Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of a...Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.展开更多
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h...Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deploye...Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.展开更多
Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a challenge.Effective resource allocation in cloud-based environmen...Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a challenge.Effective resource allocation in cloud-based environments,particularly within the IaaS model,poses persistent challenges.Existing methods often struggle with slow opti-mization,imbalanced workload distribution,and inefficient use of available assets.These limitations result in longer processing times,increased operational expenses,and inadequate resource deployment,particularly under fluctuating demands.To overcome these issues,a novel Clustered Input-Oriented Salp Swarm Algorithm(CIOSSA)is introduced.This approach combines two distinct strategies:Task Splitting Agglomerative Clustering(TSAC)with an Input Oriented Salp Swarm Algorithm(IOSSA),which prioritizes tasks based on urgency,and a refined multi-leader model that accelerates optimization processes,enhancing both speed and accuracy.By continuously assessing system capacity before task distribution,the model ensures that assets are deployed effectively and costs are controlled.The dual-leader technique expands the potential solution space,leading to substantial gains in processing speed,cost-effectiveness,asset efficiency,and system throughput,as demonstrated by comprehensive tests.As a result,the suggested model performs better than existing approaches in terms of makespan,resource utilisation,throughput,and convergence speed,demonstrating that CIOSSA is scalable,reliable,and appropriate for the dynamic settings found in cloud computing.展开更多
Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic captur...Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.展开更多
Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show ...Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.展开更多
There are more than a thousand trillion specific synaptic connections in the human brain and over a million new specific connections are formed every second during the early years of life. The assembly of these stagge...There are more than a thousand trillion specific synaptic connections in the human brain and over a million new specific connections are formed every second during the early years of life. The assembly of these staggeringly complex neuronal circuits requires specific cell-surface molecular tags to endow each neuron with a unique identity code to discriminate self from non-self. The clustered protocadherin(Pcdh) genes, which encode a tremendous diversity of cell-surface assemblies, are candidates for neuronal identity tags. We describe the adaptive evolution,genomic structure, and regulation of expression of the clustered Pcdhs. We specifically focus on the emerging3-D architectural and biophysical mechanisms that generate an enormous number of diverse cell-surface Pcdhs as neural codes in the brain.展开更多
Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and relia...Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.展开更多
This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for...This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.展开更多
Absorption of acoustic nanowave in specific frequency region is important for the design of acoustic filter. This paper puts forward a meta material model made up of fluid-conveying carbon nanotubes （CNT）, which can...Absorption of acoustic nanowave in specific frequency region is important for the design of acoustic filter. This paper puts forward a meta material model made up of fluid-conveying carbon nanotubes （CNT）, which can absorb acoustic nanowave in a given frequency range by adjusting the lengths and fluid velocities of themselves. Absorption coefficients are calculated out through the combination of the finite element method with the theoretical model, which are 0.4-0.55 relating to different fluid velocities for the crossing-distributed model. Comparisons are made between the crossed model and the aligned one, which prove that the CNT forest with crossed distribution is more effective in acoustic wave absorption.展开更多
The scientific community is continuously working to translate the novel biomedical techniques into effective medical treatments.CRISPR-Cas9 system(Clustered Regularly Interspaced Short Palindromic Repeats-9),commonly ...The scientific community is continuously working to translate the novel biomedical techniques into effective medical treatments.CRISPR-Cas9 system(Clustered Regularly Interspaced Short Palindromic Repeats-9),commonly known as the“molecular scissor”,represents a recently developed biotechnology able to improve the quality and the efficacy of traditional treatments,related to several human diseases,such as chronic diseases,neurodegenerative pathologies and,interestingly,oral diseases.Of course,dental medicine has notably increased the use of biotechnologies to ensure modern and conservative approaches:in this landscape,the use of CRISPR-Cas9 system may speed and personalize the traditional therapies,ensuring a good predictability of clinical results.The aim of this critical overview is to provide evidence on CRISPR efficacy,taking into specific account its applications in oral medicine.展开更多
Influenced by the environment and nodes status,the quality of link is not always stable in actual wireless sensor networks( WSNs). Poor links result in retransmissions and more energy consumption. So link quality is a...Influenced by the environment and nodes status,the quality of link is not always stable in actual wireless sensor networks( WSNs). Poor links result in retransmissions and more energy consumption. So link quality is an important issue in the design of routing protocol which is not considered in most traditional clustered routing protocols. A based on energy and link quality's routing protocol( EQRP) is proposed to optimize the clustering mechanism which takes into account energy balance and link quality factors. EQRP takes the advantage of high quality links to increase success rate of single communication and reduce the cost of communication. Simulation shows that,compared with traditional clustered protocol,EQRP can perform 40% better,in terms of life cycle of the whole network.展开更多
Background: Pain management for term newborns undergoing clustered painful procedures has not been tested. Kangaroo Care (chest-to-chest, skin-to-skin position of infant on mother) effectively reduces pain o...Background: Pain management for term newborns undergoing clustered painful procedures has not been tested. Kangaroo Care (chest-to-chest, skin-to-skin position of infant on mother) effectively reduces pain of single procedures, but its effect on pain from clustered procedures is not known. Aim: The aim was to test Kangaroo Care’s effect on pain in one term infant who received clustered painful procedures while determining feasibility of the Kangaroo Care intervention. Design, Setting, and Participant: A case study design was used with one healthy term newborn who received two heel sticks and one injection in one session in the mother’s postpartum room. Method: Heart rate and oxygen saturation (recorded from Massimo Pulse Oximeter every 30 seconds), crying time (total seconds of crying on videotape) and behavioral state (using Anderson Behavioral State Scoring system every 30 seconds) were measured before (5 minutes), during (10.5 minutes) and after (30 minutes) the three clustered painful procedures in a newborn who was in Kangaroo Care during all observations. One staff nurse administered the clustered procedures. Results: Heart rate increased sequentially with each heelstick, oxygen saturation remained unchanged, sleep predominated, and crying was minimal throughout the procedures. Conclusion: Kangaroo Care appeared to reduce pain from clustered painful procedures and can be further tested.展开更多
As a promising edge learning framework in future 6G networks,federated learning(FL)faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors.Data imbalance is o...As a promising edge learning framework in future 6G networks,federated learning(FL)faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors.Data imbalance is one of these challenges that can significantly degrade the learning efficiency.To deal with data imbalance issue,this work proposes a new learning framework,called clustered federated learning with weighted model aggregation(weighted CFL).Compared with traditional FL,our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration,and then performs weighted per-cluster model aggregation.Therein,the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients.Moreover,the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate.Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.展开更多
Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide...Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.展开更多
American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired perso...American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.展开更多
As high-speed railway is booming worldwide, the communication system with fast-time varying channel has drawn great attention. The comb pilot based linear minimum mean square error （LMMSE） channel estimator is prove...As high-speed railway is booming worldwide, the communication system with fast-time varying channel has drawn great attention. The comb pilot based linear minimum mean square error （LMMSE） channel estimator is proved to be an effective method for fast time-varying channel estimation. In this paper, the clustered comb pilot-aided chan- nel estimation for orthogonal frequency-division multiplexing （OFDM） system is discussed, where the time varying channel is approximated by a basis expansion model （BEM）. A modified clustered comb pilot structure is proposed and justified to improve the estimation performance compared with the clustered comb pilot proposed by Tang. Based on the complex-exponential BEM （CE-BEM） model, a suboptimal-pilot structure is proposed. In addition, optimal pilot length is analyzed and simulated with a predefined total number of pilots. The simulation results show that the modi- fied clustered comb pilot can greatly reduce the estimation error especially with high Doppler spread. The suboptimal- pilot structure with guard pilot approximation is proven to be competitive. Optimal nonzero pilot lengths for different Doppler spread are obtained by simulation with a predefined channel order and fixed pilot subcarriers.展开更多
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Development Plan in Jilin Province of China(20230203135SF)+1 种基金National Natural Science Foundation of China(41875119)Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。
文摘Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.
基金supported by the Research Incentive Grant 23200 of Zayed University,United Arab Emirates.
文摘Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
基金This work was supported by the China National Key Research and Development Plan(No.2020YFB1807204).
文摘Recently,cell-free(CF)massive multipleinput multiple-output(MIMO)becomes a promising architecture for the next generation wireless communication system,where a large number of distributed access points(APs)are deployed to simultaneously serve multiple user equipments(UEs)for improved performance.Meanwhile,a clustered CF system is considered to tackle the backhaul overhead issue in the huge connection network.In this paper,taking into account the more realistic mobility scenarios,we propose a hybrid small-cell(SC)and clustered CF massive MIMO system through classifications of the UEs and APs,and constructing the corresponding pairs to run in SC or CF mode.A joint initial AP selection of this paradigm for all the UEs is firstly proposed,which is based on the statistics of estimated channel.Then,closed-form expressions of the downlink achievable rates for both the static and moving UEs are provided under Ricean fading channel and Doppler shift effect.We also develop a semi-heuristic search algorithm to deal with the AP selection for the moving UEs by maximizing the weight average achievable rate.Numerical results demonstrate the performance gains and effective rates balancing of the proposed system.
文摘Infrastructure as a Service(IaaS)in cloud computing enables flexible resource distribution over the Internet,but achieving optimal scheduling remains a challenge.Effective resource allocation in cloud-based environments,particularly within the IaaS model,poses persistent challenges.Existing methods often struggle with slow opti-mization,imbalanced workload distribution,and inefficient use of available assets.These limitations result in longer processing times,increased operational expenses,and inadequate resource deployment,particularly under fluctuating demands.To overcome these issues,a novel Clustered Input-Oriented Salp Swarm Algorithm(CIOSSA)is introduced.This approach combines two distinct strategies:Task Splitting Agglomerative Clustering(TSAC)with an Input Oriented Salp Swarm Algorithm(IOSSA),which prioritizes tasks based on urgency,and a refined multi-leader model that accelerates optimization processes,enhancing both speed and accuracy.By continuously assessing system capacity before task distribution,the model ensures that assets are deployed effectively and costs are controlled.The dual-leader technique expands the potential solution space,leading to substantial gains in processing speed,cost-effectiveness,asset efficiency,and system throughput,as demonstrated by comprehensive tests.As a result,the suggested model performs better than existing approaches in terms of makespan,resource utilisation,throughput,and convergence speed,demonstrating that CIOSSA is scalable,reliable,and appropriate for the dynamic settings found in cloud computing.
文摘Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.
文摘Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.
基金supported by Grants from the National Natural Science Foundation of China(31630039 and 31700666)the Ministry of Science and Technology of China(2017YFA0504203 and 2018YFC1004504)the Science and Technology Commission of Shanghai Municipality(19JC1412500)。
文摘There are more than a thousand trillion specific synaptic connections in the human brain and over a million new specific connections are formed every second during the early years of life. The assembly of these staggeringly complex neuronal circuits requires specific cell-surface molecular tags to endow each neuron with a unique identity code to discriminate self from non-self. The clustered protocadherin(Pcdh) genes, which encode a tremendous diversity of cell-surface assemblies, are candidates for neuronal identity tags. We describe the adaptive evolution,genomic structure, and regulation of expression of the clustered Pcdhs. We specifically focus on the emerging3-D architectural and biophysical mechanisms that generate an enormous number of diverse cell-surface Pcdhs as neural codes in the brain.
基金the Australia Coal Association Research Program(ACARP)(Grant Nos.C26006 and C26053)Supports from CSIRO。
文摘Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.
基金supported by the National Natural Science Foundation of China (No.11102018)
文摘This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.
基金Project supported by the Basic Scientific Research of National Defense(No.B2720133015)the Basic Research Fund of Northwestern Polytechnical University(No.3102014JCQ01045)
文摘Absorption of acoustic nanowave in specific frequency region is important for the design of acoustic filter. This paper puts forward a meta material model made up of fluid-conveying carbon nanotubes （CNT）, which can absorb acoustic nanowave in a given frequency range by adjusting the lengths and fluid velocities of themselves. Absorption coefficients are calculated out through the combination of the finite element method with the theoretical model, which are 0.4-0.55 relating to different fluid velocities for the crossing-distributed model. Comparisons are made between the crossed model and the aligned one, which prove that the CNT forest with crossed distribution is more effective in acoustic wave absorption.
文摘The scientific community is continuously working to translate the novel biomedical techniques into effective medical treatments.CRISPR-Cas9 system(Clustered Regularly Interspaced Short Palindromic Repeats-9),commonly known as the“molecular scissor”,represents a recently developed biotechnology able to improve the quality and the efficacy of traditional treatments,related to several human diseases,such as chronic diseases,neurodegenerative pathologies and,interestingly,oral diseases.Of course,dental medicine has notably increased the use of biotechnologies to ensure modern and conservative approaches:in this landscape,the use of CRISPR-Cas9 system may speed and personalize the traditional therapies,ensuring a good predictability of clinical results.The aim of this critical overview is to provide evidence on CRISPR efficacy,taking into specific account its applications in oral medicine.
基金Supported by the National Natural Science Foundation of China(No.61300180)Beijing Higher Education Young Elite Teacher Project(No.YETP1755)+1 种基金the Fundamental Research Funds for the Central Universities of China(No.TD2014-01)the Importation and Development of High-caliber Talents Project of Beijing Municipal Institutions(No.CIT&TCD201504039)
文摘Influenced by the environment and nodes status,the quality of link is not always stable in actual wireless sensor networks( WSNs). Poor links result in retransmissions and more energy consumption. So link quality is an important issue in the design of routing protocol which is not considered in most traditional clustered routing protocols. A based on energy and link quality's routing protocol( EQRP) is proposed to optimize the clustering mechanism which takes into account energy balance and link quality factors. EQRP takes the advantage of high quality links to increase success rate of single communication and reduce the cost of communication. Simulation shows that,compared with traditional clustered protocol,EQRP can perform 40% better,in terms of life cycle of the whole network.
文摘Background: Pain management for term newborns undergoing clustered painful procedures has not been tested. Kangaroo Care (chest-to-chest, skin-to-skin position of infant on mother) effectively reduces pain of single procedures, but its effect on pain from clustered procedures is not known. Aim: The aim was to test Kangaroo Care’s effect on pain in one term infant who received clustered painful procedures while determining feasibility of the Kangaroo Care intervention. Design, Setting, and Participant: A case study design was used with one healthy term newborn who received two heel sticks and one injection in one session in the mother’s postpartum room. Method: Heart rate and oxygen saturation (recorded from Massimo Pulse Oximeter every 30 seconds), crying time (total seconds of crying on videotape) and behavioral state (using Anderson Behavioral State Scoring system every 30 seconds) were measured before (5 minutes), during (10.5 minutes) and after (30 minutes) the three clustered painful procedures in a newborn who was in Kangaroo Care during all observations. One staff nurse administered the clustered procedures. Results: Heart rate increased sequentially with each heelstick, oxygen saturation remained unchanged, sleep predominated, and crying was minimal throughout the procedures. Conclusion: Kangaroo Care appeared to reduce pain from clustered painful procedures and can be further tested.
文摘As a promising edge learning framework in future 6G networks,federated learning(FL)faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors.Data imbalance is one of these challenges that can significantly degrade the learning efficiency.To deal with data imbalance issue,this work proposes a new learning framework,called clustered federated learning with weighted model aggregation(weighted CFL).Compared with traditional FL,our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration,and then performs weighted per-cluster model aggregation.Therein,the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients.Moreover,the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate.Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.
文摘Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.
基金This research was supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT and Future Planning(NRF-2019R1A2C1084308).
文摘American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.
文摘As high-speed railway is booming worldwide, the communication system with fast-time varying channel has drawn great attention. The comb pilot based linear minimum mean square error （LMMSE） channel estimator is proved to be an effective method for fast time-varying channel estimation. In this paper, the clustered comb pilot-aided chan- nel estimation for orthogonal frequency-division multiplexing （OFDM） system is discussed, where the time varying channel is approximated by a basis expansion model （BEM）. A modified clustered comb pilot structure is proposed and justified to improve the estimation performance compared with the clustered comb pilot proposed by Tang. Based on the complex-exponential BEM （CE-BEM） model, a suboptimal-pilot structure is proposed. In addition, optimal pilot length is analyzed and simulated with a predefined total number of pilots. The simulation results show that the modi- fied clustered comb pilot can greatly reduce the estimation error especially with high Doppler spread. The suboptimal- pilot structure with guard pilot approximation is proven to be competitive. Optimal nonzero pilot lengths for different Doppler spread are obtained by simulation with a predefined channel order and fixed pilot subcarriers.
