Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ofIran.Methods:The included...Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ofIran.Methods:The included cases were composed of the suspectedpertussis patients who came from Mazandaran province andregistered in the Center for Disease Control and Prevention from20 March 2012 to 20 March 2018.A discrete wavelet transformbasedmethod was used to detect the aberrations.All analyseswere performed using MATLAB Software version 2018a andExcel 2010.Results:A total of 1162 cases were recruited in the study,including 545(46.90%)males and 617(53.10%)females,withmedian age of 1.47(0.22-9.56)years.The median age of maleswas 1.18(0.21-8.24)years,while that of females was 1.82(0.21-10.75)years.Concerning the level of the alarm threshold,it was1.28 case/d when k=2,while it was 1.34 case/d when k=3.Thetotal detected aberration days were 123 d and 57 d by consideringk=2 and 3,respectively.The most defined alarm threshold wasrelated to spring(>2 cases/d)and summer(>1 case/d),respectively.Conclusions:The sensitivity of the surveillance system issubjected to a different time.Thus,determining the level of alarmthreshold periodically using different methods is recommended.展开更多
In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scal...In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.展开更多
Ten years ago,three teams experimentally demonstrated the first spasers,or plasmonic nanolasers,after the spaser concept was first proposed theoretically in 2003.An overview of the significant progress achieved over t...Ten years ago,three teams experimentally demonstrated the first spasers,or plasmonic nanolasers,after the spaser concept was first proposed theoretically in 2003.An overview of the significant progress achieved over the last 10 years is presented here,together with the original context of and motivations for this research.After a general introduction,we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers.This is followed by an overview of crucial technological progress,including lasing threshold reduction,dynamic modulation,room-temperature operation,electrical injection,the control and improvement of spasers,the array operation of spasers,and selected applications of single-particle spasers.Research prospects are presented in relation to several directions of development,including further miniaturization,the relationship with Bose-Einstein condensation,novel spaser-based interconnects,and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.展开更多
Under the acceptable hypothesis that cardiac rhythm is an approximately deterministic process with a small scale noise component, an available way is provided to construct a model that can reflect its prominent dynami...Under the acceptable hypothesis that cardiac rhythm is an approximately deterministic process with a small scale noise component, an available way is provided to construct a model that can reflect its prominent dynamics of the deterministic component. When applied to the analysis of 19 heart rate data sets, three main findings are stated. The obtained model can reflect prominent dynamics of the deterministic component of cardiac rhythm; cardiac chaos is stated in a reliable way; dynamical noise plays an important role in the generation of complex cardiac rhythm.展开更多
Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while p...Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while preserving data privacy.This paper introduces one of the first FL frameworks that efficiently integrates swarm intelligence-based aggregation methods to large-scale energy consumption forecasting,by extending the TensorFlow Federated Core framework with specialized functional enhancements.The primary objective is to enhance forecasting accuracy in decentralized learning settings.We investigated the effectiveness of various nature-inspired metaheuristics for optimizing the aggregation of local model updates from distributed energy resource nodes into a global model for load forecasting tasks,including Grey Wolf Optimization(GWO),Particle Swarm Optimization(PSO),and Firefly Algorithm(FFA)against the standard Federated Averaging(FedAvg)algorithm.Using a real-world dataset comprising of 4,438 distinct energy consumers,we demonstrate that metaheuristic aggregators consistently outperform the most well-known method,Federated Averaging in predictive accuracy.Among these approaches,GWO emerges as the superior performer achieving up to 23.6%error reduction.Our findings underscore the significant potential of metaheuristic-based aggregation mechanisms in improving FL outcomes,particularly in energy forecasting applications involving large-scale distributed data scenarios.展开更多
Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models ...Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models still struggle to capture nonlinear relationships influenced by external factors like weather and consumer behavior,assume constant variance in energy data over time,and often fail to model sequential data.To address these limitations,we propose a hybrid Transformer-based model with Liquid Neural Networks and learnable encodings for building energy forecasting.The model leverages Dense Layers to learn non-linear mappings to create embeddings that capture underlying patterns in time series energy data.Additionally,a Convolutional Neural Network encoder is integrated to enhance the model's ability to understand temporal dynamics through spatial mappings.To address the limitations of classic attention mechanisms,we implement a reservoir processing module using Liquid Neural Networks which introduces a controlled non-linearity through dynamic reservoir computing,enabling the model to capture complex patterns in the data.For model evaluation,we utilized both pilot data and state-of-the-art datasets to determine the model's performance across various building contexts,including large apartment and commercial buildings and small households,with and without on-site energy production.The proposed transformer model demonstrates good predictive accuracy and training time efficiency across various types of buildings and testing configurations.Specifically,SMAPE scores indicate a reduction in prediction error,with improvements ranging from 1.5%to 50%over basic transformer,LSTM and ANN models while the higher R²values further confirm the model's reliability in capturing energy time series variance.The 8%improvement in training time over the basic transformer model,highlights the hybrid model computational efficiency without compromising accuracy.展开更多
The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration ob...The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high variance.As computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP error.This approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the GP.This approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.展开更多
基金The authors would like to express their appreciation for the Iranian Ministry of Health and Center for Communicable Diseases Control for their constant support and collaboration.This article was extracted from the Ph.D.thesis by Yousef Alimohamadi and financially supported by Tehran University of Medical Sciences.
文摘Objective:To define the level of alarm threshold for pertussisaberrations and to detect the aberrations of the reported suspectedcases of pertussis from the Mazandaran province in the north ofIran.Methods:The included cases were composed of the suspectedpertussis patients who came from Mazandaran province andregistered in the Center for Disease Control and Prevention from20 March 2012 to 20 March 2018.A discrete wavelet transformbasedmethod was used to detect the aberrations.All analyseswere performed using MATLAB Software version 2018a andExcel 2010.Results:A total of 1162 cases were recruited in the study,including 545(46.90%)males and 617(53.10%)females,withmedian age of 1.47(0.22-9.56)years.The median age of maleswas 1.18(0.21-8.24)years,while that of females was 1.82(0.21-10.75)years.Concerning the level of the alarm threshold,it was1.28 case/d when k=2,while it was 1.34 case/d when k=3.Thetotal detected aberration days were 123 d and 57 d by consideringk=2 and 3,respectively.The most defined alarm threshold wasrelated to spring(>2 cases/d)and summer(>1 case/d),respectively.Conclusions:The sensitivity of the surveillance system issubjected to a different time.Thus,determining the level of alarmthreshold periodically using different methods is recommended.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.
基金financial support from the DARPA/DSO Extreme Optics and Imaging(EXTREME)Program(Award HR00111720032)financial support from AFOSR Grant FA9550-18-1-0002+8 种基金supported by the National Natural Science Foundation of China(Grant Nos.91950115,11774014,and 61521004)the Beijing Natural Science Foundation(Grant No.Z180011)the National Key R&D Program of China(Grant No.2018YFA0704401)supported by the“UK Engineering and Physical Sciences Research Council”support from the Beijing Innovation Centre for Future Chips at Tsinghua Universityprovided by Grant No.DE-SC0007043 from the Materials Sciences and Engineering Division of the Office of the Basic Energy Sciences,Office of Science,U.S.Department of Energyperformed using support from Grant No.DE-FG02-01ER15213 from the Chemical Sciences,Biosciences and Geosciences Division,Office of Basic Energy Sciences,Office of Science,US Department of EnergyAdditional support for MIS came from NSF EFRI NewLAW Grant EFMA-1741691MURI Grant No.N00014-17-1-2588 from the Office of Naval Research(ONR).
文摘Ten years ago,three teams experimentally demonstrated the first spasers,or plasmonic nanolasers,after the spaser concept was first proposed theoretically in 2003.An overview of the significant progress achieved over the last 10 years is presented here,together with the original context of and motivations for this research.After a general introduction,we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers.This is followed by an overview of crucial technological progress,including lasing threshold reduction,dynamic modulation,room-temperature operation,electrical injection,the control and improvement of spasers,the array operation of spasers,and selected applications of single-particle spasers.Research prospects are presented in relation to several directions of development,including further miniaturization,the relationship with Bose-Einstein condensation,novel spaser-based interconnects,and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. 69735101 and 69872009).
文摘Under the acceptable hypothesis that cardiac rhythm is an approximately deterministic process with a small scale noise component, an available way is provided to construct a model that can reflect its prominent dynamics of the deterministic component. When applied to the analysis of 19 heart rate data sets, three main findings are stated. The obtained model can reflect prominent dynamics of the deterministic component of cardiac rhythm; cardiac chaos is stated in a reliable way; dynamical noise plays an important role in the generation of complex cardiac rhythm.
基金the framework of the Horizon Europe European Commission project DEDALUS(Grant Agreement No.101103998).
文摘Federated learning(FL)is essential to energy transition as it leverages decentralized energy data and machine learning to collaborative train global energy predictive models across distributed energy resources while preserving data privacy.This paper introduces one of the first FL frameworks that efficiently integrates swarm intelligence-based aggregation methods to large-scale energy consumption forecasting,by extending the TensorFlow Federated Core framework with specialized functional enhancements.The primary objective is to enhance forecasting accuracy in decentralized learning settings.We investigated the effectiveness of various nature-inspired metaheuristics for optimizing the aggregation of local model updates from distributed energy resource nodes into a global model for load forecasting tasks,including Grey Wolf Optimization(GWO),Particle Swarm Optimization(PSO),and Firefly Algorithm(FFA)against the standard Federated Averaging(FedAvg)algorithm.Using a real-world dataset comprising of 4,438 distinct energy consumers,we demonstrate that metaheuristic aggregators consistently outperform the most well-known method,Federated Averaging in predictive accuracy.Among these approaches,GWO emerges as the superior performer achieving up to 23.6%error reduction.Our findings underscore the significant potential of metaheuristic-based aggregation mechanisms in improving FL outcomes,particularly in energy forecasting applications involving large-scale distributed data scenarios.
基金the DEDALUS project grant number 101103998 funded by the European Commission as part of the Horizon Europe Framework Programme and within Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number PN-IV-P8-8.1-PRE-HE-ORG-2023-0111,within PNCDI IV.
文摘Accurate forecasting of buildings'energy demand is essential for building operators to manage loads and resources efficiently,and for grid operators to balance local production with demand.However,nowadays models still struggle to capture nonlinear relationships influenced by external factors like weather and consumer behavior,assume constant variance in energy data over time,and often fail to model sequential data.To address these limitations,we propose a hybrid Transformer-based model with Liquid Neural Networks and learnable encodings for building energy forecasting.The model leverages Dense Layers to learn non-linear mappings to create embeddings that capture underlying patterns in time series energy data.Additionally,a Convolutional Neural Network encoder is integrated to enhance the model's ability to understand temporal dynamics through spatial mappings.To address the limitations of classic attention mechanisms,we implement a reservoir processing module using Liquid Neural Networks which introduces a controlled non-linearity through dynamic reservoir computing,enabling the model to capture complex patterns in the data.For model evaluation,we utilized both pilot data and state-of-the-art datasets to determine the model's performance across various building contexts,including large apartment and commercial buildings and small households,with and without on-site energy production.The proposed transformer model demonstrates good predictive accuracy and training time efficiency across various types of buildings and testing configurations.Specifically,SMAPE scores indicate a reduction in prediction error,with improvements ranging from 1.5%to 50%over basic transformer,LSTM and ANN models while the higher R²values further confirm the model's reliability in capturing energy time series variance.The 8%improvement in training time over the basic transformer model,highlights the hybrid model computational efficiency without compromising accuracy.
文摘The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high variance.As computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP error.This approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the GP.This approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.
