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This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise. [ABSTRACT FROM AUTHOR]

In the field of target tracking, probability multiple hypothesis tracking (PMHT) algorithm, as a batch processing algorithm, has much less computation than the traditional multiple hypothesis tracking algorithm. Currently, the application of PMHT algorithm is limited by centralized processing. On the basis of the traditional algorithm, this study firstly derives the algorithm likelihood under sensor network to obtain the post-correlation parameter under multi-sensor algorithm, followed by hybrid consensus based on the consensus processing strategy, and finally the posteriori estimation of the target parameters is accomplished by using Kalman filtering. This study enables the PMHT algorithm to be applied to the fully distributed sensor network without fusion centers. The experimental results show that under different clutter densities, the distributed PMHT has more than 90% improvement in tracking error compared to the single-sensor algorithm. Distributed PMHT has close tracking performance and faster computation compared to centralized algorithms. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to improve cancer-related fatigue (CRF) and health outcomes of colorectal cancer patients by understanding the status quo of CRF, exploring the relations of coping, anxiety symptoms, depressive symptoms, body image perception and CRF, and also identifying the factors affecting CRF based on a generalized linear modeling approach. Patients and Methods: An exploratory cross-sectional study was conducted on 370 colorectal cancer patients at two hospitals in Anhui Province, China, from July 2020 to February 2021. The data were collected by using general information questionnaire, cancer fatigue scale, simplified coping style questionnaire, generalized anxiety disorder-7 scale, patient health questionnaire-9, and body image scale. Descriptive statistics, t-tests, one-way analysis of variance, Pearson correlation analyses, and generalized linear model analyses were applied to analyze the data. Results: The average CRF score of the patients was 21.612 (SD=6.160), with a prevalence rate of 69.4% for clinically relevant fatigue. The generalized linear model revealed that: In step 1, gender (female) (B=1.799, Waldχ2=7.506, p=0.006), per capita monthly income (1001– 3000 RMB) (B=− 1.673, Waldχ2=5.536, p=0.019) and treatment modalities (chemotherapy+others) (B=2.425, Waldχ2=8.211, p=0.004) were related to CRF. In step 2, depressive symptoms (B=1.223, Waldχ2=129.019, p< 0.001) and negative coping strategies (B=0.215, Waldχ2=11.347, p=0.001) exhibited significant positive correlations with CRF, positive coping strategies (B=− 0.319, Waldχ2=59.175, p< 0.001) showed significant negative correlations with CRF; While anxiety symptoms (B=0.162, Waldχ2=1.840, p=0.175) and body image perception (B=0.013, Waldχ2=0.048, p=0.826) had no correlations with CRF. Conclusion: The prevalence of CRF was relatively high among colorectal cancer patients. Coping and depressive symptoms were the modifiable influencing factors of CRF. Tailored interventions dedicated to promoting positive coping behavior, diminishing negative coping behavior and reducing depressive symptoms may improve the CRF of patients with colorectal cancer. Healthcare providers working with these patients should receive corresponding education and training in these complementary treatments. Additionally, when developing non-pharmacological interventions, appropriate consideration of the patients' gender, income condition and the type of anticancer treatment is also necessary. [ABSTRACT FROM AUTHOR]