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
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
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
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
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
Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for...Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.展开更多
Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,an...Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.展开更多
Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By...Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.展开更多
Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR sc...Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.展开更多
With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the pres...With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the present study explores the implications of integrating image-generative AI into Landscape Architecture courses from three perspectives: stimulating students creative design potential, expanding approaches to form and concept generation, and enhancing the visualization of spatial scenes. Furthermore, it discusses application strategies from three dimensions: AI-assisted conceptual generation, human-machine collaboration for design refinement, and optimization of scheme presentation and evaluation. This paper aims to provide relevant educators with insights and references.展开更多
This article reviews the research advances in traditional Chinese medicine rhubarb and its compound formulations in the treatment of sepsis,with particular emphasis on elucidating their mechanisms of action and clinic...This article reviews the research advances in traditional Chinese medicine rhubarb and its compound formulations in the treatment of sepsis,with particular emphasis on elucidating their mechanisms of action and clinical application value.Research has demonstrated that rhubarb and its compound formulations exert therapeutic effects via multiple targets and mechanisms,including anti-inflammatory actions,protection of the intestinal barrier,modulation of immune balance,inhibition of oxidative stress,and regulation of associated signaling pathways.Clinically,rhubarb has shown distinct advantages in enhancing gastrointestinal function,mitigating systemic inflammatory responses,and reducing mortality rates among patients with sepsis.These findings provide a foundational reference for the integrated prevention and treatment of sepsis through the combined use of traditional Chinese and Western medicine.展开更多
Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and ...Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.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(U24A2079,22272003,22301013)the Program of Beijing Municipal Education Commission(KZ20231000506)+1 种基金the National Key Research and Development Program of China(2023YFB3810800)the China Postdoctoral Science Foundation(2025M770117,GZC20250088)。
文摘Carbon dots(CDs),as emerging zero-dimensional carbon-based nanomaterials,demonstrate immense potential across optical displays,bioimaging,chemical sensing,information anti-counterfeiting,and optoelectronic devices.This promise stems from their exceptional tunable photoluminescence,low toxicity,biocompatibility,and abundant raw material sources.Since their discovery,research has centered on resolving controversies regarding classification,formation mechanism,microstructure,and luminescence principles while achieving controllable optoelectronic properties.Applications have evolved from basic fluorescent labeling to advanced domains including multimodal theranostics,high-sensitivity(bio/chemical)sensing,stable optoelectronic devices,intelligent anticounterfeiting systems,and environmental/energy catalysis.Future challenges demand breakthroughs in structural homogeneity/scalable eco-fabrication,universal structure-opto/electronic-property models,stability/efficiency in complex environments,and multifunctional synergy(e.g.,photo-electro-catalysis).This comprehensive review systematically examines milestone advances in CDs research over the past decade—spanning synthesis methodologies,photo/electronic property modulation mechanisms,and innovative applications—while dissecting key challenges and envisioning future pathways as versatile intelligent nanoplatforms.
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.
基金support from the Contract Research(“Development of Breathable Fabrics with Nano-Electrospun Membrane”,CityU ref.:9231419“Research and application of antibacterial and healing-promoting smart nanofiber dressing for children’s burn wounds”,CityU ref:PJ9240111)+1 种基金the National Natural Science Foundation of China(“Study of Multi-Responsive Shape Memory Polyurethane Nanocomposites Inspired by Natural Fibers”,Grant No.51673162)Startup Grant of CityU(“Laboratory of Wearable Materials for Healthcare”,Grant No.9380116).
文摘Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.
文摘Liver transplantation(LT)remains the optimal life-saving intervention for patients with end-stage liver disease.Despite the recent advances in LT several barriers,including organ allocation,donor-recipient matching,and patient education,persist.With the growing progress of artificial intelligence,particularly large language models(LLMs)like ChatGPT,new applications have emerged in the field of LT.Current studies demonstrating usage of ChatGPT in LT include various areas of application,from clinical settings to research and education.ChatGPT usage can benefit both healthcare professionals,by decreasing the time spent on non-clinical work,but also LT recipients by providing accurate information.Future potential applications include the expanding usage of ChatGPT and other LLMs in the field of LT pathology and radiology as well as the automated creation of discharge summaries or other related paperwork.Additionally,the next models of ChatGPT might have the potential to provide more accurate patient education material with increased readability.Although ChatGPT usage presents promising applications,there are certain ethical and practical limitations.Key concerns include patient data privacy,information accuracy,misinformation possibility and lack of legal framework.Healthcare providers and policymakers should collaborate for the establishment of a controlled framework for the safe use of ChatGPT.The aim of this minireview is to summarize current literature on ChatGPT in LT,highlighting both opportunities and limitations,while also providing future possible applications.
文摘Objective:To systematically sort out the application forms and effects of digital health intervention technologies in oral health management,and provide references for the digital development of stomatology.Methods:By reviewing relevant domestic and foreign studies and clinical practices,this paper summarizes and analyzes the main application forms of digital health interventions,including digital health education,intelligent detection equipment,telemedicine platforms,oral health big data platforms,and school-hospital collaborative screening robots.Results:Studies have shown that digital health interventions can effectively improve the public’s oral health knowledge level,optimize personal health behaviors,enhance clinical diagnosis efficiency,reduce overall medical costs,and promote the innovation and upgrading of oral health management models.Conclusion:Digital health intervention represents an inevitable trend in the future development of stomatology.In the future,it is still necessary to improve data security and privacy protection,technology adaptability and popularity,as well as relevant policies and norms,to give full play to its potential value.
基金funded by the Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center 2025(GWJJMB202510024146)the Post-Subsidy Project for Standard Development of Guizhou Provincial Market Supervision and Administration Bureau 2025(DB52/T1726-2023)the Guizhou Provincial Health Commission Science and Technology Fund Project(gzwkj2024-076,gzwkj2026-146).
文摘Diabetic retinopathy(DR)is a leading cause of vision loss among working-age populations,with early screening significantly reducing the risk of blindness.However,resource-limited regions often face challenges in DR screening due to a shortage of ophthalmologists.This study reports the implementation and outcomes of the Chinese local standard DB52/T 1726-2023,Regulations for the application of diabetic retinopathy screening artificial intelligence,in Cambodian healthcare institutions.A pilot DR screening program with independent operational capability is established by providing a non-mydriatic fundus camera and deploying a localized diabetic retinopathy artificial intelligence(DR-AI)screening platform at the Cambodia-Kingdom Friendship Hospital in Phnom Penh,along with comprehensive training.From January to August 2025,a total of 565 patients with type 2 diabetes were screened,yielding a DR detection rate of 26.0%(147 cases).Research findings demonstrate that applying mature Chinese DR-AI screening standards and technological solutions through international collaboration in regions with a scarcity of ophthalmic professionals is both feasible and effective.This project serves as a reference for promoting DR-AI in resource-constrained countries and regions,highlighting its significant potential to leverage AI in addressing the global burden of chronic diseases and advancing the modernization of health systems.
基金Supported by Applied Brand Course of Mianyang Teacher's College(Investigation and Monitoring of Natural Resources).
文摘With the rapid development of image-generative AI (artificial intelligence) technology, its application in undergraduate Landscape Architecture education has demonstrated significant potential. Based on this, the present study explores the implications of integrating image-generative AI into Landscape Architecture courses from three perspectives: stimulating students creative design potential, expanding approaches to form and concept generation, and enhancing the visualization of spatial scenes. Furthermore, it discusses application strategies from three dimensions: AI-assisted conceptual generation, human-machine collaboration for design refinement, and optimization of scheme presentation and evaluation. This paper aims to provide relevant educators with insights and references.
基金Supported by National Natural Science Foundation of China(82374346)Double Hundred Outstanding Young and Middle-aged Medical and Health Talents of Wuxi City(BJ2023071)Scientific Research Project of Wuxi Municipal Health Commission(Q202358).
文摘This article reviews the research advances in traditional Chinese medicine rhubarb and its compound formulations in the treatment of sepsis,with particular emphasis on elucidating their mechanisms of action and clinical application value.Research has demonstrated that rhubarb and its compound formulations exert therapeutic effects via multiple targets and mechanisms,including anti-inflammatory actions,protection of the intestinal barrier,modulation of immune balance,inhibition of oxidative stress,and regulation of associated signaling pathways.Clinically,rhubarb has shown distinct advantages in enhancing gastrointestinal function,mitigating systemic inflammatory responses,and reducing mortality rates among patients with sepsis.These findings provide a foundational reference for the integrated prevention and treatment of sepsis through the combined use of traditional Chinese and Western medicine.
基金supported by ScientificResearch Fund of National Health Commission of the People’s Republic of China-Major Science and Technology Program for Medicine and Health in Zhejiang Province(WKJ-ZJ-2406).
文摘Objectives This study aimed to explore the lagged and cumulative effects of risk factors on disability in older adults using distributed lag non-linear models(DLNMs).Methods We utilized data from the China Health and Retirement Longitudinal Study(CHARLS).After feature selection via Elastic Net Regularization,we applied DLNMs to evaluate the lagged effects of risk factors.Disability was defined as the presence of any difficulties in basic activities of daily living(BADL).The cumulative relative risk(CRR)was calculated by summing the lag-specific risk estimates,representing the cumulative disability risk over the specified lag period.Effect modifications and sensitivity analyses were also performed.Results This study included a total of 2,318 participants.Early-phase lag factors,such as the difficulty in stooping(CRR=3.58;95%CI:2.31-5.55;P<0.001)and walking(CRR=2.77;95%CI:1.39-5.55;P<0.001),exerted the strongest effects immediately upon occurrence.Mid-phase lag factors,such as arthritis(CRR=1.51;95%CI:1.10-2.06;P=0.001),showed a resurgence in disability risk within 2-3 years.Late-phase lag factors,including depressive symptoms(CRR=2.38;95%CI:1.30-4.35;P<0.001)and elevated systolic blood pressure(CRR=1.64;95%CI:1.06-2.79;P=0.02),exhibited significant long-term cumulative risks.Conversely,grip strength(CRR=0.80;95%CI:0.54-0.95;P=0.02)and social participation(CRR=0.89;95%CI:0.73-0.99;P=0.04)were significant protective factors.Conclusions The findings underscore the importance of tailored interventions that account for various lag characteristics of different factors to effectively mitigate disability risk.Future studies should explore the underlying biological and sociological mechanisms of these lagged effects,identify intervention strategies that target risk factors with different lagged patterns,and evaluate their effectiveness.
