Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of ...Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of leisure-time physical activity with cardiovascular and all-cause mortality across occupational physical activity levels.Methods This study utilized individual participant data from 21 cohort studies,comprising both published and unpublished data.Eligibility criteria included individual-level data on leisure-time and occupational physical activity(categorized as sedentary,low,moderate,and high)along with data on all-cause and/or cardiovascular mortality.A 2-stage individual participant data meta-analysis was conducted,with separate analysis of each study using Cox proportional hazards models(Stage 1).These results were combined using random-effects models(Stage 2).Results Higher leisure-time physical activity levels were associated with lower all-cause and cardiovascular mortality risk across most occupational physical activity levels,for both males and females.Among males with sedentary work,high compared to sedentary leisure-time physical activity was associated with lower all-cause(hazard ratios(HR)=0.77,95%confidence interval(95%CI):0.70-0.85)and cardiovascular mortality(HR=0.76,95%CI:0.66-0.87)risk.Among males with high levels of occupational physical activity,high compared to sedentary leisure-time physical activity was associated with lower all-cause(HR=0.84,95%CI:0.74-0.97)and cardiovascular mortality(HR=0.79,95%CI:0.60-1.04)risk,while HRs for low and moderate levels of leisure-time physical activity ranged between 0.87 and 0.97 and were not statistically significant.Among females,most effects were similar but more imprecise,especially in the higher occupational physical activity levels.Conclusion Higher levels of leisure-time physical activity were generally associated with lower mortality risks.However,results for workers with moderate and high occupational physical activity levels,especially women,were more imprecise.Our findings suggests that workers may benefit from engaging in high levels of leisure-time physical activity,irrespective of their level of occupational physical activity.展开更多
Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accur...Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accuracy about active tectonics in the research area and by ordering, some questions in the results are also discussed. It shows that the most dangerous fault segments for strong earthquakes in the future include: segments B and A of the southern boundary fault of the Yangyuan basin, the southern boundary fault of the Xuanhua basin, the east segment of the southern Huaian fault and the east segment of the northern YanggaoTianzhen fault. The most dangerous area is YangyuanShenjing basin, the second one is TianzhenHuaianXuanhua basin and the third dangerous areas are WanquanZhangjiakou and northeast of Yuxian to southwest of Fanshan.展开更多
文章研究了使用Oracle Active Data Guard实现PMS2.0数据库读写分离。生产管理系统PMS是电力信息系统中重要的核心业务系统,随着数据量不断快速增长,数据库的I/O吞吐量以及资源的耗用愈发凸显性能瓶颈,造成业务用户并发操作延时甚至失...文章研究了使用Oracle Active Data Guard实现PMS2.0数据库读写分离。生产管理系统PMS是电力信息系统中重要的核心业务系统,随着数据量不断快速增长,数据库的I/O吞吐量以及资源的耗用愈发凸显性能瓶颈,造成业务用户并发操作延时甚至失败。传统的单节点数据库或ORACLE RAC集群已无法应对。根据目前成熟的业界解决方案,采用读写分离的方式,可以有效解决这个问题。展开更多
The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained ...The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.展开更多
This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ...This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.展开更多
By moving computations from computing nodes to storage nodes, active storage technology provides an efficient for data-intensive high-performance computing applications. The existing studies have neglected the heterog...By moving computations from computing nodes to storage nodes, active storage technology provides an efficient for data-intensive high-performance computing applications. The existing studies have neglected the heterogeneity of storage nodes on the performance of active storage systems. We introduce CADP, a capability-aware data placement scheme for heterogeneous active storage systems to obtain high-performance data processing. The basic idea of CADP is to place data on storage nodes based on their computing capability and storage capability, so that the load-imbalance among heterogeneous servers can be avoided. We have implemented CADP under a parallel I/O system. The experimental results show that the proposed capability-aware data placement scheme can improve the active storage system performance significantly.展开更多
Extractive agents of extractive distillation separation for mixtures of dichlorobenzene were analyzed and compared, gas-liquid equilibrium data （VLE data） was measured for dichlorobenzene and diphenylamine, the appr...Extractive agents of extractive distillation separation for mixtures of dichlorobenzene were analyzed and compared, gas-liquid equilibrium data （VLE data） was measured for dichlorobenzene and diphenylamine, the appropriate extractive agent was selected by relatively volatility, the temperature was studied on the effect of extractive separation. VLE data was measured for dichlorobenzene, the parameters were simulated in Wilson equation. The infinite dilute activity coefficient of dichlorobenzene in diphenylamine were measured by chromatogram apparatus, the model parameters were correlated by the single parameter method for dichlorobenzene and diphenyl -amine, VLE data of m-dichlorobenzene-p- dichlorobenzene -o-dichlorobenzene-diphenylamine system was measured and calculated by six part model parameters. The results of correlation and experiment were provided a basis for study of extractive distillation simulation and experiment in this work.展开更多
The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defe...The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.展开更多
We have collected an up-to-date sample of 123 superluminal sources （84 quasars, 27 BL Lac objects and 12 galaxies） and calculated the apparent velocities （βapp） for 224 components in the sources with the A-CDM mo...We have collected an up-to-date sample of 123 superluminal sources （84 quasars, 27 BL Lac objects and 12 galaxies） and calculated the apparent velocities （βapp） for 224 components in the sources with the A-CDM model. We checked the relationships between their proper motions, redshifts,βapp and 5 GHz flux densities. Our analysis shows that the radio emission is strongly boosted by the Doppler effect. The superluminal motion and the relativistic beaming boosting effect are, to some extent, the same in active galactic nuclei.展开更多
A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years...A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.展开更多
In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training perfo...In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training performance of Sigmoid, ReLu, Leaky-ReLu and L & exp. activity functions for few inputs to multiple output training patterns. Our MLNNs model has L hidden layers with two or three inputs to four or six outputs data variations by BP (backpropagation) NN (neural network) training. We focused on the multi teacher training signals to investigate and evaluate the training performance in MLNNs to select the best and good activity function for data enlargement and hence could be applicable for image and signal processing (synaptic divergence) along with the proposed methods with convolution networks. We specifically used four activity functions from which we found out that L & exp. activity function can suite DENN (data enlargement neural network) training since it could give the highest percentage training abilities compared to the other activity functions of Sigmoid, ReLu and Leaky-ReLu during simulation and training of data in the network. And finally, we recommend L & exp. function to be good for MLNNs and may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple teacher training patterns using original generated data and hence can be tried with CNN (convolution neural networks) of image processing.展开更多
The relationship between the earth stress field, earth surface displacement field and the gravity variation is deduced. Algorithms based on the boundary element method to compute the earth stress variation using the e...The relationship between the earth stress field, earth surface displacement field and the gravity variation is deduced. Algorithms based on the boundary element method to compute the earth stress variation using the earth surface displacement is discussed. The stress field variation in Jiashi region, Xinjiang, China is obtained from the GPS data observed in 1997 and 1998, respectively, and the relationship among the local stress field variation, seismic activities and fault tectonic activities is discussed.展开更多
基金The Trùndelag Health Study (HUNT) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology), Trùndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public HealthThe coordination of European Prospective Investigation into Cancer and Nutrition - Spain study (EPIC) is financially supported by the International Agency for Research on Cancer (IARC)+7 种基金by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC)supported by Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andaluc 1a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain)funded by The Netherlands Organisation for Health Research and DevelopmentZon Mw (Grant No.: 531-00141-3)Funding for the SHIP study has been provided by the Federal Ministry for Education and Research (BMBFidentification codes 01 ZZ96030, 01 ZZ0103, and 01 ZZ0701)support from the Swedish Research Council (2018-02527 and 2019-00193)financed by the Helmholtz Zentrum München - German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria.
文摘Background There is insufficient evidence to provide recommendations for leisure-time physical activity among workers across various occupational physical activity levels.This study aimed to assess the association of leisure-time physical activity with cardiovascular and all-cause mortality across occupational physical activity levels.Methods This study utilized individual participant data from 21 cohort studies,comprising both published and unpublished data.Eligibility criteria included individual-level data on leisure-time and occupational physical activity(categorized as sedentary,low,moderate,and high)along with data on all-cause and/or cardiovascular mortality.A 2-stage individual participant data meta-analysis was conducted,with separate analysis of each study using Cox proportional hazards models(Stage 1).These results were combined using random-effects models(Stage 2).Results Higher leisure-time physical activity levels were associated with lower all-cause and cardiovascular mortality risk across most occupational physical activity levels,for both males and females.Among males with sedentary work,high compared to sedentary leisure-time physical activity was associated with lower all-cause(hazard ratios(HR)=0.77,95%confidence interval(95%CI):0.70-0.85)and cardiovascular mortality(HR=0.76,95%CI:0.66-0.87)risk.Among males with high levels of occupational physical activity,high compared to sedentary leisure-time physical activity was associated with lower all-cause(HR=0.84,95%CI:0.74-0.97)and cardiovascular mortality(HR=0.79,95%CI:0.60-1.04)risk,while HRs for low and moderate levels of leisure-time physical activity ranged between 0.87 and 0.97 and were not statistically significant.Among females,most effects were similar but more imprecise,especially in the higher occupational physical activity levels.Conclusion Higher levels of leisure-time physical activity were generally associated with lower mortality risks.However,results for workers with moderate and high occupational physical activity levels,especially women,were more imprecise.Our findings suggests that workers may benefit from engaging in high levels of leisure-time physical activity,irrespective of their level of occupational physical activity.
基金National major basic-theory planning project Mechanism and Prediction of Strong Earthquake (95130105) and the Key Project from China Seismological Bureau (95040803).
文摘Deterministic, probabilistic and composite-grading methods are used to get the possible locations of strong earth-quakes in the future in Norwest Beijing and its vicinity based on the quantitative data and their accuracy about active tectonics in the research area and by ordering, some questions in the results are also discussed. It shows that the most dangerous fault segments for strong earthquakes in the future include: segments B and A of the southern boundary fault of the Yangyuan basin, the southern boundary fault of the Xuanhua basin, the east segment of the southern Huaian fault and the east segment of the northern YanggaoTianzhen fault. The most dangerous area is YangyuanShenjing basin, the second one is TianzhenHuaianXuanhua basin and the third dangerous areas are WanquanZhangjiakou and northeast of Yuxian to southwest of Fanshan.
文摘文章研究了使用Oracle Active Data Guard实现PMS2.0数据库读写分离。生产管理系统PMS是电力信息系统中重要的核心业务系统,随着数据量不断快速增长,数据库的I/O吞吐量以及资源的耗用愈发凸显性能瓶颈,造成业务用户并发操作延时甚至失败。传统的单节点数据库或ORACLE RAC集群已无法应对。根据目前成熟的业界解决方案,采用读写分离的方式,可以有效解决这个问题。
文摘The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.
基金supported by the Royal Golden Jubilee(RGJ)Ph.D.Programme(Grant No.PHD/0079/2561)through the National Research Council of Thailand(NRCT)and Thailand Research Fund(TRF).
文摘This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.
基金Supported by the National Science and Technology Foundation of China(61572377)the Natural Science Foundation of Hubei Province(2014CFB239)+2 种基金the Open Fund from HPCL(201512-02)the Open Fund from SKLSE(2015-A-06)the US National Science Foundation(CNS-1162540)
文摘By moving computations from computing nodes to storage nodes, active storage technology provides an efficient for data-intensive high-performance computing applications. The existing studies have neglected the heterogeneity of storage nodes on the performance of active storage systems. We introduce CADP, a capability-aware data placement scheme for heterogeneous active storage systems to obtain high-performance data processing. The basic idea of CADP is to place data on storage nodes based on their computing capability and storage capability, so that the load-imbalance among heterogeneous servers can be avoided. We have implemented CADP under a parallel I/O system. The experimental results show that the proposed capability-aware data placement scheme can improve the active storage system performance significantly.
文摘Extractive agents of extractive distillation separation for mixtures of dichlorobenzene were analyzed and compared, gas-liquid equilibrium data （VLE data） was measured for dichlorobenzene and diphenylamine, the appropriate extractive agent was selected by relatively volatility, the temperature was studied on the effect of extractive separation. VLE data was measured for dichlorobenzene, the parameters were simulated in Wilson equation. The infinite dilute activity coefficient of dichlorobenzene in diphenylamine were measured by chromatogram apparatus, the model parameters were correlated by the single parameter method for dichlorobenzene and diphenyl -amine, VLE data of m-dichlorobenzene-p- dichlorobenzene -o-dichlorobenzene-diphenylamine system was measured and calculated by six part model parameters. The results of correlation and experiment were provided a basis for study of extractive distillation simulation and experiment in this work.
基金supported by the National Nature Science Foundation of China(Nos.62103357,62203376)the Science and Technology Plan of Hebei Education Department(No.QN2021139)+1 种基金the Nature Science Foundation of Hebei Province(Nos.F2021203043,F2022203074)the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network,Nanjing Institute of Technology(No.XTCX202203).
文摘The emerging of false data injection attacks(FDIAs)can fool the traditional detection methods by injecting false data,which has brought huge risks to the security of smart grids.For this reason,a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids.The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds.First,a dynamic physical grid model under FDIAs is modeled,in which model uncertainty and parameter uncertainty are taken into account.Then,an interval observer-based detection method against FDIAs is proposed,where a detection criteria using interval residual is put forward.Corresponding to the detection results,the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs.Linear matrix inequality(LMI)approach is applied to design the resilient controller with H_(∞)performance.The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin.Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs.The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.
基金the NSFC(Grants 10573005 and 10633010)the 973 project(2007CB815405)
文摘We have collected an up-to-date sample of 123 superluminal sources （84 quasars, 27 BL Lac objects and 12 galaxies） and calculated the apparent velocities （βapp） for 224 components in the sources with the A-CDM model. We checked the relationships between their proper motions, redshifts,βapp and 5 GHz flux densities. Our analysis shows that the radio emission is strongly boosted by the Doppler effect. The superluminal motion and the relativistic beaming boosting effect are, to some extent, the same in active galactic nuclei.
基金supported by the National Basic Research Program of China(No.2007CB407303)
文摘A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.
文摘In this paper, we present a study on activity functions for an MLNN (multi-layered neural network) and propose a suitable activity function for data enlargement processing. We have carefully studied the training performance of Sigmoid, ReLu, Leaky-ReLu and L & exp. activity functions for few inputs to multiple output training patterns. Our MLNNs model has L hidden layers with two or three inputs to four or six outputs data variations by BP (backpropagation) NN (neural network) training. We focused on the multi teacher training signals to investigate and evaluate the training performance in MLNNs to select the best and good activity function for data enlargement and hence could be applicable for image and signal processing (synaptic divergence) along with the proposed methods with convolution networks. We specifically used four activity functions from which we found out that L & exp. activity function can suite DENN (data enlargement neural network) training since it could give the highest percentage training abilities compared to the other activity functions of Sigmoid, ReLu and Leaky-ReLu during simulation and training of data in the network. And finally, we recommend L & exp. function to be good for MLNNs and may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple teacher training patterns using original generated data and hence can be tried with CNN (convolution neural networks) of image processing.
基金State Natural Science Foundation of China !(49774214)the State Key Project !(96-913-07).
文摘The relationship between the earth stress field, earth surface displacement field and the gravity variation is deduced. Algorithms based on the boundary element method to compute the earth stress variation using the earth surface displacement is discussed. The stress field variation in Jiashi region, Xinjiang, China is obtained from the GPS data observed in 1997 and 1998, respectively, and the relationship among the local stress field variation, seismic activities and fault tectonic activities is discussed.
