fields were highlighted.The future directions of the HTC process were also provided,which would respond to climate change by promoting the development of the sustainable carbon materials field.
基金funded by Sichuan Science and Technology Program,grant numbers 2021YFYZ0010,2023YFH0006,2025YFHZ0295The Basic Research Program of Sichuan Provincial Research Institutes,grant numbers 2024JDKY0001 and 2023JDKY0001.
文摘The genus Actinidia is primarily functionally dioecious,and early sex identification plays a crucial role in improving breeding efficiency and reducing production costs.In this study,the accuracy of three sex-linked molecular markers(SyGI[Shy Girl],FrBy[Friendly Boy],and SmY1)in sex identification was evaluated in various Actinidia species.The selected marker products were subsequently cloned and sequenced in six wild Actinidia species.Ninety-six wild A.chinensis chinensis accessions and 74 A.chinensis deliciosa accessions,most of which were wild,with only one cultivated,were used for comprehensive primer validation.Thirty-three juvenile A.chinensis chinensis hybrid seedlings were used for practical application tests.The results showed that the marker SyGI accurately identified the sex of 20 samples from six Actinidia species and 96 A.chinensis chinensis accessions with 100%reliability.For Actinidia chinensis deliciosa,the identification accuracy reached 98.65%.Sequence analysis revealed that SyGI shared the highest similarity with the male-specific genomic region.Furthermore,SyGI achieved 100%accuracy in identifying the sex of 33 juvenile A.chinensis chinensis individuals.The findings confirm that the SyGI marker possesses high accuracy,strong specificity,and broad applicability,making it a valuable tool for kiwifruit breeding programs.The cloned sequences from wild Actinidia species also provide important references for future research on the mechanisms of sexual evolution and determination.
文摘The escalating global crisis of antibiotic resistance necessitates urgent development of novel antimicrobial agents.In this context,antimicrobial peptides(AMPs)derived from fish emerge as a highly promising strategic resource,owing to their unique structural diversity and the exceptional adaptability and tolerance conferred by evolutionary pressures in aquatic environments.This review systematically synthesizes key advances in fish-derived AMP research.It details their diverse sourcing avenues,encompassing tissues from live fish(e.g.,skin,mucus,gills,intestines)and processing byproducts(e.g.,scales,skins,viscera).The discussion covers efficient isolation,purification,and synthesis strategies,and critically examines their defining feature:unique multi-target synergistic antimicrobial mechanisms(including microbial membrane disruption,intracellular targeting,and immunomodulation),which contribute to a reduced propensity for resistance development.To address inherent limitations of natural AMPs(such as susceptibility to proteolysis and potential toxicity),the review highlights innovative optimization approaches,including computational-aided rational design,amino acid modification,cyclization,and hybrid peptide construction.Furthermore,the review elaborates on their significant application potential across crucial domains:food preservation(inhibiting spoilage organisms,extending shelf-life),sustainable aquaculture(as antibiotic alternatives,enhancing disease resistance,improving water quality),and the development of novel anti-infective therapeutics(particularly against drug-resistant infections).Therefore,this work aims to provide a comprehensive theoretical foundation and innovative strategic insights to foster in-depth research and the sustainable exploitation of this vital strategic biological resource.
基金supported by the National Natural Science Foundation of China(NSFC No.52271228)the Natural Science Foundation of Shaanxi Province(No.2023-JC-ZD-21)the Doctoral Dissertation Innovation Fund of Xi'an University of Technology(No.101-252072301)。
文摘Graphitic carbon nitride(g-CN)stands out as the most promising candidate for solar energy conversion owing to its easy preparation,metal-free nature,flexible molecular structure,moderate bandgap,and excellent thermal/chemical stability.To enhance the performance of intrinsic g-CN,a supramolecular self-assembly strategy has been proposed to regulate the molecular structure of supramolecular precursors through non-covalent interactions across molecular building blocks,thereby optimizing the electronic structure of g-CN.This review provides a comprehensive overview of the recent progress in supramolecular self-assembly-derived graphitic carbon nitride(SM-CN)from both experimental and theoretical computational research in synthesis strategies,including synthesis methods and influencing factors,providing a theoretical foundation for the design of supramolecular assembly.It also discusses modification strategies,such as internal modification of the conjugated plane,interlayer optimization,and construction of heterointerfaces to improve the electronic structure of SM-CN owing to its unique layered structure.This review further summarizes the applications of SM-CN in environment and energy,including wastewater treatment,sterilization and disinfection/air purification,water splitting,H_(2)O_(2)production,organic synthesis/biomass conversion,CO_(2)reduction,photocatalytic coupling technology.Finally,perspectives and outlooks for the future development of SM-CN aim to inspire further innovation in the design and construction of high-performance SM-CN for broader applications.
基金supported and funded by the National Natural Science Foundation of China(HWG2022001,12402135,52575201 and 12502114)the China Postdoctoral Science Foundation(2024M763860)support from Chongqing City Science and Technology Program(Grant No.CSTB2025NSCQ-GPX0760,CSTB2025NSCQ-GPX0778 and CSTB2025NSCQ-GPX0784).
文摘Natural evolution has endowed biological surfaces with unique microstructural features,enabling them to achieve complex functions such as grasping,climbing,and self-cleaning through precise regulation of adhesion.Inspired by this,bioinspired adhesive microstructures have shown tremendous application potential in the rapidly advancing and highly innovative biomedical field.This paper systematically reviews the adhesion systems of biological surfaces like those of geckos and tree frogs,and conducts an in-depth analysis of the adhesion mechanisms underlying various microstructures and their corresponding bioinspired adhesives from the critical perspective of structural characteristics.It reviews different types of interfacial adhesion models,with special emphasis on the suitability of the Cantor-Borodich profile model for accurately describing multiscale hierarchical adhesive structures in diverse and complex biological systems.The paper focuses on elaborating the significant contributions of bioinspired adhesives in biomedical engineering,particularly their practical and impactful applications in wearable medical devices such as stable adhesion in dynamic physiological environments,surgical instruments such as low-damage soft tissue gripping,and drug delivery systems such as enhanced transdermal delivery efficiency.Additionally,it outlines current development prospects and key challenges such as long-term biocompatibility,environmental adaptability,and structure-function synergistic optimization,providing new ideas and valuable references for further research and application of bioinspired adhesive microstructures in biomedical engineering.
基金the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Programthe Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number“NBU-FFR-2025-2903-09”.
文摘Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.
