A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction betwe...A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction between polyelectrolyte and charged fluorophore. As for the system, the excimer-monomer conversion, thermosensitive behavior and thermo-responsive reversibility were investigated experimentally. Temperature variation and temperature-distribution induced fluorescence changes can be observed directly by naked eyes. Thus, this polymer system holds promise for serving as a fluorescent thermometer.展开更多
A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent...A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent property relative to solvents which can be turned-on by CH_(2)Cl_(2) and CHCl_(3) solvent molecules.The mechanism of this selective fluorescence emission has been studied based on the crystal structure and the spectrum analysis.The tuning on and off fluorescence of complex 1 can be controlled by the process of supramolecular aggregation/deag-gregation in different solvents.展开更多
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
Barren paddy fields characterized by poor soil structure,shallow tillage layers and low organic carbon content are a common limitation to rice production in subtropical China.As a novel approach to soil improvement,gr...Barren paddy fields characterized by poor soil structure,shallow tillage layers and low organic carbon content are a common limitation to rice production in subtropical China.As a novel approach to soil improvement,granulated organic amendments offer significant potential.Previous studies have shown that granulated straw can improve soil physicochemical properties and rapidly increase the soil organic carbon(SOC)content.However,their effects on barren paddies remain underexplored.This study evaluated four soil amendment strategies:no organic amendments(CK),10 t ha^(–1)of composted manure(M10),20 t ha^(–1)of granulated organic amendment(G20),and 40 t ha^(–1)of granulated organic amendment(G40).The objective was to assess the effects of these amendments on soil structure,the contents of aggregate-associated carbon(AAC),particulate organic carbon(POC)and mineral-associated organic carbon(MAOC),and the chemical stability of MAOC among various size aggregates in both topsoil(0–20 cm)and subsoil(20–40 cm).The results demonstrated that organic amendment inputs significantly increased the macroaggregate(>250μm)proportion and improved soil structural stability.These amendments also elevated the carbon concentration within aggregates of various sizes and facilitated the redistribution of organic carbon from microaggregates(53–250μm)and silt+clay fractions(<53μm)to macroaggregates.The proportion of POC to AAC declined with decreasing aggregate size,whereas the proportion of MAOC increased.In the topsoil,macroaggregate formation enhanced the protection of POC,supported the accumulation of non-hydrolyzable carbon within MAOC,and accelerated the formation of intra-microaggregates.In the subsoil,mineral-bound organic carbon remained the dominant form of carbon sequestration.In conclusion,the application of 40 t ha^(–1)of granulated organic amendment proved to be a successful tactic for enhancing soil physicochemical structure,increasing SOC content,and improving carbon stability.This approach offers a promising and innovative solution for the sustainable management and restoration of barren paddy fields.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
Protein aggregation drives proteinopathies ranging from ALS to systemic amyloidosis,yet the multiscale determinants bridging sequence,structure,and kinetics remain elusive.We present SKALE,an interpretable machine lea...Protein aggregation drives proteinopathies ranging from ALS to systemic amyloidosis,yet the multiscale determinants bridging sequence,structure,and kinetics remain elusive.We present SKALE,an interpretable machine learning framework that integrates sequence motifs,AlphaFold-derived structural descriptors,and experimental kinetics to decode aggregation mechanisms.SKALE identifies latent hotspots that evade conventional tools and matches high-performing neural baselines while preserving computational efficiency.In ALS-linked SOD1 G86R,the model isolates a risk region at residues 72-91 where preserved β-sheet geometry coincides with weakened hydrogen bonding to drive nucleation.Similarly,analysis of TDP-43 S332N reveals that a locally unwound helix increases surface exposure,a prediction validated by showing that targeted deletion of model-identified regions significantly reduces cellular aggregation.The framework generalizes to Tau P301L and PRNP variants where it uncovers distal aggregation-prone regions to discriminate pathogenic drivers from neutral mutations.Interpretability analysis further disentangles global from mutation-local mechanisms to reveal that β-sheet propensity acts as a shared determinant while hydrogen bond dynamics define specific routes to nucleation.These findings establish SKALE as a scalable,disease-agnostic engine that combines high-fidelity prediction with biophysical resolution to decode the molecular logic of misfolding and guide therapeutic design.展开更多
Asphaltenes generally exist in the form of molecular aggregates in crude oil or in petroleum residues,and asphaltene aggregates can usually cause serious problems to oil exploitation,transportation,and processing.Achi...Asphaltenes generally exist in the form of molecular aggregates in crude oil or in petroleum residues,and asphaltene aggregates can usually cause serious problems to oil exploitation,transportation,and processing.Achieving deaggregation and separation of asphaltene aggregates is a premise and basis for molecular characterization and processing of heavy oils.Aiming at the intermolecular interactions in asphaltene molecular aggregates,it has proposed and summarized that aspahltene aggregates can be subject to deaggregation by means of five approaches,including solvent diluting,removing active sites,moderate heating,ultrasonication and on-line molecular collision.Moreover,asphaltenes can be further separated to narrow fractions for molecular-level research based on polarity difference,molecular size difference,acid-base properties,and reactivity difference.展开更多
Due to the high affinity between dithiocarbamate （DTC） and Hg2＋, a fluorescent probe based on squaraine chromophore with DTC side arm for Hg2＋ via coordination induced deaggregation signaling has been designed and...Due to the high affinity between dithiocarbamate （DTC） and Hg2＋, a fluorescent probe based on squaraine chromophore with DTC side arm for Hg2＋ via coordination induced deaggregation signaling has been designed and synthesized. Squaraine has a high tendency to aggregate in aqueous solution, and such self-aggregation usually results in a dramatic absorption spectral broadening with fluorescence emission quenching. The combination of the DTC side arm of the probe with Hg2＋ induces steric hindrance, leading to the deaggregation of the dye complex, companying with a fluorescence emission restoration. In EtOH-H2O （20：80, v/v） solution, this ＂turn on＂ fluorescent probe has high selectivity and sensitivity toward Hg2＋ over other metal ions, and the limit of detection for Hg2＋ was estimated as 2.19 × 10^-8 mol/L by 3σ/k.展开更多
The deaggregating ability of β-CD and α-CD against the aggregated n-hexadecyl β-naphthoate （A16） and n-dodecyl β-naphthoate （A12） depended not only on the aggregating tendency of A16 and A12 but also on the in...The deaggregating ability of β-CD and α-CD against the aggregated n-hexadecyl β-naphthoate （A16） and n-dodecyl β-naphthoate （A12） depended not only on the aggregating tendency of A16 and A12 but also on the initial concentration of the aggregated A16 or A12. The inclusive ability of β-CD with the substrates is greater than that of α-CD under hydrophobiclipophilic interaction.展开更多
The new method for determining ground-motion parameters in the Indonesian Earthquake Resistant Building Code SNI （Indonesia National Standard） 03-1726-2012 has significant changes than the previous code. The maps of...The new method for determining ground-motion parameters in the Indonesian Earthquake Resistant Building Code SNI （Indonesia National Standard） 03-1726-2012 has significant changes than the previous code. The maps of mean and modal of magnitude and distance presented here are intended to convey information about the distribution ofprobabilistic seismic sources and to provide prescriptions or suggestions for seismic sources to use in developing artificial ground motion in building design or retrofit projects. This paper presents deaggregation of Indonesia Seismic Hazard Map 2010 for Sumatra. Deaggregation for 0.2-s and 1.0-s pseudo SA （spectral acceleration） is performed for 10% PE （probability of exceedance） in 50 years （475-year mean return period） and 2% PE in 50 years （2,475-year mean return period）. The information of deaggregation analysis can and perhaps should be considered in a complex seismic-resistant design decision-making environment.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and m...Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).展开更多
The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charg...The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.展开更多
As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and use...As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have sugg...Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.展开更多
Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,com...Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.展开更多
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio...Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.展开更多
Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the a...Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.展开更多
The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained...The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.展开更多
基金financially supported by the Science and Technology Planning Project of Guangdong Province(No.2014A010105009)the National Key Basic Research Program of China(No.2013CB834702)+1 种基金the National Natural Science Foundation of China(Nos.21574044 and 21474031)the Fundamental Research Funds for the Central Universities(No.2015ZY013)
文摘A simple and effective polymer fluorescent thermosensitive system was successfully developed based on the synergistic effect of excimer/monomer interconversion of pyrene derivatives and electrostatic interaction between polyelectrolyte and charged fluorophore. As for the system, the excimer-monomer conversion, thermosensitive behavior and thermo-responsive reversibility were investigated experimentally. Temperature variation and temperature-distribution induced fluorescence changes can be observed directly by naked eyes. Thus, this polymer system holds promise for serving as a fluorescent thermometer.
基金the National Science Council of the People’s Republic of China for supporting this research(Nos.21071018,21271026).
文摘A Cu(II)coordination complex(1)with Schiff ligand derived from diaminomaleonitrile was synthesized and characterized,in which the ligand is rigid,planar and conjugated.The complex 1 displays an interesting fluorescent property relative to solvents which can be turned-on by CH_(2)Cl_(2) and CHCl_(3) solvent molecules.The mechanism of this selective fluorescence emission has been studied based on the crystal structure and the spectrum analysis.The tuning on and off fluorescence of complex 1 can be controlled by the process of supramolecular aggregation/deag-gregation in different solvents.
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
基金financially supported by the National Key Research and Development Program of China(2024YFD1900104 and 2021YFD1901203)the National Natural Science Foundation of China(42177293,42130716 and U23A2009)the Chinese Academy of Sciences Talent Plan Program。
文摘Barren paddy fields characterized by poor soil structure,shallow tillage layers and low organic carbon content are a common limitation to rice production in subtropical China.As a novel approach to soil improvement,granulated organic amendments offer significant potential.Previous studies have shown that granulated straw can improve soil physicochemical properties and rapidly increase the soil organic carbon(SOC)content.However,their effects on barren paddies remain underexplored.This study evaluated four soil amendment strategies:no organic amendments(CK),10 t ha^(–1)of composted manure(M10),20 t ha^(–1)of granulated organic amendment(G20),and 40 t ha^(–1)of granulated organic amendment(G40).The objective was to assess the effects of these amendments on soil structure,the contents of aggregate-associated carbon(AAC),particulate organic carbon(POC)and mineral-associated organic carbon(MAOC),and the chemical stability of MAOC among various size aggregates in both topsoil(0–20 cm)and subsoil(20–40 cm).The results demonstrated that organic amendment inputs significantly increased the macroaggregate(>250μm)proportion and improved soil structural stability.These amendments also elevated the carbon concentration within aggregates of various sizes and facilitated the redistribution of organic carbon from microaggregates(53–250μm)and silt+clay fractions(<53μm)to macroaggregates.The proportion of POC to AAC declined with decreasing aggregate size,whereas the proportion of MAOC increased.In the topsoil,macroaggregate formation enhanced the protection of POC,supported the accumulation of non-hydrolyzable carbon within MAOC,and accelerated the formation of intra-microaggregates.In the subsoil,mineral-bound organic carbon remained the dominant form of carbon sequestration.In conclusion,the application of 40 t ha^(–1)of granulated organic amendment proved to be a successful tactic for enhancing soil physicochemical structure,increasing SOC content,and improving carbon stability.This approach offers a promising and innovative solution for the sustainable management and restoration of barren paddy fields.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
基金International Brain Research Organization(IBRO)Rising Star Awardee and received an IBRO Early Career Principal Investigator Grant(No.PM010CNI000148)supported by Sunway University internal grant(No.GRTIN-IGS[02]-CVVR-11-2023)+2 种基金supported by the Fundamental Research Funds from the Central of Public Welfare Research Institute,China Rehabilitation Institutesupported by the research initiation funding scheme provided by Henan University of Technology(No.0004/31401568)Shenzhen Vaccine Biopharmaceuticals Limited(No.0004/51100292).
文摘Protein aggregation drives proteinopathies ranging from ALS to systemic amyloidosis,yet the multiscale determinants bridging sequence,structure,and kinetics remain elusive.We present SKALE,an interpretable machine learning framework that integrates sequence motifs,AlphaFold-derived structural descriptors,and experimental kinetics to decode aggregation mechanisms.SKALE identifies latent hotspots that evade conventional tools and matches high-performing neural baselines while preserving computational efficiency.In ALS-linked SOD1 G86R,the model isolates a risk region at residues 72-91 where preserved β-sheet geometry coincides with weakened hydrogen bonding to drive nucleation.Similarly,analysis of TDP-43 S332N reveals that a locally unwound helix increases surface exposure,a prediction validated by showing that targeted deletion of model-identified regions significantly reduces cellular aggregation.The framework generalizes to Tau P301L and PRNP variants where it uncovers distal aggregation-prone regions to discriminate pathogenic drivers from neutral mutations.Interpretability analysis further disentangles global from mutation-local mechanisms to reveal that β-sheet propensity acts as a shared determinant while hydrogen bond dynamics define specific routes to nucleation.These findings establish SKALE as a scalable,disease-agnostic engine that combines high-fidelity prediction with biophysical resolution to decode the molecular logic of misfolding and guide therapeutic design.
文摘Asphaltenes generally exist in the form of molecular aggregates in crude oil or in petroleum residues,and asphaltene aggregates can usually cause serious problems to oil exploitation,transportation,and processing.Achieving deaggregation and separation of asphaltene aggregates is a premise and basis for molecular characterization and processing of heavy oils.Aiming at the intermolecular interactions in asphaltene molecular aggregates,it has proposed and summarized that aspahltene aggregates can be subject to deaggregation by means of five approaches,including solvent diluting,removing active sites,moderate heating,ultrasonication and on-line molecular collision.Moreover,asphaltenes can be further separated to narrow fractions for molecular-level research based on polarity difference,molecular size difference,acid-base properties,and reactivity difference.
基金financially supported by the National Science Foundation for Fostering Talents in Basic Research of the National Natural Science Foundation of China (No.J1103303)the National Natural Science Foundation of China (No.20702005)+2 种基金the Fujian Provincial Department of Science and Technology,China (No.2013Y0062)Funding (Type A) from Fujian Education Department,PR China (Nos.JA12038 and JA13043)the Science and Technology Development Fund of Fuzhou University,China (No.600902)
文摘Due to the high affinity between dithiocarbamate （DTC） and Hg2＋, a fluorescent probe based on squaraine chromophore with DTC side arm for Hg2＋ via coordination induced deaggregation signaling has been designed and synthesized. Squaraine has a high tendency to aggregate in aqueous solution, and such self-aggregation usually results in a dramatic absorption spectral broadening with fluorescence emission quenching. The combination of the DTC side arm of the probe with Hg2＋ induces steric hindrance, leading to the deaggregation of the dye complex, companying with a fluorescence emission restoration. In EtOH-H2O （20：80, v/v） solution, this ＂turn on＂ fluorescent probe has high selectivity and sensitivity toward Hg2＋ over other metal ions, and the limit of detection for Hg2＋ was estimated as 2.19 × 10^-8 mol/L by 3σ/k.
基金We thank the Natural Science Foundation of China（No.20172069）for financial support.
文摘The deaggregating ability of β-CD and α-CD against the aggregated n-hexadecyl β-naphthoate （A16） and n-dodecyl β-naphthoate （A12） depended not only on the aggregating tendency of A16 and A12 but also on the initial concentration of the aggregated A16 or A12. The inclusive ability of β-CD with the substrates is greater than that of α-CD under hydrophobiclipophilic interaction.
文摘The new method for determining ground-motion parameters in the Indonesian Earthquake Resistant Building Code SNI （Indonesia National Standard） 03-1726-2012 has significant changes than the previous code. The maps of mean and modal of magnitude and distance presented here are intended to convey information about the distribution ofprobabilistic seismic sources and to provide prescriptions or suggestions for seismic sources to use in developing artificial ground motion in building design or retrofit projects. This paper presents deaggregation of Indonesia Seismic Hazard Map 2010 for Sumatra. Deaggregation for 0.2-s and 1.0-s pseudo SA （spectral acceleration） is performed for 10% PE （probability of exceedance） in 50 years （475-year mean return period） and 2% PE in 50 years （2,475-year mean return period）. The information of deaggregation analysis can and perhaps should be considered in a complex seismic-resistant design decision-making environment.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
文摘Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).
基金supported by Jiangsu Provincial Science and Technology Project,grant number J2023124.Jing Guo received this grant,the URLs of sponsors’website is https://kxjst.jiangsu.gov.cn/(accessed on 06 June 2024).
文摘The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system.This study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation platforms.Our approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation attacks.We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation.The xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging data.Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging points.Furthermore,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 nodes.This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.
基金supported by the National Key R&D Program of China(No.2023YFB2703700)the National Natural Science Foundation of China(Nos.U21A20465,62302457,62402444,62172292)+4 种基金the Fundamental Research Funds of Zhejiang Sci-Tech University(Nos.23222092-Y,22222266-Y)the Program for Leading Innovative Research Team of Zhejiang Province(No.2023R01001)the Zhejiang Provincial Natural Science Foundation of China(Nos.LQ24F020008,LQ24F020012)the Foundation of State Key Laboratory of Public Big Data(No.[2022]417)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01119).
文摘As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported by the Innovation and Technology Commission (ITCPD/17-9)the Hong Kong Research Grants Council,China (GRF16104517)(to KKKC)。
文摘Parkinson's disease (PD) is a common degenerative disorder that is becoming increasingly prevalent because of the global aging population.The exact cause of the disorder is unknown;however,recent studies have suggested that multiple factors may contribute to its pathogenesis.PD is characterized by a movement disorder that primarily affects motor control;pathologically,the disease is marked by the presence of Lewy bodies (LBs) in the brain.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2022R1C1C2012463).
文摘Integrating Artificial Intelligence of Things(AIoT)in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges,including privacy preservation,computational efficiency,and regulatory compliance.Traditional approaches,such as differential privacy,homomorphic encryption,and secure multi-party computation,often fail to balance performance and privacy,rendering them unsuitable for resource-constrained healthcare AIoT environments.This paper introduces LMSA(Lightweight Multi-Key Secure Aggregation),a novel framework designed to address these challenges and enable efficient,secure federated learning across distributed healthcare institutions.LMSA incorporates three key innovations:(1)a lightweight multikey management system leveraging Diffie-Hellman key exchange and SHA3-256 hashing,achieving O(n)complexity with AES(Advanced Encryption Standard)-256-level security;(2)a privacy-preserving aggregation protocol employing hardware-accelerated AES-CTR(CounTeR)encryption andmodular arithmetic for securemodel weight combination;and(3)a resource-optimized implementation utilizing AES-NI(New Instructions)instructions and efficient memory management for real-time operations on constrained devices.Experimental evaluations using the National Institutes of Health(NIH)Chest X-ray dataset demonstrate LMSA’s ability to train multi-label thoracic disease prediction models with Vision Transformer(ViT),ResNet-50,and MobileNet architectures across distributed healthcare institutions.Memory usage analysis confirmed minimal overhead,with ViT(327.30 MB),ResNet-50(89.87 MB),and MobileNet(8.63 MB)maintaining stable encryption times across communication rounds.LMSA ensures robust security through hardware acceleration,enabling real-time diagnostics without compromising patient confidentiality or regulatory compliance.Future research aims to optimize LMSA for ultra-low-power devices and validate its scalability in heterogeneous,real-world environments.LMSA represents a foundational advancement for privacy-conscious healthcare AI applications,bridging the gap between privacy and performance.
基金funded by the Deanship of Scientific Research,the Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia under the project(KFU250420).
文摘Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.
基金supported by State Grid science and technology projects“Research on energy and power sup-ply and demand interactive simulation technology for new power system(5100-202257028A-1-1-ZN)”.
文摘Increasing interest has been directed toward the potential of heterogeneous flexible loads to mitigate the challenges associated with the increasing variability and uncertainty of renewable generation.Evaluating the aggregated flexible region of load clusters managed by load aggregators is the crucial basis of power system scheduling for the system operator.This is because the aggregation result affects the qual-ity of the scheduling schemes.A stringent computation based on the Minkowski sum is NP-hard,whereas existing approximation meth-ods that use a special type of polytope exhibit limited adaptability when aggregating heterogeneous loads.This study proposes a stringent internal approximation method based on the convex hull of multiple layers of maximum volume boxes and embeds it into a day-ahead scheduling optimization model.The numerical results indicate that the aggregation accuracy can be improved compared with methods based on one type of special polytope,including boxes,zonotopes,and homothets.Hence,the reliability and economy of the power sys-tem scheduling can be enhanced.
基金Funds for High-Level Talents Programof Xi’an International University(Grant No.XAIU202411).
文摘The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.
