Local Reaction Probability Effects in Non-Classical Kinetics: Batch and Steady State

1992 ◽  
Vol 290 ◽  
Author(s):  
Zhong-You Shi ◽  
Raoul Kopelman

AbstractThe reaction A+A→0 is simulated in 1-D and 2-D square lattices with various local reaction probabilities, P. The effective reaction order, X, and the nearest neighbor distance distribution (NNDD), are evaluated in all these reactions. For batch reactions, sharp increases in X with increasing P occur at early times. Classical reaction limited kinetics is obtained at early times only when P→0. At long times, all reactions are in the non-classical, diffusion limited regime, regardless of P. For steady state reactions, our results demonstrate a similar behavior of X with P. The NNDD at steady state in 1-D media at P=1.0, i.e. diffusion limited reaction, follows the previously reported skewed exponential shape. This is no longer true for P<I. Finally, at P→0, as expected, an exponential (Poissonian) distribution is obtained for both reaction conditions.

2015 ◽  
Vol 143 (21) ◽  
pp. 215102 ◽  
Author(s):  
Paweł Nałęcz-Jawecki ◽  
Paulina Szymańska ◽  
Marek Kochańczyk ◽  
Jacek Miękisz ◽  
Tomasz Lipniacki

AIChE Journal ◽  
2006 ◽  
Vol 52 (5) ◽  
pp. 1673-1689 ◽  
Author(s):  
Marko Laakkonen ◽  
Pasi Moilanen ◽  
Ville Alopaeus ◽  
Juhani Aittamaa

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.


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