Cause-Consequence Relationship

At the heart of any stochastic model is a random number generator. This is an algorithm that generates a series of numbers such that each successive number has an equal probability of possessing any value, and each number is statistically independent of the other numbers in the series. In real life, randomness does not exist—every consequence has a cause. However, in many cases the cause—consequence relationship is not known about, so a random sequence of events is used to simulate the real world. 

Several recent reviews have concluded that, until now, no definitive cause-effect relationships have been established between exposure to environmental concentrations of chlorinated chemicals and serious adverse health consequences for humans.78-80. For fish-eating birds and mammals, however, substantial evidence... 

A key element in decision making will be to understand the relationship between the level of chemical exposure and the consequent risks to health or the environment. There are two main ways in which we can understand this relationship through the species sensitivity distribution (SSD) or the dose-response curve. The SSD is perhaps the more useful for environmental assessment because it integrates all species, whereas the dose response describes the cause-effect relationship for only one species. Nevertheless, the dose-response relationship could be a valuable tool for environmental assessment when the species described is either particularly sensitive,... 

To study this dynamic interplay between cognitive and physical space further, we are conducting new experiments which eschew the manipulative constraints on monkeys and very young children, whilst preserving the essential (search) action-to-visible consequent relationship which active, mature, human primates take for granted. Cyclic and productive, circular in the causative relationships between actions and representations—where action spawns representation and representation action— the hunt is surely on for the complex of feedback loops which active, embodied, cognitive systems create for themselves as a result of their own activity. 

To analyze and measure the reliability and maintainability characteristics of a system, there must be a mathematical model of the system that shows the functional relationships among all the components, the subsystems, and the overil system. The reliability of the system is a function of the reliabilities of its components. A system reliability model consists of some combination of a reliability block diagram or cause-consequence chart, a definition of all equipment failure and repair distributions, and a statement of spare and repair strategies (Kapur 1996a). All reliability analyses and optimizations are made on these conceptual mathematical models of the system. 

The design of fault tolerant systems consists in preventing a fault to cause an error and consequently a failure in the implemented system. Therefore, there is a cause-effect relationship from the particle hit (fault) to the erroneous result (system failure), as demonstrated in Fig. 2.2. In this work, we will use the definition presented inAvizienis (2004). 

These costs are easily calculable when the omission has led to unintended consequences well identified and with a clear cause-effect relationship and are usually documented in the records of the reparative activity executed. For example, repair costs of a product under warranty as a result of not having executed the final inspection of the product or administrative penalty for breach of statutory duty in environmental management due to not having identified this legal requirement. 

The use of wireless technology has become an integral part of educational institutions. The technology offers increased flexibility compared to stationary arrangements. However, there is concern among parents and in the scientific community that wireless access systems could have adverse side effects. Headaches, nosebleeds, nausea, nervous pains and some types of asthma, are reported as some of the effects, but substantial scientific uncertainties exist about the cause-effect relationships (Repacholi et al. 2005 and Wiart et al. 2008). This applies in particular to the long term consequences of the technology. 

Different approaches for diagnosis have been developed depending on the kind of knowledge used to describe the process model. Usually, a specific methodology is applied for a specific process. These diagnostic methods may be broadly classified as quantitative model-based methods, qualitative model and search-based methods, and process history-based methods. Model-based (qualitative and quantitative) approaches make use of causal analysis (cause and effect/antecedents and consequences relationships) as the basis of knowledge representation, i.e., they link individual component malfunctions to deviations in measured values. 

When looking at a supply chain, each process and project inside it seems to be demand-driven. This means that all supply chain members should share a focus on end customers in order to achieve the best customer service, and consequently customer loyally and profitability (see Proposition no. 4). For this reason, supply chain risk assessment should be linked to specific objectives of the supply ch such as service quality, timeliness, flexibility and efficiency (see Proposition no. 1). Risks can be considered as a threat or obstacle to achieving the supply chain goal. Risk evaluators should prioritize objectives, assessing the impact of potentially negative events and cause-effect relationships along the supply chain. 

Until recently most industrial scale, and even bench scale, bioreactors of this type were agitated by a set of Rushton turbines having about one-thind the diameter of the bioreactor (43) (Fig. 3). In this system, the air enters into the lower agitator and is dispersed from the back of the impeller blades by gas-fiUed or ventilated cavities (44). The presence of these cavities causes the power drawn by the agitator, ie, the power requited to drive it through the broth, to fall and this has important consequences for the performance of the bioreactor with respect to aeration (35). k a has been related to the power per unit volume, P/ U, in W/m and to the superficial air velocity, in m/s (20), where is the air flow rate per cross-sectional area of bioreactor. This relationship in water is... 

When the relationship between the distribution coefficient of a solute and solvent composition, or the corrected retention volume and solvent composition, was evaluated for aqueous solvent mixtures, it was found that the simple relationship identified by Purnell and Laub and Katz et al. no longer applied. The suspected cause for the failure was the strong association between the solvent and water. As a consequence, the mixture was not binary in nature but, in fact, a ternary system. An aqueous solution of methanol, for example, contained methanol, water and methanol associated with water. It follows that the prediction of the net distribution coefficient or net retention volume for a ternary system would require the use of three distribution coefficients one representing the distribution of the solute between the stationary phase and water, one representing that between the stationary phase and methanol and one between the stationary phase and the methanol/water associate. Unfortunately, as the relative amount of association varies with the initial... 

The consequence of the relationships of Table 5.3 and Fig. 5.2 is that for a neutral thermal sensation, at steady state, the core temperature increases while the skin temperature decreases with increased metabolic activity (Fig. 5.3). The increase in metabolism causes sweating which decreases skin tem-perature. 

The previous sections of this chapter have established that NEMCA, or Electrochemical Promotion, is caused by the electrochemically controlled backspillover of ionic species onto the catalyst surface and by the concomitant change on catalyst work function and adsorption binding energies. Although the latter may be considered as a consequence of the former, experiment has shown some surprisingly simple relationships between change AO in catalyst... 

Mechanistic Approaches. Adequate and appropriate river-quality assessment must provide predictive information on the possible consequences of water and land development. This requires an understanding of the relevant cause and effect relationships and suitable data to develop predictive models for basin management. This understanding may be achieved through qualitative, semi-quantitative or quantitative approaches. When quantitative or semi-quantitative methods are not available the qualitative approach must be applied. Qualitative assessments involve knowledge of how basin activities may affect river quality. This requires the use of various descriptive methods. An example of this kind of assessment is laboratory evaluation of the extent to which increases in plant nutrients, temperature or flow may lead to accelerated eutrophication with consequent reduction of water quality. 

For future research in this field, in addition to physiological and biochemical approaches, genetic analysis will be essential in the establishment of causal relationships between the induction of a stress protein and the establishment of tolerance to the stress condition. In most cases it is not difficult to detect the induction of new proteins during stress. However, the induction of new proteins does not necessarily establish stress tolerance it may well be the consequence of damage caused by stress conditions. Thus, genetic mutants will be necessary to test the physiological role of a stress protein. 

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