In an attempt to demonstrate the biological activities of a short peptide.Arg-Gly-Asp- Ser (RGDS) was synthesized and used for bioassay,The data obtained here proved that RGDS ob- viously inhibited PAF- and/or ADP-ind...In an attempt to demonstrate the biological activities of a short peptide.Arg-Gly-Asp- Ser (RGDS) was synthesized and used for bioassay,The data obtained here proved that RGDS ob- viously inhibited PAF- and/or ADP-induced platelet aggregation.The present paper revealed that RG- DS had vasodilative action and the cGMP accumulation may be one of the mechanisms of RGDS exer- ting bioactivities.展开更多
To reduce mucosal damage in the gastrointestinal tract caused by aspirin,we developed a dissolvable polymeric microneedle(MN)patch loaded with aspirin.Biodegradable polymers provide mechanical strength to the MNs.The ...To reduce mucosal damage in the gastrointestinal tract caused by aspirin,we developed a dissolvable polymeric microneedle(MN)patch loaded with aspirin.Biodegradable polymers provide mechanical strength to the MNs.The MN tips punctured the cuticle of the skin and dissolved when in contact with the subcutaneous tissue.The aspirin in the MN patch is delivered continuously through an array of micropores created by the punctures,providing a stable plasma concentration of aspirin.The factors affecting the stability of aspirin during MNs fabrication were comprehensively analyzed,and the hydrolysis rate of aspirin in the MNs was less than 2%.Compared to oral administration,MN administration not only had a smoother plasma concentration curve but also resulted in a lower effective dose of antiplatelet aggregation.Aspirin-loaded MNs were mildly irritating to the skin,causing only slight erythema on the skin and recovery within 24 h.In summary,aspirin-loaded MNs provide a new method to reduce gastrointestinal adverse effects in patients requiring aspirin regularly.展开更多
Platelet hyper-aggregability triggered death and disability due to cardiovascular diseases is increasing worldwide and becoming a global concern. Therefore, it is necessary to synthesize newer drugs for the management...Platelet hyper-aggregability triggered death and disability due to cardiovascular diseases is increasing worldwide and becoming a global concern. Therefore, it is necessary to synthesize newer drugs for the management of platelet aggregation. In this study, we investigated the antiplatelet aggregation activity of a novel series of ferrocenylimine compounds(3–10), N-(3-nitro-2-hydroxylbenzylidene)-3-ferrocenylimine(3), N-(3-bromo-2-hydroxylbenzylidene)-3-ferrocenylimine(4), N-(3-bromo-5-chlorosalicylidene)-3-ferrocenylimine(5), N-(ferrocenylformidene)-3-ferrocenylimine(6), N-(3-nitro-2-hydroxylbenzylidene)-4-ferrocenylimine(7), N-(3-bromo-2-hydroxylbenzylidene)-4-ferrocenylimine(8), N-(3-bromo-5-chlorosalicyl)-4-ferrocenylimine(9), N-(ferrocenylformidene)-4-ferrocenylimine(10) on thrombin-and ADP-induced platelet aggregation. The synthesized ferrocenylimine compounds(3–10) were found to exhibit higher antiplatelet aggregation activity than their precursors, which are 3-ferrocenylaniline(compound 1) and 4-ferrocenylaniline(compound 2). Among the derivatives, compounds 5, 6 and 10 possessed excellent platelet aggregation inhibition against the agonists.展开更多
Antiplatelet aggregation effects of YIGSK, RGDS, RGDV, RGDF, YIGSKRGDS, YIGSKRGDV and YIGSKRGDF were observed. By comparing their activities it was found that by coupling YIGSK and RGD containing peptides the antiplat...Antiplatelet aggregation effects of YIGSK, RGDS, RGDV, RGDF, YIGSKRGDS, YIGSKRGDV and YIGSKRGDF were observed. By comparing their activities it was found that by coupling YIGSK and RGD containing peptides the antiplatelet aggregation effects of some of the compounds may be enhanced.展开更多
In the present study, five fluorine substituted and three chlorine substituted 1,3-dihydroxyxanthones were synthesized in one step.The yields ranged from 48% to 72%.Among them, compounds 12 and 15–18 were reported fo...In the present study, five fluorine substituted and three chlorine substituted 1,3-dihydroxyxanthones were synthesized in one step.The yields ranged from 48% to 72%.Among them, compounds 12 and 15–18 were reported for the first time.The antitumor, antityrosinase and antiplatelet aggregation activities of all or part of compounds 1–19 were evaluated.Compounds 1, 2, 4, 6–7, 10–15 and 19 exhibited enhanced cytotoxicity against certain cancer cells.Compound 10, containing 2,4-difluorophenyl at the C7 position, particularly exhibited superior antitumor activity.The inhibition rate of compound 18 against tyrosinase was approximately 22%.Compounds 1–3, 6, 9, 12 and 18, 19 exhibited obvious inhibitory platelet aggregation induced by ADP in rats.Moreover, the effects of compounds 2 and 3 were more pronounced.These results demonstrated that compounds 1–4, 6–7, 9–15 and 19 were promising leads for further structural modification.展开更多
BACKGROUND Peripheral artery disease(PAD)affects millions globally,with a 5.6%prevalence in 2015 impacting 236 million adults,rising above 10%in those over 60 due to factors like diabetes and smoking.Post-revasculariz...BACKGROUND Peripheral artery disease(PAD)affects millions globally,with a 5.6%prevalence in 2015 impacting 236 million adults,rising above 10%in those over 60 due to factors like diabetes and smoking.Post-revascularization,single antiplatelet therapy(SAPT)is standard,but dual antiplatelet therapy(DAPT)may improve outcomes,though duration and bleeding risks are unclear.The 2024 American College of Cardiology/American Heart Association guidelines endorse short-term DAPT,yet evidence gaps remain in comparative efficacy and safety.We hypothesized that DAPT reduces cardiovascular events and reinterventions vs SAPT without significantly elevating bleeding in PAD patients’post-lower extremity revascularization.AIM To evaluate the efficacy and safety of DAPT vs SAPT in PAD patients’post-revascularization.METHODS This systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,searching PubMed,EMBASE,and ScienceDirect up to July 2025.Included were randomized controlled trials(RCTs)and cohort studies from various global settings(e.g.,hospitals,tertiary care)comparing DAPT(aspirin plus P2Y12 inhibitor for>1 month)to SAPT in symptomatic PAD patients undergoing endovascular or surgical revascularization(n up to 28244 participants selected via eligibility criteria).Data were pooled using random-effects models for risk ratio(RR)with 95%CI;heterogeneity was assessed via the I²statistic.Quality appraisal used Risk of Bias in Non-randomized Studies of Interventions for cohorts and Risk of Bias 2.0 for RCTs;certainty was evaluated via Grading of Recommendations Assessment,Development and Evaluation(GRADE).RESULTS Twelve studies(3 RCTs,9 cohorts,conducted 2010–2025 with follow-ups of 6 months to 5 years)were included.DAPT showed no significant difference but a trend toward reduced all-cause mortality(RR:0.52,95%CI:0.27–1.01,P=0.05,DAPT of 298/9545 events vs SAPT of 165/566 events)or stroke(RR:0.72,95%CI:0.30–1.72,P=0.46,DAPT of 16/3729 events vs SAPT of 41/7673 events)vs SAPT.DAPT significantly reduced cardiac mortality(RR:0.46,95%CI:0.27–0.80,P=0.006,DAPT of 78/2903 events vs SAPT of 171/1465 events,risk difference:-5.4%),myocardial infarction(RR:0.82,95%CI:0.71–0.94,P=0.004,DAPT of 233/7704 events vs SAPT of 262/9130 events,risk difference:-1.8%),and major reintervention(RR:0.58,95%CI:0.35–0.98,P=0.04,DAPT of 803/205 events vs SAPT of 1197/4 events,risk difference:-42%).Bleeding showed no difference(RR:1.12,95%CI:0.42–3.03,P=0.82,DAPT of 195/2775 events vs SAPT of 202/8234 events).Heterogeneity was high(I^(2)=59%–97%).Quality revealed moderate to serious bias in cohorts and some concerns in RCTs;GRADE certainty moderate for cardiac mortality,myocardial infarction,reintervention,low for others due to inconsistency and imprecision.CONCLUSION DAPT reduces cardiac mortality,myocardial infarction,and major reintervention risks compared to SAPT in PAD post-revascularization without apparent bleeding increase,though limited by heterogeneity and low certainty for some outcomes.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
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
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
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
Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices i...Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.展开更多
In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative mu...In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.展开更多
OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome...OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome(ACS)rats and the intervention of Huayu Qutan formula(化瘀祛痰方).METHODS:The ACS rats with syndrome of phlegm and blood stasis rats were established.Platelet,platelet aggregation,platelet activation markers and TMA/FMO3/TMAO pathway were detected.Metagenomics technology was employed to analyze the characteristics of the gut microbiota.RESULTS:Huayu Qutan formula and gut microbes could inhibit high platelet reactivity and regulate the TMA/FMO3/TMAO pathway.The dominant bacteria in ACS rats including but not limited to the major phyla,Firmicutes,Bacteroidetes,Actinobacteria,and Proteobacteria,also including some low abundance phyla,Fusobacteria,Verrucomicrobia,Spirochaetes,and Deferribacteres.The dominant bacteria in the Huayu Qutan formula group were Synergistetes,Deferribacteres,Deferribacteraceae,Faecalibacterium and Mucispirillum.In the Huayu Qutan formula combined with fecal bacteria enema group,the dominant bacteria were Verrucomicrobia,Verrucomicrobiae,Akkermansia and Verrucomicrobium.These gut microbiota were correlated with pathways such as Riboflavin metabolism and Arachidonic acid metabolism.CONCLUSION:Huayu Qutan formula may prevent ACS by modulating gut microbes Synergistetes,Faecalibacterium and Allobaculum,regulating the iron metabolism of Deferribacteres,and driving the TMA/FMO3/TMAO pathway to regulate gut microbiota function,and improving platelet aggregation.Akkermansia may serve as a promising probiotic,which could drive TMA/FMO3/TMAO pathway to regulate Arachidonic acid metabolism to improve platelet aggregation.The findings of this study provide a theoretical basis for the theory of"the heart is connected with the small intestine".展开更多
To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous ...Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.展开更多
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w...Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.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
基金This project was supported by the National Natural Science Foundation
文摘In an attempt to demonstrate the biological activities of a short peptide.Arg-Gly-Asp- Ser (RGDS) was synthesized and used for bioassay,The data obtained here proved that RGDS ob- viously inhibited PAF- and/or ADP-induced platelet aggregation.The present paper revealed that RG- DS had vasodilative action and the cGMP accumulation may be one of the mechanisms of RGDS exer- ting bioactivities.
基金by the National Key Research and Development Plan of China[No.2016YFC1000902].
文摘To reduce mucosal damage in the gastrointestinal tract caused by aspirin,we developed a dissolvable polymeric microneedle(MN)patch loaded with aspirin.Biodegradable polymers provide mechanical strength to the MNs.The MN tips punctured the cuticle of the skin and dissolved when in contact with the subcutaneous tissue.The aspirin in the MN patch is delivered continuously through an array of micropores created by the punctures,providing a stable plasma concentration of aspirin.The factors affecting the stability of aspirin during MNs fabrication were comprehensively analyzed,and the hydrolysis rate of aspirin in the MNs was less than 2%.Compared to oral administration,MN administration not only had a smoother plasma concentration curve but also resulted in a lower effective dose of antiplatelet aggregation.Aspirin-loaded MNs were mildly irritating to the skin,causing only slight erythema on the skin and recovery within 24 h.In summary,aspirin-loaded MNs provide a new method to reduce gastrointestinal adverse effects in patients requiring aspirin regularly.
基金Global Excellence and Stature(GES),2016,University of Johannesburg
文摘Platelet hyper-aggregability triggered death and disability due to cardiovascular diseases is increasing worldwide and becoming a global concern. Therefore, it is necessary to synthesize newer drugs for the management of platelet aggregation. In this study, we investigated the antiplatelet aggregation activity of a novel series of ferrocenylimine compounds(3–10), N-(3-nitro-2-hydroxylbenzylidene)-3-ferrocenylimine(3), N-(3-bromo-2-hydroxylbenzylidene)-3-ferrocenylimine(4), N-(3-bromo-5-chlorosalicylidene)-3-ferrocenylimine(5), N-(ferrocenylformidene)-3-ferrocenylimine(6), N-(3-nitro-2-hydroxylbenzylidene)-4-ferrocenylimine(7), N-(3-bromo-2-hydroxylbenzylidene)-4-ferrocenylimine(8), N-(3-bromo-5-chlorosalicyl)-4-ferrocenylimine(9), N-(ferrocenylformidene)-4-ferrocenylimine(10) on thrombin-and ADP-induced platelet aggregation. The synthesized ferrocenylimine compounds(3–10) were found to exhibit higher antiplatelet aggregation activity than their precursors, which are 3-ferrocenylaniline(compound 1) and 4-ferrocenylaniline(compound 2). Among the derivatives, compounds 5, 6 and 10 possessed excellent platelet aggregation inhibition against the agonists.
文摘Antiplatelet aggregation effects of YIGSK, RGDS, RGDV, RGDF, YIGSKRGDS, YIGSKRGDV and YIGSKRGDF were observed. By comparing their activities it was found that by coupling YIGSK and RGD containing peptides the antiplatelet aggregation effects of some of the compounds may be enhanced.
基金Fujian Provincial Department of Science and Technology(Grant No.2018Y0072)
文摘In the present study, five fluorine substituted and three chlorine substituted 1,3-dihydroxyxanthones were synthesized in one step.The yields ranged from 48% to 72%.Among them, compounds 12 and 15–18 were reported for the first time.The antitumor, antityrosinase and antiplatelet aggregation activities of all or part of compounds 1–19 were evaluated.Compounds 1, 2, 4, 6–7, 10–15 and 19 exhibited enhanced cytotoxicity against certain cancer cells.Compound 10, containing 2,4-difluorophenyl at the C7 position, particularly exhibited superior antitumor activity.The inhibition rate of compound 18 against tyrosinase was approximately 22%.Compounds 1–3, 6, 9, 12 and 18, 19 exhibited obvious inhibitory platelet aggregation induced by ADP in rats.Moreover, the effects of compounds 2 and 3 were more pronounced.These results demonstrated that compounds 1–4, 6–7, 9–15 and 19 were promising leads for further structural modification.
文摘BACKGROUND Peripheral artery disease(PAD)affects millions globally,with a 5.6%prevalence in 2015 impacting 236 million adults,rising above 10%in those over 60 due to factors like diabetes and smoking.Post-revascularization,single antiplatelet therapy(SAPT)is standard,but dual antiplatelet therapy(DAPT)may improve outcomes,though duration and bleeding risks are unclear.The 2024 American College of Cardiology/American Heart Association guidelines endorse short-term DAPT,yet evidence gaps remain in comparative efficacy and safety.We hypothesized that DAPT reduces cardiovascular events and reinterventions vs SAPT without significantly elevating bleeding in PAD patients’post-lower extremity revascularization.AIM To evaluate the efficacy and safety of DAPT vs SAPT in PAD patients’post-revascularization.METHODS This systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines,searching PubMed,EMBASE,and ScienceDirect up to July 2025.Included were randomized controlled trials(RCTs)and cohort studies from various global settings(e.g.,hospitals,tertiary care)comparing DAPT(aspirin plus P2Y12 inhibitor for>1 month)to SAPT in symptomatic PAD patients undergoing endovascular or surgical revascularization(n up to 28244 participants selected via eligibility criteria).Data were pooled using random-effects models for risk ratio(RR)with 95%CI;heterogeneity was assessed via the I²statistic.Quality appraisal used Risk of Bias in Non-randomized Studies of Interventions for cohorts and Risk of Bias 2.0 for RCTs;certainty was evaluated via Grading of Recommendations Assessment,Development and Evaluation(GRADE).RESULTS Twelve studies(3 RCTs,9 cohorts,conducted 2010–2025 with follow-ups of 6 months to 5 years)were included.DAPT showed no significant difference but a trend toward reduced all-cause mortality(RR:0.52,95%CI:0.27–1.01,P=0.05,DAPT of 298/9545 events vs SAPT of 165/566 events)or stroke(RR:0.72,95%CI:0.30–1.72,P=0.46,DAPT of 16/3729 events vs SAPT of 41/7673 events)vs SAPT.DAPT significantly reduced cardiac mortality(RR:0.46,95%CI:0.27–0.80,P=0.006,DAPT of 78/2903 events vs SAPT of 171/1465 events,risk difference:-5.4%),myocardial infarction(RR:0.82,95%CI:0.71–0.94,P=0.004,DAPT of 233/7704 events vs SAPT of 262/9130 events,risk difference:-1.8%),and major reintervention(RR:0.58,95%CI:0.35–0.98,P=0.04,DAPT of 803/205 events vs SAPT of 1197/4 events,risk difference:-42%).Bleeding showed no difference(RR:1.12,95%CI:0.42–3.03,P=0.82,DAPT of 195/2775 events vs SAPT of 202/8234 events).Heterogeneity was high(I^(2)=59%–97%).Quality revealed moderate to serious bias in cohorts and some concerns in RCTs;GRADE certainty moderate for cardiac mortality,myocardial infarction,reintervention,low for others due to inconsistency and imprecision.CONCLUSION DAPT reduces cardiac mortality,myocardial infarction,and major reintervention risks compared to SAPT in PAD post-revascularization without apparent bleeding increase,though limited by heterogeneity and low certainty for some outcomes.
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.
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.
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.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20230628015the State Key Laboratory of Particle Detection and Electronics under Grant No.SKLPDE-KF-202314.
文摘Digital twin is a novel technology that has achieved significant progress in industrial manufactur-ing systems in recent years.In the digital twin envi-ronment,entities in the virtual space collect data from devices in the physical space to analyze their states.However,since a lot of devices exist in the physical space,the digital twin system needs to aggregate data from multiple devices at the edge gateway.Homomor-phic integrity and confidentiality protections are two important requirements for this data aggregation pro-cess.Unfortunately,existing homomorphic encryp-tion algorithms do not support integrity protection,and existing homomorphic signing algorithms require all signers to use the same signing key,which is not feasible in the digital twin environment.Moreover,for both integrity and confidentiality protections,the homomorphic signing algorithm must be compatible with the aggregation manner of the homomorphic en-cryption algorithm.To address these issues,this paper designs a novel homomorphic aggregation scheme,which allows multiple devices in the physical space to sign different data using different keys and support in-tegrity and confidentiality protections.Finally,the security of the newly designed scheme is analyzed,and its efficiency is evaluated.Experimental results show that our scheme is feasible for real world applications.
基金the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.,with the project number 52051L240001.
文摘In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.
基金Supported by National Natural Science Foundation of China Project:based on the Theory of“the Heart is in Harmony with the Small Intestine”to Explore the Influence and Mechanism of Gut Microbes on High Platelet Reactivity of Acute Coronary Syndrome with Phlegm and Blood Stasis Syndrome(No.82104841)Education Department of Liaoning Province Young Science and Technology Talents"Seedling"Project:to Explore the Effect and Mechanism of Huayu Qutan Formula on Platelet Function in Acute Coronary Syndrome Patients with Phlegm and Blood Stasis Syndrome after Percutaneous Coronary Intervention based on Intestinal Microbiome(No.L202039)。
文摘OBJECTIVE:To investigate the effects of gut microbes regulation of the trimethylamine(TMA)/flavin containing monooxygenase 3(FMO3)/trimethylamine N-oxide(TMAO)pathway on platelet aggregation in acute coronary syndrome(ACS)rats and the intervention of Huayu Qutan formula(化瘀祛痰方).METHODS:The ACS rats with syndrome of phlegm and blood stasis rats were established.Platelet,platelet aggregation,platelet activation markers and TMA/FMO3/TMAO pathway were detected.Metagenomics technology was employed to analyze the characteristics of the gut microbiota.RESULTS:Huayu Qutan formula and gut microbes could inhibit high platelet reactivity and regulate the TMA/FMO3/TMAO pathway.The dominant bacteria in ACS rats including but not limited to the major phyla,Firmicutes,Bacteroidetes,Actinobacteria,and Proteobacteria,also including some low abundance phyla,Fusobacteria,Verrucomicrobia,Spirochaetes,and Deferribacteres.The dominant bacteria in the Huayu Qutan formula group were Synergistetes,Deferribacteres,Deferribacteraceae,Faecalibacterium and Mucispirillum.In the Huayu Qutan formula combined with fecal bacteria enema group,the dominant bacteria were Verrucomicrobia,Verrucomicrobiae,Akkermansia and Verrucomicrobium.These gut microbiota were correlated with pathways such as Riboflavin metabolism and Arachidonic acid metabolism.CONCLUSION:Huayu Qutan formula may prevent ACS by modulating gut microbes Synergistetes,Faecalibacterium and Allobaculum,regulating the iron metabolism of Deferribacteres,and driving the TMA/FMO3/TMAO pathway to regulate gut microbiota function,and improving platelet aggregation.Akkermansia may serve as a promising probiotic,which could drive TMA/FMO3/TMAO pathway to regulate Arachidonic acid metabolism to improve platelet aggregation.The findings of this study provide a theoretical basis for the theory of"the heart is connected with the small intestine".
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金supported by Shanghai Municipal Commission of Science and Technology,China(Grant No.:19XD1400300)the National Natural Science Foundation of China(Grant Nos.:821040821,82273867,and 82030107).
文摘Amphiphiles,including surfactants,have emerged as indispensable elements in materials science and pharmaceutical science,and their functions are highly relying on the critical micelle concentration(CMC)[1,2].Numerous fluorimetry-based probes have been developed to measure CMCs[3](Fig.S1).However,CMC measurements using these probes suffer from a time-consuming and laborious procedure and large uncertainties,primarily due to their poor photo-stabilities and highly fluctuating fluorescence backgrounds.
基金supported By Guangdong Major Project of Basic and Applied Basic Research(2023B0303000009)Guangdong Basic and Applied Basic Research Foundation(2024A1515030153,2025A1515011587)+1 种基金Project of Department of Education of Guangdong Province(2023ZDZX4046)Shen-zhen Natural Science Fund(Stable Support Plan Program 20231122121608001),Ningbo Municipal Science and Technology Bureau(ZX2024000604).
文摘Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.
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.
