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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.

Figure 10.2. Hemolysis-based holin screening. When recombinant protein expression is induced in E. coli, holin-encoding clones often undergo lysis. This is detectable by a zone of hemolysis in blood agar, presumably a nonspeci c effect due to the release of intracellular bacterial contents. Depicted here are pre- and post-induction examples of two clones encoding lysin/holin genes, along with positive (B) and negative (A, E G) controls: (A) empty plasmid; (B) aerolysin, a secreted bacterial hemolysin; (C D) holin + lysin; (E) random phage DNA; (F G) lysin alone. It should be noted that the technique is not perfectly sensitive or speci c for lysin/holin-encoding cassettes, as other toxic proteins encoded by phage could induce a similar effect. Nevertheless, the method provides a potential alternative for cloning lytic enzymes that avoids complications from holin-based toxicity.

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)..

Photoshop automatically uses the first characters you type as the layer name. You can change the layer name by Alt-double-clicking on the layer name in the Layers palette to bring up the Layer Properties dialog box.

When you add a shape, you add both a graphical element (a circle, a bar, or other) and an associated text placeholder. The same applies to deletion; removing a shape also removes its associated text placeholder from the diagram. n

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Activating early warning triggers, and activating the CIRT Analyzing message header, source, and target ISP Analyzing the attacking network(s) IP address