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Bacteriorhodopsin is a light-driven proton pump consisting of seven transmembrane -helices. Although the gross structure of this protein is known, the detailed orientation of the helices and the exact location of interhelical loops are unclear. One possible way to study a problem of this type would be to classify individual amino acid side-chains as being (1) buried in the interior of the protein, (2) exposed on the protein surface but in the interior of the membrane or (3) exposed on the protein surface in contact with the extracellular solution.

Step 4 Compute accurate values Several iterations at Steps 2 and 3 are needed to obtain accurate values for ErlangsPS and TSLC. The procedure should be stopped when the di erences between consecutive values are below a certain threshold (e.g., 5%). The typical number of iterations needed to obtain accurate results is 2 or 3. When deploying (E)GPRS in the BCCH TRX, TSLC does not depend on load, hence only the initial calculation is needed.

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As you might imagine from the name, comparison operators let you compare one operand with the other in various ways. If the comparison test is successful, the expression evaluates to true; otherwise, it evaluates to false. You often use comparison operators with decision and looping statements such as if and while (these are covered in 4).

In many cases, the complexity of calculation and data analysis is so great that human reasoning itself is incapable of handling it properly The reason computation is so complex is often the number of di erent types of evidence needed for decision-making Problems require solutions that are based on multivariate analysis For this purpose there are several di erent types of analysis algorithms available in the literature for example, clustering algorithms, neural networks and statistical processing The precision of calculation made by an automated algorithm is greater than a humanmade calculation Automated algorithms can maintain the accuracy even when several decisions need to be taken during a day When a model of expert reasoning and an automated decision algorithm are available, that information can be reused in other parts of the network for the same purpose.

For example, if an expert creates a model for automatic analysis of QoS deterioration in the base station, the same model likely can be reused for other base stations in the network However, ne-tuning of the model might be needed to adapt the solution to di erent types of base stations or other network elements There are several ways to create models for automated decision-making One of them is the learning machine In an optimised area, the autonomic system learns from decisions made by several experts After the learning period, the algorithm is able to take similar decisions autonomously An example is utilisation of an arti cial neural network for QoS monitoring The neural network is capable of separating di erently behaving elements from each other Additionally, if the neural network is taught what kind of behaviour is acceptable and what is not, it can automatically detect poorly performing elements.

Well-con gured automated and intelligent solutions are not as error-prone as humans simply because humans are not capable of handling the data as accurately as algorithms Algorithms can also handle huge masses of data, without increasing the number of errors A good example is detection of anomalies from large datasets, a task that a proper algorithm can do uently at a low error rate Moreover, the processing speed (computational power) of computers is far higher than any human-based e ort One important aspect in automation is the speed of reaction Networks generate performance data 24 hours per day Computer-aided solutions can react to the required situations fast and no human presence is required In reality, most of the processes are still fully controlled by engineers, especially to verify telecom network parameter changes Fully automated optimisation loops controlled only by algorithms are still rare.

However, certain tasks, such as data transfer and provisioning of new parameter settings, can be autonomously run at night when there is no tra c in the network (impact on business is low)..

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