Independent and joint association of physical activity and sedentary behavior on all-cause mortality

Backgrounds:Physical activity(PA)and sedentary behavior(SB)have been associated with mortality,while the joint association with mortality is rarely reported among Chinese population.We aimed to examine the independent and joint association of PA and SB with all-cause mortality in southern China.Methods:A cohort of 12,608 China Hypertension Survey participants aged≥35 years were enrolled in 2013 to 2014,with a follow-up period of 5.4 years.Baseline self-reported PA and SB were collected via the questionnaire.Kaplan–Meier curves(log-rank test)and Cox proportional hazards regression were performed to evaluate the associations of PA and SB on all-cause mortality.Results:A total of 11,744 eligible participants were included in the analysis.Over an average of 5.4 years of follow-up,796 deaths occurred.The risk of all-cause mortality was lower among participants with high PA than those with low to moderate level(5.2%vs.8.9%;hazards ratio[HR]:0.75,95%confidence interval[CI]:0.61–0.87).Participants with SB≥6 h had a higher risk of all-cause mortality than those with SB<6 h(7.8%vs.6.0%;HR:1.37,95%CI:1.17–1.61).Participants with prolonged SB(≥6 h)and inadequate PA(low to moderate)had a higher risk of all-cause mortality compared to those with SB<6 h and high PA(11.2%vs.4.9%;HR:1.67,95%CI:1.35–2.06).Even in the participants with high PA,prolonged SB(≥6 h)was still associated with the higher risk of all-cause mortality compared with SB<6 h(7.0%vs.4.9%;HR:1.33,95%CI:1.12–1.56).Conclusions:Among Chinese population,PA and SB have a joint association with the risk of all-cause mortality.Participants with inadequate PA and prolonged SB had the highest risk of all-cause mortality compared with others.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

Call for Papers International Symposium on Toxicology Jointly Sponsored by the China Preventive Medical Association and the Chinese Pharmacological Society

The scientific program will consist of symposia and poster sessions. Topics related to theoretical and applied research in the domain of toxicology and toxicological studies on chemicals of public concern will be welcome. The presenter’s name, address, and telephone and FAX numbers should be submitted along with the title of the presentation and whether it is oral or poster. Deadline: April 15, 1990. Papers should be submitted to:

Data association based on target signal classification information

In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.

Vessel fusion tracking with a dual-frequency high-frequency surface wave radar and calibrated by an automatic identification system

High-frequency surface wave radar（HFSWR） and automatic identification system（AIS） are the two most important sensors used for vessel tracking.The HFSWR can be applied to tracking all vessels in a detection area,while the AIS is usually used to verify the information of cooperative vessels.Because of interference from sea clutter,employing single-frequency HFSWR for vessel tracking may obscure vessels located in the blind zones of Bragg peaks.Analyzing changes in the detection frequencies constitutes an effective method for addressing this deficiency.A solution consisting of vessel fusion tracking is proposed using dual-frequency HFSWR data calibrated by the AIS.Since different systematic biases exist between HFSWR frequency measurements and AIS measurements,AIS information is used to estimate and correct the HFSWR systematic biases at each frequency.First,AIS point measurements for cooperative vessels are associated with the HFSWR measurements using a JVC assignment algorithm.From the association results of the cooperative vessels,the systematic biases in the dualfrequency HFSWR data are estimated and corrected.Then,based on the corrected dual-frequency HFSWR data,the vessels are tracked using a dual-frequency fusion joint probabilistic data association（JPDA）-unscented Kalman filter（UKF） algorithm.Experimental results using real-life detection data show that the proposed method is efficient at tracking vessels in real time and can improve the tracking capability and accuracy compared with tracking processes involving single-frequency data.

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time,monitor accurately the disease dynamics of live pigs,and reduce the morbidity and mortality of pigs in the existing large-scale farming model,pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs.However,it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets.In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig,this study proposed a method that used color feature,target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm,which based on joint probability data association and particle filter.Experimental results show the proposed algorithm can quickly and accurately track pigs in the video,and it is able to cope with partial occlusions and recover the tracks after temporary loss.

Group tracking algorithm for split maneuvering based on complex domain topological descriptions

A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group target, split models of group targets are established based on a sliding window feedback mechanism to determine the occurrence and classification of split maneuvering, which makes the tracked objects focus by group members effectively. The track of an outlier single target is reconstructed by the sequential least square method. At the same time, the relationship between the group members is expressed by the complex domain topological description method, which solves the problem of point-track association between the members. The Singer method is then used to update the tracks. Compared with classical multi-target tracking algorithms based on Multiple Hypothesis Tracking （MHT） and the Different Structure Joint Probabilistic Data Association （DS-JPDA） algorithm, the proposed algorithm has better tracking accuracy and stability, is robust against environmental clutter and has stable time-consumption under both classical radar conditions and partly resolvable conditions.