The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model. C 2015 AIP Publishing LLC. [http://dx

A test platform with a millimeter-wave radar sensor, lane-line sensor, gyroscope, and controller area network was established to improve safety in using adaptive cruise control systems in vehicles. The motion-state characterization data of the host vehicle and surrounding vehicles in a real traffic environment were captured. The prediction method for the lane-changing maneuver of the vehicle ahead was developed using a hidden Markov model based on the distance between the host vehicle and the front vehicle, as well as the lateral and longitudinal velocities of the vehicle in front. The adaptive cruise control system control algorithm for assessing the target vehicle was optimized. The model was tested, and its predictions were compared with measured data. Result shows that the lane-changing and lane-keeping behaviors of the vehicle ahead can be predicted efficiently and accurately by the model. The maximum prediction accuracy rate for straight roads was 97% with the time window length of 4.5 s, whereas that for curved roads was 96% with the time window length of 3.5 s.

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

Evacuation modeling is a promising measure to support decision making in scenarios such as flooding, explosion, terrorist attack and other emergency incidents. Given the special attention to the terrorist attack, we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident. Sarin dispersion process is described by Gaussian puff model. Due to sarin’s special properties of being colorless and odorless, we focus more on the modeling of agents’ perceiving and reasoning process and use a Belief, Desire, Intention (BDI) architecture to solve the problem. Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration. A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation. Though far from perfect, the proposed model could serve to support decision making.

Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals' behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals' behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

The implementation of precursor management can improve safety performance of construction projects through effectively managing the correlations between construction accidents and their precursors. However, a system of comprehensive knowledge about what precursors mean within the context of construction safety is still lacking. This study aims to capture the nature of precursors in the construction industry and explore the process of a precursor event evolving into a construction accident to fill this gap. Based on 135 construction accident reports in China, this study adopts grounded theory to identify different types of accident precursors and explore their interactions with the development of the accident. An indicator system of precursors for construction accidents was developed, which included two major categories of precursors: behavioral factors and physical factors and five minor categories of precursors: individual behavior factors, organizational driving factors, objective physical factors, construction environmental factors, mechanical equipment factors. In addition, a precursor management strategy that includes the three stages of identification, response and effectiveness testing was established. The results of the study reveal the correlations between precursors and construction accidents, which can promote construction professionals’ better understanding about precursors and improve their capabilities of managing precursors in practice.

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