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The evolvement of large complicated system’s accidents can be treated as a chain of events (event chain), they drive the development of system’s potential risks, and worsen the safety condition of system, until the accidents happen. Some of the events are “critical events”, which have decisive influence to the accident occurrence, and they are the key points of system safety risk control. In this condition, the Event Sequence Diagram (ESD) is used for dynamic system’s accident evolvement modeling, for its unique modeling symbols can effectively describe the evolvement of accident in a certain system. Based on this, system critical event is defined to describe the key aspects of system safety. It can be understood as the last opportunity to avoid the accident. Searching algorithms of system critical events are designed, also a sensitive analysis method of relative variables is provided. A Monte Carlo simulation method is discussed for solving ESD model, which can provide quantitative description of accidents’ evolvement.

In the given research we offer the technique for the calculation of the density of events which people retrieve from autobiographical memory. We wanted to prove a non-uniformity nature of memories distribution in the course of time and were interested with the law of distribution of these events during life course.

Let $\mathcal{N}$ and $\mathcal{M}$ be two road networks represented in vector form and covering rectangular areas Rand R?, respectively, not necessarily parallel to each other, but with R? ? R. We assume that $\mathcal{N}$ and $\mathcal{M}$ use different coordinate systems at (possibly) different, but known scales. Let $\mathcal{B}$ and $\mathcal{A}$ denote sets of "prominent" road points (e.g., intersections) associated with $\mathcal{N}$ and $\mathcal{M}$, respectively. The positions of road points on both sets may contain a certain amount of "noise" due to errors and the finite precision of measurements. We propose an algorithm for determining approximate matches, in terms of the bottleneckdistance, between $\mathcal{A}$ and a subset $\mathcal{B}'$ of $\mathcal{B}$. We consider the characteristics of the problem in order to achieve a high degree of efficiency. At the same time, so as not to compromise the usability of the algorithm, we keep the complexity required for the data as low as possible. As the algorithm that guarantees to find a possible match is expensive due to the inherent complexity of the problem, we propose a lossless filteringalgorithm that yields a collection of candidate regions that contain a subset Sof $\mathcal{B}$ such that $\mathcal{A}$ maymatch a subset $\mathcal{B}'$ of S. Then we find possible approximate matchings between $\mathcal{A}$ and subsets of Susing the matching algorithm. We have implemented the proposed algorithm and report results that show the efficiency of our approach.

Long-distance transmission of signals is a critical event in the life of trees. Many physiological studies have deduced that hormone-like substances together with hydraulic/ electrical signals mediate this important function of the plant life. However the nature of the complex network of signalling in trees has remained essentially unexplored. Recent molecular and genetic studies offer new approaches to understanding the mechanisms underlying the transmission of signals.

The origins of modern medical technology may be traced to nineteenth-century europe, when the industrial revolution ushered in sweeping changes in every aspect of life. Of all the momentous discoveries and inventions of this period, there was one relatively obscure scientific event that laid the foundation for the subsequent development of Doppler technologies in the twentieth century — the discovery of a natural phenomenon that came to be known as Doppler effect. Another critical event was the discovery of the piezoelectric phenomenon by Pierre Curie and Jacques Curie, which enabled the development of ultrasonic transducers many decades later. This chapter briefly describes the origin of the Doppler theory during the nineteenth century and traces the development of diagnostic Doppler ultrasound technology during the second half of the twentieth century to the present.

Almost all natural sediment deposits support the growth of microbial assemblages. On occasions these assemblages become sufficiently developed to be recognisable as microbial mats or biofilms but even before the development of such extensive structure, microbial colonisation can influence the microstructure of the bed and the critical erosion threshold for the resuspension of particles. The entrainment of particles is a critical event in transport of sediments and as yet there is still insufficient knowledge to accurately predict the response of natural sediments to hydrodynamic stress. Early attempts to predict sediment behaviour were for the most part based on the relationship between hydrodynamic stress and particle size (see Miller et al. 1977). This type of relationships, such as the Shield’s curve, have proved to be reasonably reliable for abiotic well-sorted sandy sediments. However, the erosional behaviour of sediment mixtures, small cohesive sediments and, to a large extent, natural sediments in situ defies adequate description. Many workers now recognise that predictions based purely on physical parameters are insufficient to explain even the erosional behaviour of non-cohesive sediments in situ (Paterson and Daborn 1991; Daborn et al. 1993). It has long been suggested that the biological “status” of the sediment may influence transport processes (Carter 1933), but there are still relatively few studies that deal directly with this problem. There is increasing evidence in the literature that as Montague (1986) stated: “The physical and chemical properties of sediments that contain a significant biotic community are radically different from those of abiotic sediments ”.

During the last few years various process calculi have been extended to include real-time in order to handle quantitative aspects of real-time systems, for instance that some critical event must not or should happen within a certain time period. A number of extensions of classical process calculi with explicit timing constraints has emerged in the last few years. We mention the calculi defined in [Wan90] and in [Che91], all being extensions of CCS [Mi189], and the ones defined in [NSY91] and [BB89]. Common to these real-time calculi is that time is represented by some dense time domain, e.g. as in this paper the non-negative reals. Other timed calculi based on discrete time include [NRSV90, DS89, HR91].