Stochastic and State Space Models of Carcinogenesis Under Complex Situation

With more and more biological mechanisms of cancer development being discovered, in order to improve cancer control and prevention, it becomes necessary to develop effective and efficient mathematical and statistical models and methods to incorporate the biological information, and to identify critical events in the process of carcinogenesis. In this dissertation, the complex nature of carcinogenesis has been represented by stochastic system model; combining this model with information from observations and prior knowledge, we have developed state space models to evaluate cancer gene mutations and cell proliferation at different cancer development stages. Also, we have proposed a generalized Bayesian method via multi-level Gibbs sampling procedure to predict state (stage) variables of the models. In this dissertation, stochastic models have been proposed for initiation, promotion and complete carcinomas experiments; these experiments are most commonly performed in cancer risk assessment of environmental agents. These stochastic models are simple multi-pathway models which are constructed based on biological mechanisms. The estimates we obtained from the models have provided quantitative evaluation of dose related mutation rates of major genes and cells proliferation rates; these results could be used to assess the risk of developing malignant tumor in the environment we live. More complicated stochastic and state space models have been developed for sporadic human colon cancer and for hereditary and non-hereditary human liver cancer. We have utilized the proposed models to fit to Surveillance Epidemiology and End Results (SEER) data. The results imply that our models have effectively incorporated biological information and observations; these models fitted the data very well and the inferences based on estimate were very consistent with biological findings. Furthermore, the models reflected the complex nature of carcinogenesis. We notice that many cancers are developed through multiple-stage multiple-pathway. Our analyses of colon cancer and liver cancer have showed that some pathways are more devastated than others. This suggests thus it would be more efficient to intervene or treat the critical events in the more devastated pathways

BIOMECHANICAL ANALYSIS OF THE PADDLING TECHNIQUE AND THE VELOCITY OF 1000M FULL PADDLING EVENT: A CASE STUDY.

Biomechanical analysis from data obtained by video camera was used to investigate the paddling technique and the velocity of 1000m full paddling event. The results showed the characteristics and the advantages of Meng’s paddling technique. The data also revealed deficiencies and provided a set of kinematics parameters for evaluation, diagnosis and training of paddling techniques

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

Supersonic ejectors are of great interest for various industries as they can improve the quality of the low-grade heat source in an eco-friendly and sustainable way. However, the impact of steam condensation on the supersonic ejector performances is not fully understood and is usually neglected by using the dry gas assumptions. The non-equilibrium condensation occurs during the expansion and mixing process and is tightly coupled with the high turbulence, oblique and expansion waves in supersonic flows. In this paper, we develop a wet steam model based on the computational fluid dynamics to understand the intricate feature of the steam condensation in the supersonic ejector. The numerical results show that the dry gas model exaggerates the expansion characteristics of the primary nozzle by 21.95%, which predicts the Mach number of 2.00 at the nozzle exit compared to 1.64 for the wet steam model. The dry gas model computes the static temperature lower to 196 K, whereas the wet steam model predicts the static temperature should above the triple point due to the phase change process. The liquid fraction can reach 7.2% of the total mass based on the prediction of the wet steam model. The performance analysis indicates that the dry gas model over-estimates a higher entrainment ratio by 11.71% than the wet steam model for the steam ejector

Semi-Supervised Learning for Personalized Web Recommender System

To learn a Web browsing behavior model, a large amount of labelled data must be available beforehand. However, very often the labelled data is limited and expensive to generate, since labelling typically requires human expertise. It could be even worse when we want to train personalized model. This paper proposes to train a personalized Web browsing behavior model by semi-supervised learning. The preliminary result based on the data from our user study shows that semi-supervised learning performs fairly well even though there are very few labelled data we can obtain from the specific user

Impact of Temperament Types and Anger Intensity on Drivers\u27 EEG Power Spectrum and Sample Entropy: An On-road Evaluation Toward Road Rage Warning

"Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less consideration of drivers\u27 individual differences. This study aims to explore the impact of temperament types and anger intensity on drivers\u27 EEG characteristics. Thirty-two drivers with valid license were enrolled to perform on-road experiments on a particularly busy route on which a variety of provoking events like cutting in line of surrounding vehicle, jaywalking, occupying road of non-motor vehicle and traffic congestion frequently happened. Then, muti-factor analysis of variance (ANOVA) and post hoc analysis were utilized to study the impact of temperament types and anger intensity on drivers\u27 power spectrum and sample entropy of θ and β waves extracted from EEG signals. The study results firstly indicated that right frontal region of the brain has close relationship with driving anger. Secondly, there existed significant main effects of temperament types on power spectrum and sample entropy of β wave while significant main effects of anger intensity on power spectrum and sample entropy of θ and β wave were all observed. Thirdly, significant interactions between temperament types and anger intensity for power spectrum and sample entropy of β wave were both noted. Fourthly, with the increase of anger intensity, the power spectrum and sample entropy both decreased sufficiently for θ wave while increased remarkably for β wave. The study results can provide a theoretical support for designing a personalized and hierarchical warning system for road rage

Representing Volumetric Videos as Dynamic MLP Maps

This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes. Recent advances in neural scene representations demonstrate their remarkable capability to model and render complex static scenes, but extending them to represent dynamic scenes is not straightforward due to their slow rendering speed or high storage cost. To solve this problem, our key idea is to represent the radiance field of each frame as a set of shallow MLP networks whose parameters are stored in 2D grids, called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all frames. Representing 3D scenes with shallow MLPs significantly improves the rendering speed, while dynamically predicting MLP parameters with a shared 2D CNN instead of explicitly storing them leads to low storage cost. Experiments show that the proposed approach achieves state-of-the-art rendering quality on the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering with a speed of 41.7 fps for $512 \times 512$ images on an RTX 3090 GPU. The code is available at https://zju3dv.github.io/mlp_maps/.Comment: Accepted to CVPR 2023. The first two authors contributed equally to this paper. Project page: https://zju3dv.github.io/mlp_maps